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a/doc/LectureNotes/_build/html/_sources/week48.ipynb b/doc/LectureNotes/_build/html/_sources/week48.ipynb index c0298d422..34d69ba5d 100644 --- a/doc/LectureNotes/_build/html/_sources/week48.ipynb +++ b/doc/LectureNotes/_build/html/_sources/week48.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d7c532d5", + "id": "da40c119", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "d27ab2b4", + "id": "82348ef6", "metadata": { "editable": true }, @@ -22,14 +22,14 @@ "# Week 48: Gradient boosting and summary of course\n", "**Morten Hjorth-Jensen**, Department of Physics and Center for Computing in Science Education, University of Oslo, Norway\n", "\n", - "Date: **Nov 24, 2024**\n", + "Date: **Nov 25, 2024**\n", "\n", "Copyright 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license" ] }, { "cell_type": "markdown", - "id": "9f55b05a", + "id": "61b3407d", "metadata": { "editable": true }, @@ -39,7 +39,7 @@ }, { "cell_type": "markdown", - "id": "e72578f8", + "id": "c40156de", "metadata": { "editable": true }, @@ -56,23 +56,25 @@ "a. These lecture notes at \n", "\n", "b. See also lecture notes from week 47 at . The lecture on Monday starts with a repetition on AdaBoost before we move over to gradient boosting with examples\n", - "\n", - "\n", "\n", - "c. Video on Decision trees \n", + "c. Video of lecture at \n", "\n", - "d. Video on boosting methods \n", + "d. Whiteboard notes at \n", "\n", - "e. Video on AdaBoost \n", + "e. Video on Decision trees \n", "\n", - "f. Video on Gradient boost, part 1, parts 2-4 follow thereafter \n", + "f. Video on boosting methods \n", "\n", - "g. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at ." + "g. Video on AdaBoost \n", + "\n", + "h. Video on Gradient boost, part 1, parts 2-4 follow thereafter \n", + "\n", + "i. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at ." ] }, { "cell_type": "markdown", - "id": "51ee8fb3", + "id": "53d9e808", "metadata": { "editable": true }, @@ -91,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "5b4ed2be", + "id": "9edfc128", "metadata": { "editable": true }, @@ -118,7 +120,7 @@ }, { "cell_type": "markdown", - "id": "3e76e282", + "id": "9278d8ee", "metadata": { "editable": true }, @@ -129,7 +131,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "acba62b3", + "id": "116422d2", "metadata": { "collapsed": false, "editable": true @@ -203,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "c5cbe0e8", + "id": "2bf79506", "metadata": { "editable": true }, @@ -219,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "19bd77f2", + "id": "a80dc181", "metadata": { "editable": true }, @@ -230,7 +232,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "84f1af2e", + "id": "61ef464c", "metadata": { "collapsed": false, "editable": true @@ -245,7 +247,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "6c01d900", + "id": "ab3892d7", "metadata": { "collapsed": false, "editable": true @@ -263,7 +265,7 @@ }, { "cell_type": "markdown", - "id": "896072f6", + "id": "2d4ac770", "metadata": { "editable": true }, @@ -283,7 +285,7 @@ }, { "cell_type": "markdown", - "id": "babc9c9c", + "id": "20f3e403", "metadata": { "editable": true }, @@ -297,7 +299,7 @@ }, { "cell_type": "markdown", - "id": "2ed28584", + "id": "ea52430d", "metadata": { "editable": true }, @@ -309,7 +311,7 @@ }, { "cell_type": "markdown", - "id": "5b5b9631", + "id": "86cb9f50", "metadata": { "editable": true }, @@ -326,7 +328,7 @@ }, { "cell_type": "markdown", - "id": "7b6c43ec", + "id": "00ca4f95", "metadata": { "editable": true }, @@ -338,7 +340,7 @@ }, { "cell_type": "markdown", - "id": "3962039f", + "id": "ddc56700", "metadata": { "editable": true }, @@ -352,7 +354,7 @@ }, { "cell_type": "markdown", - "id": "ee5f259c", + "id": "eb17dae2", "metadata": { "editable": true }, @@ -364,7 +366,7 @@ }, { "cell_type": "markdown", - "id": "079d8ee3", + "id": "9ae66e77", "metadata": { "editable": true }, @@ -377,7 +379,7 @@ }, { "cell_type": "markdown", - "id": "11f821a2", + "id": "ae777b9c", "metadata": { "editable": true }, @@ -389,7 +391,7 @@ }, { "cell_type": "markdown", - "id": "60990073", + "id": "8b3174c5", "metadata": { "editable": true }, @@ -399,7 +401,7 @@ }, { "cell_type": "markdown", - "id": "4d7195ab", + "id": "b4590742", "metadata": { "editable": true }, @@ -427,7 +429,7 @@ }, { "cell_type": "markdown", - "id": "4bee726a", + "id": "d6f866fd", "metadata": { "editable": true }, @@ -443,7 +445,7 @@ }, { "cell_type": "markdown", - "id": "1190b9a0", + "id": "b190e045", "metadata": { "editable": true }, @@ -455,7 +457,7 @@ }, { "cell_type": "markdown", - "id": "2702211b", + "id": "df876b5f", "metadata": { "editable": true }, @@ -466,7 +468,7 @@ }, { "cell_type": "markdown", - "id": "1b1d9984", + "id": "33756540", "metadata": { "editable": true }, @@ -478,7 +480,7 @@ }, { "cell_type": "markdown", - "id": "cdf7e56a", + "id": "9b588d4a", "metadata": { "editable": true }, @@ -488,7 +490,7 @@ }, { "cell_type": "markdown", - "id": "ac6f3b86", + "id": "5f289c53", "metadata": { "editable": true }, @@ -500,7 +502,7 @@ }, { "cell_type": "markdown", - "id": "faf6fb5f", + "id": "23f5d161", "metadata": { "editable": true }, @@ -510,7 +512,7 @@ }, { "cell_type": "markdown", - "id": "969f86ed", + "id": "fd058392", "metadata": { "editable": true }, @@ -522,7 +524,7 @@ }, { "cell_type": "markdown", - "id": "a98a541c", + "id": "5a8517bd", "metadata": { "editable": true }, @@ -532,7 +534,7 @@ }, { "cell_type": "markdown", - "id": "d8be5438", + "id": "94eec67e", "metadata": { "editable": true }, @@ -544,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "932f43c5", + "id": "d4936ab1", "metadata": { "editable": true }, @@ -558,7 +560,7 @@ }, { "cell_type": "markdown", - "id": "24630eb9", + "id": "7bafaad4", "metadata": { "editable": true }, @@ -574,7 +576,7 @@ }, { "cell_type": "markdown", - "id": "9a7fa4b6", + "id": "b91cc27d", "metadata": { "editable": true }, @@ -586,7 +588,7 @@ }, { "cell_type": "markdown", - "id": "7ead62b2", + "id": "ee926ea4", "metadata": { "editable": true }, @@ -602,7 +604,7 @@ }, { "cell_type": "markdown", - "id": "91639c49", + "id": "88be0c12", "metadata": { "editable": true }, @@ -614,7 +616,7 @@ }, { "cell_type": "markdown", - "id": "e43865c2", + "id": "a16704d1", "metadata": { "editable": true }, @@ -624,7 +626,7 @@ }, { "cell_type": "markdown", - "id": "1884c219", + "id": "1667ae43", "metadata": { "editable": true }, @@ -636,7 +638,7 @@ }, { "cell_type": "markdown", - "id": "bbb1cdbb", + "id": "21368026", "metadata": { "editable": true }, @@ -648,7 +650,7 @@ }, { "cell_type": "markdown", - "id": "7e3590f6", + "id": "3f57f184", "metadata": { "editable": true }, @@ -660,7 +662,7 @@ }, { "cell_type": "markdown", - "id": "2eb8c86b", + "id": "adc38409", "metadata": { "editable": true }, @@ -671,7 +673,7 @@ }, { "cell_type": "markdown", - "id": "e477d15a", + "id": "d4de19b9", "metadata": { "editable": true }, @@ -683,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "b6f01e59", + "id": "9b9b6dcd", "metadata": { "editable": true }, @@ -694,7 +696,7 @@ }, { "cell_type": "markdown", - "id": "71c85471", + "id": "a274021b", "metadata": { "editable": true }, @@ -706,7 +708,7 @@ }, { "cell_type": "markdown", - "id": "2d5be340", + "id": "fe46199f", "metadata": { "editable": true }, @@ -716,7 +718,7 @@ }, { "cell_type": "markdown", - "id": "e426517c", + "id": "913a48d6", "metadata": { "editable": true }, @@ -728,7 +730,7 @@ }, { "cell_type": "markdown", - "id": "c0c7e993", + "id": "e707fd99", "metadata": { "editable": true }, @@ -740,7 +742,7 @@ }, { "cell_type": "markdown", - "id": "7457a096", + "id": "1b749c34", "metadata": { "editable": true }, @@ -752,7 +754,7 @@ }, { "cell_type": "markdown", - "id": "424d5bc9", + "id": "b79b881c", "metadata": { "editable": true }, @@ -764,7 +766,7 @@ }, { "cell_type": "markdown", - "id": "d1fa658b", + "id": "115a36fd", "metadata": { "editable": true }, @@ -774,7 +776,7 @@ }, { "cell_type": "markdown", - "id": "f8fc6b15", + "id": "49086250", "metadata": { "editable": true }, @@ -786,7 +788,7 @@ }, { "cell_type": "markdown", - "id": "9ca82f54", + "id": "a840811a", "metadata": { "editable": true }, @@ -796,7 +798,7 @@ }, { "cell_type": "markdown", - "id": "6c028686", + "id": "d213a918", "metadata": { "editable": true }, @@ -808,7 +810,7 @@ }, { "cell_type": "markdown", - "id": "4007a0ef", + "id": "68a50e61", "metadata": { "editable": true }, @@ -818,7 +820,7 @@ }, { "cell_type": "markdown", - "id": "ba456347", + "id": "b62cf357", "metadata": { "editable": true }, @@ -830,7 +832,7 @@ }, { "cell_type": "markdown", - "id": "c8df4ef3", + "id": "5da2ca5c", "metadata": { "editable": true }, @@ -840,7 +842,7 @@ }, { "cell_type": "markdown", - "id": "da46f9d9", + "id": "803e473e", "metadata": { "editable": true }, @@ -852,7 +854,7 @@ }, { "cell_type": "markdown", - "id": "5c60f30d", + "id": "35e1f9c1", "metadata": { "editable": true }, @@ -862,7 +864,7 @@ }, { "cell_type": "markdown", - "id": "a5bc4dfe", + "id": "3b7c369c", "metadata": { "editable": true }, @@ -874,7 +876,7 @@ }, { "cell_type": "markdown", - "id": "3987cc1e", + "id": "dae26491", "metadata": { "editable": true }, @@ -894,7 +896,7 @@ }, { "cell_type": "markdown", - "id": "b35df09e", + "id": "57668483", "metadata": { "editable": true }, @@ -906,7 +908,7 @@ }, { "cell_type": "markdown", - "id": "5b6a209c", + "id": "5830f7a2", "metadata": { "editable": true }, @@ -916,7 +918,7 @@ }, { "cell_type": "markdown", - "id": "d619a097", + "id": "b933bd20", "metadata": { "editable": true }, @@ -932,7 +934,7 @@ }, { "cell_type": "markdown", - "id": "b7311abb", + "id": "6e8dff87", "metadata": { "editable": true }, @@ -944,7 +946,7 @@ }, { "cell_type": "markdown", - "id": "4bfb4209", + "id": "1d6a0fa2", "metadata": { "editable": true }, @@ -972,7 +974,7 @@ }, { "cell_type": "markdown", - "id": "4cf4c1f4", + "id": "eacc9804", "metadata": { "editable": true }, @@ -985,7 +987,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "453dff54", + "id": "29c9b0a6", "metadata": { "collapsed": false, "editable": true @@ -1010,7 +1012,7 @@ }, { "cell_type": "markdown", - "id": "d6470164", + "id": "f5eaa0fc", "metadata": { "editable": true }, @@ -1021,7 +1023,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "b6e01886", + "id": "1db44432", "metadata": { "collapsed": false, "editable": true @@ -1110,7 +1112,7 @@ }, { "cell_type": "markdown", - "id": "3cede4c9", + "id": "60fbb085", "metadata": { "editable": true }, @@ -1128,7 +1130,7 @@ }, { "cell_type": "markdown", - "id": "94e3b59f", + "id": "cbc6c37e", "metadata": { "editable": true }, @@ -1141,7 +1143,7 @@ }, { "cell_type": "markdown", - "id": "2f000f62", + "id": "675390d3", "metadata": { "editable": true }, @@ -1153,7 +1155,7 @@ }, { "cell_type": "markdown", - "id": "014da5e9", + "id": "ac24e4fb", "metadata": { "editable": true }, @@ -1163,7 +1165,7 @@ }, { "cell_type": "markdown", - "id": "022aee06", + "id": "cfdd6b7f", "metadata": { "editable": true }, @@ -1175,7 +1177,7 @@ }, { "cell_type": "markdown", - "id": "d0d3e174", + "id": "de1524db", "metadata": { "editable": true }, @@ -1185,7 +1187,7 @@ }, { "cell_type": "markdown", - "id": "8af32042", + "id": "506fa7d0", "metadata": { "editable": true }, @@ -1197,7 +1199,7 @@ }, { "cell_type": "markdown", - "id": "4a384d95", + "id": "cc03bbc9", "metadata": { "editable": true }, @@ -1210,7 +1212,7 @@ }, { "cell_type": "markdown", - "id": "bd5b9bb9", + "id": "a1145d71", "metadata": { "editable": true }, @@ -1222,7 +1224,7 @@ }, { "cell_type": "markdown", - "id": "560b3106", + "id": "f9bbe14d", "metadata": { "editable": true }, @@ -1234,7 +1236,7 @@ }, { "cell_type": "markdown", - "id": "4742d327", + "id": "44ca0a6c", "metadata": { "editable": true }, @@ -1246,7 +1248,7 @@ }, { "cell_type": "markdown", - "id": "d160a37f", + "id": "7791881d", "metadata": { "editable": true }, @@ -1256,7 +1258,7 @@ }, { "cell_type": "markdown", - "id": "f3cde07e", + "id": "b3cffde0", "metadata": { "editable": true }, @@ -1268,7 +1270,7 @@ }, { "cell_type": "markdown", - "id": "cf95ad5e", + "id": "842710e4", "metadata": { "editable": true }, @@ -1278,7 +1280,7 @@ }, { "cell_type": "markdown", - "id": "bb614b9d", + "id": "2d0ac22f", "metadata": { "editable": true }, @@ -1294,7 +1296,7 @@ }, { "cell_type": "markdown", - "id": "19e3e8fa", + "id": "8b7ba0b6", "metadata": { "editable": true }, @@ -1306,7 +1308,7 @@ }, { "cell_type": "markdown", - "id": "9330b34b", + "id": "d0ad4149", "metadata": { "editable": true }, @@ -1327,7 +1329,7 @@ }, { "cell_type": "markdown", - "id": "e648539a", + "id": "6efc5e2e", "metadata": { "editable": true }, @@ -1338,7 +1340,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "d12810d3", + "id": "4a6a7ba3", "metadata": { "collapsed": false, "editable": true @@ -1389,7 +1391,7 @@ }, { "cell_type": "markdown", - "id": "f3e95eca", + "id": "23c85cdf", "metadata": { "editable": true }, @@ -1400,7 +1402,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "d6426469", + "id": "777b44f6", "metadata": { "collapsed": false, "editable": true @@ -1448,7 +1450,7 @@ }, { "cell_type": "markdown", - "id": "8de56415", + "id": "79c731f6", "metadata": { "editable": true }, @@ -1471,7 +1473,7 @@ }, { "cell_type": "markdown", - "id": "dfc59b18", + "id": "abf51c81", "metadata": { "editable": true }, @@ -1484,7 +1486,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "79e4cc00", + "id": "3243120a", "metadata": { "collapsed": false, "editable": true @@ -1542,7 +1544,7 @@ }, { "cell_type": "markdown", - "id": "c4700b19", + "id": "3415611e", "metadata": { "editable": true }, @@ -1553,7 +1555,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "eb0594c5", + "id": "0a1059eb", "metadata": { "collapsed": false, "editable": true @@ -1640,7 +1642,7 @@ }, { "cell_type": "markdown", - "id": "bbf154c4", + "id": "e88f148c", "metadata": { "editable": true }, @@ -1650,7 +1652,7 @@ }, { "cell_type": "markdown", - "id": "84d512fc", + "id": "05417d95", "metadata": { "editable": true }, @@ -1665,7 +1667,7 @@ }, { "cell_type": "markdown", - "id": "71b8eb59", + "id": "07dabfb9", "metadata": { "editable": true }, @@ -1681,7 +1683,7 @@ }, { "cell_type": "markdown", - "id": "473329b7", + "id": "f4be6096", "metadata": { "editable": true }, @@ -1706,7 +1708,7 @@ }, { "cell_type": "markdown", - "id": "6c94f44a", + "id": "13fbfb78", "metadata": { "editable": true }, @@ -1752,7 +1754,7 @@ }, { "cell_type": "markdown", - "id": "d9942ded", + "id": "6e70a00a", "metadata": { "editable": true }, @@ -1787,7 +1789,7 @@ }, { "cell_type": "markdown", - "id": "7818c700", + "id": "498439cc", "metadata": { "editable": true }, @@ -1810,7 +1812,7 @@ }, { "cell_type": "markdown", - "id": "cc946602", + "id": "5aefcdd5", "metadata": { "editable": true }, @@ -1833,7 +1835,7 @@ }, { "cell_type": "markdown", - "id": "c2b75185", + "id": "e5724f99", "metadata": { "editable": true }, @@ -1853,7 +1855,7 @@ }, { "cell_type": "markdown", - "id": "4600986a", + "id": "4b014afb", "metadata": { "editable": true }, @@ -1869,7 +1871,7 @@ }, { "cell_type": "markdown", - "id": "cfb572d5", + "id": "86d5bf4b", "metadata": { "editable": true }, @@ -1897,7 +1899,7 @@ }, { "cell_type": "markdown", - "id": "3e38f862", + "id": "6fd99cf9", "metadata": { "editable": true }, @@ -1915,7 +1917,7 @@ }, { "cell_type": "markdown", - "id": "76abe122", + "id": "40a95915", "metadata": { "editable": true }, @@ -1940,7 +1942,7 @@ }, { "cell_type": "markdown", - "id": "0b246db8", + "id": "de9b4ef9", "metadata": { "editable": true }, @@ -1958,7 +1960,7 @@ }, { "cell_type": "markdown", - "id": "9a6555fc", + "id": "09e09e8e", "metadata": { "editable": true }, @@ -1988,7 +1990,7 @@ }, { "cell_type": "markdown", - "id": "c2561e72", + "id": "4048b90f", "metadata": { "editable": true }, @@ -2002,7 +2004,7 @@ }, { "cell_type": "markdown", - "id": "796637c8", + "id": "d62bdef5", "metadata": { "editable": true }, @@ -2028,7 +2030,7 @@ }, { "cell_type": "markdown", - "id": "6afdb59e", + "id": "7110e8f8", "metadata": { "editable": true }, @@ -2057,7 +2059,7 @@ }, { "cell_type": "markdown", - "id": "95097424", + "id": "49da7ac1", "metadata": { "editable": true }, @@ -2074,7 +2076,7 @@ }, { "cell_type": "markdown", - "id": "31a33cea", + "id": "b7ddf675", "metadata": { "editable": true }, @@ -2096,7 +2098,7 @@ }, { "cell_type": "markdown", - "id": "d6583875", + "id": "4f93bfd8", "metadata": { "editable": true }, @@ -2114,7 +2116,7 @@ }, { "cell_type": "markdown", - "id": "285bbf5d", + "id": "553b0708", "metadata": { "editable": true }, @@ -2137,7 +2139,7 @@ }, { "cell_type": "markdown", - "id": "5b51dec9", + "id": "c82a9f73", "metadata": { "editable": true }, @@ -2156,7 +2158,7 @@ }, { "cell_type": "markdown", - "id": "4cb37b8f", + "id": "5791b95e", "metadata": { "editable": true }, @@ -2172,7 +2174,7 @@ }, { "cell_type": "markdown", - "id": "45fe33d7", + "id": "2681e655", "metadata": { "editable": true }, @@ -2187,7 +2189,7 @@ }, { "cell_type": "markdown", - "id": "3bbbaa88", + "id": "1764e858", "metadata": { "editable": true }, @@ -2211,7 +2213,7 @@ }, { "cell_type": "markdown", - "id": "d6a9f606", + "id": "435da59f", "metadata": { "editable": true }, @@ -2223,7 +2225,7 @@ }, { "cell_type": "markdown", - "id": "81aaff6e", + "id": "c7959e4f", "metadata": { "editable": true }, @@ -2241,7 +2243,7 @@ }, { "cell_type": "markdown", - "id": "9f856eb3", + "id": "fa48be28", "metadata": { "editable": true }, @@ -2251,7 +2253,7 @@ }, { "cell_type": "markdown", - "id": "f15429d1", + "id": "02027f10", "metadata": { "editable": true }, @@ -2269,7 +2271,7 @@ }, { "cell_type": "markdown", - "id": "e8a8f8e7", + "id": "7247c2cd", "metadata": { "editable": true }, @@ -2279,7 +2281,7 @@ }, { "cell_type": "markdown", - "id": "4fb17989", + "id": "0f51c953", "metadata": { "editable": true }, @@ -2298,7 +2300,7 @@ }, { "cell_type": "markdown", - "id": "0bed5ddd", + "id": "3ed82ee8", "metadata": { "editable": true }, @@ -2310,7 +2312,7 @@ }, { "cell_type": "markdown", - "id": "f8e64229", + "id": "f95ef8c8", "metadata": { "editable": true }, @@ -2324,7 +2326,7 @@ }, { "cell_type": "markdown", - "id": "f8f02179", + "id": "36be46d9", "metadata": { "editable": true }, @@ -2339,7 +2341,7 @@ }, { "cell_type": "markdown", - "id": "19008689", + "id": "374b4069", "metadata": { "editable": true }, @@ -2357,7 +2359,7 @@ }, { "cell_type": "markdown", - "id": "a3d31b3a", + "id": "5676ade3", "metadata": { "editable": true }, @@ -2371,7 +2373,7 @@ }, { "cell_type": "markdown", - "id": "79d29641", + "id": "5dc3dfdc", "metadata": { "editable": true }, @@ -2389,7 +2391,7 @@ }, { "cell_type": "markdown", - "id": "4d19d779", + "id": "7c26c421", "metadata": { "editable": true }, @@ -2413,7 +2415,7 @@ }, { "cell_type": "markdown", - "id": "b8fa06ec", + "id": "73947428", "metadata": { "editable": true }, @@ -2451,7 +2453,7 @@ }, { "cell_type": "markdown", - "id": "262fc510", + "id": "69763d57", "metadata": { "editable": true }, @@ -2474,7 +2476,7 @@ }, { "cell_type": "markdown", - "id": "36608f14", + "id": "3ee70227", "metadata": { "editable": true }, @@ -2510,7 +2512,7 @@ }, { "cell_type": "markdown", - "id": "a2b4652f", + "id": "6c8fe760", "metadata": { "editable": true }, @@ -2531,7 +2533,7 @@ }, { "cell_type": "markdown", - "id": "8cef8c03", + "id": "82d92000", "metadata": { "editable": true }, @@ -2553,7 +2555,7 @@ }, { "cell_type": "markdown", - "id": "df85a3e6", + "id": "81af1340", "metadata": { "editable": true }, @@ -2573,7 +2575,7 @@ }, { "cell_type": "markdown", - "id": "f01aaa3c", + "id": "9ccf4491", "metadata": { "editable": true }, @@ -2588,7 +2590,7 @@ }, { "cell_type": "markdown", - "id": "0005330c", + "id": "d561f7fa", "metadata": { "editable": true }, @@ -2606,7 +2608,7 @@ }, { "cell_type": "markdown", - "id": "efcfaccf", + "id": "2f59b95d", "metadata": { "editable": true }, @@ -2636,7 +2638,7 @@ }, { "cell_type": "markdown", - "id": "f7b109c9", + "id": "89648aa7", "metadata": { "editable": true }, @@ -2665,7 +2667,7 @@ }, { "cell_type": "markdown", - "id": "e9142380", + "id": "1bd0711b", "metadata": { "editable": true }, @@ -2696,7 +2698,7 @@ }, { "cell_type": "markdown", - "id": "cb638c92", + "id": "2e71e339", "metadata": { "editable": true }, @@ -2719,7 +2721,7 @@ }, { "cell_type": "markdown", - "id": "676fd56e", + "id": "3f1e33e9", "metadata": { "editable": true }, @@ -2737,7 +2739,7 @@ }, { "cell_type": "markdown", - "id": "ac1d93d3", + "id": "0b2bc8fa", "metadata": { "editable": true }, @@ -2760,7 +2762,7 @@ }, { "cell_type": "markdown", - "id": "c5047fcb", + "id": "3fad774c", "metadata": { "editable": true }, @@ -2781,7 +2783,7 @@ }, { "cell_type": "markdown", - "id": "bdd66000", + "id": "f418cc25", "metadata": { "editable": true }, @@ -2797,7 +2799,7 @@ }, { "cell_type": "markdown", - "id": "7fb56aeb", + "id": "5b323429", "metadata": { "editable": true }, diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js index 123007877..ba00b956f 100644 --- a/doc/LectureNotes/_build/html/searchindex.js +++ b/doc/LectureNotes/_build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"3D volumes of neurons": [[46, "d-volumes-of-neurons"], [47, "d-volumes-of-neurons"]], "A Classification Tree": [[10, "a-classification-tree"], [48, "a-classification-tree"], [49, "a-classification-tree"]], "A Frequentist approach to data analysis": [[1, "a-frequentist-approach-to-data-analysis"], [36, "a-frequentist-approach-to-data-analysis"]], "A better approach": [[9, "a-better-approach"]], "A deep CNN model (From Raschka et al)": [[46, "a-deep-cnn-model-from-raschka-et-al"], [47, "a-deep-cnn-model-from-raschka-et-al"]], "A first summary": [[36, "a-first-summary"]], "A more advanced example": [[22, "a-more-advanced-example"], [42, "a-more-advanced-example"]], "A more compact expression": [[0, "a-more-compact-expression"], [40, "a-more-compact-expression"]], "A more efficient way of coding the above Convolution": [[46, "a-more-efficient-way-of-coding-the-above-convolution"]], "A new Cost Function": [[38, "a-new-cost-function"], [39, "a-new-cost-function"]], "A possible code using Scikit-Learn": [[48, "a-possible-code-using-scikit-learn"], [49, "a-possible-code-using-scikit-learn"]], "A possible implementation of a neural network": [[45, "a-possible-implementation-of-a-neural-network"]], "A quick Reminder on Lagrangian Multipliers": [[9, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[5, "a-simple-example"], [47, "a-simple-example"]], "A soft classifier": [[9, "a-soft-classifier"]], "A standard BM setup": [[50, "a-standard-bm-setup"]], "A top-down approach, recursive binary splitting": [[48, "a-top-down-approach-recursive-binary-splitting"], [49, "a-top-down-approach-recursive-binary-splitting"]], "A top-down perspective on Neural networks": [[2, "a-top-down-perspective-on-neural-networks"], [43, "a-top-down-perspective-on-neural-networks"], [44, "a-top-down-perspective-on-neural-networks"]], "A typical Decision Tree with its pertinent Jargon, Classification Problem": [[48, "a-typical-decision-tree-with-its-pertinent-jargon-classification-problem"]], "A warm-up example": [[22, "a-warm-up-example"], [42, "a-warm-up-example"]], "A way to Read the Bias-Variance Tradeoff": [[39, "a-way-to-read-the-bias-variance-tradeoff"]], "ADAM algorithm, taken from Goodfellow et al": [[22, "adam-algorithm-taken-from-goodfellow-et-al"], [42, "adam-algorithm-taken-from-goodfellow-et-al"]], "ADAM optimizer": [[14, "adam-optimizer"], [41, "adam-optimizer"], [42, "adam-optimizer"]], "Activation functions": [[13, "activation-functions"], [42, "activation-functions"], [43, "activation-functions"], [44, "activation-functions"], [44, "id3"], [45, "activation-functions"], [45, "id3"], [46, "activation-functions"], [47, "activation-functions"]], "Activation functions, Logistic and Hyperbolic ones": [[42, "activation-functions-logistic-and-hyperbolic-ones"], [43, "activation-functions-logistic-and-hyperbolic-ones"], [44, "activation-functions-logistic-and-hyperbolic-ones"]], "Activation functions, examples": [[45, "activation-functions-examples"]], "AdaBoost Examples": [[48, "adaboost-examples"], [49, "adaboost-examples"], [50, "adaboost-examples"]], "AdaGrad algorithm, taken from Goodfellow et al": [[22, "adagrad-algorithm-taken-from-goodfellow-et-al"], [42, "adagrad-algorithm-taken-from-goodfellow-et-al"]], "Adaptive Boosting, AdaBoost": [[48, "adaptive-boosting-adaboost"], [49, "adaptive-boosting-adaboost"], [50, "adaptive-boosting-adaboost"]], "Adaptive boosting: AdaBoost, Basic Algorithm": [[11, "adaptive-boosting-adaboost-basic-algorithm"], [48, "adaptive-boosting-adaboost-basic-algorithm"], [49, "adaptive-boosting-adaboost-basic-algorithm"], [50, "adaptive-boosting-adaboost-basic-algorithm"]], "Add Dense layers on top": [[46, "add-dense-layers-on-top"], [47, "add-dense-layers-on-top"]], "Adding Neural Networks": [[42, "adding-neural-networks"], [43, "adding-neural-networks"]], "Adding a hidden layer": [[43, "adding-a-hidden-layer"], [44, "adding-a-hidden-layer"]], "Adding error analysis and training set up": [[36, "adding-error-analysis-and-training-set-up"], [37, "adding-error-analysis-and-training-set-up"]], "Additional Remarks": [[46, "additional-remarks"], [47, "additional-remarks"]], "Additional courses of interest": [[50, "additional-courses-of-interest"]], "Adjust hyperparameters": [[2, "adjust-hyperparameters"], [44, "adjust-hyperparameters"], [45, "adjust-hyperparameters"]], "Adversarial learning": [[50, "adversarial-learning"]], "Algorithms and codes for Adagrad, RMSprop and Adam": [[22, "algorithms-and-codes-for-adagrad-rmsprop-and-adam"], [41, "algorithms-and-codes-for-adagrad-rmsprop-and-adam"], [42, "algorithms-and-codes-for-adagrad-rmsprop-and-adam"]], "Algorithms for Setting up Decision Trees": [[10, "algorithms-for-setting-up-decision-trees"], [48, "algorithms-for-setting-up-decision-trees"], [49, "algorithms-for-setting-up-decision-trees"]], "Alternative differential equations": [[31, "alternative-differential-equations"]], "An Overview of Ensemble Methods": [[11, "an-overview-of-ensemble-methods"], [48, "an-overview-of-ensemble-methods"], [49, "an-overview-of-ensemble-methods"]], "An extrapolation example": [[5, "an-extrapolation-example"]], "An optimization/minimization problem": [[36, "an-optimization-minimization-problem"]], "Analyzing the last results": [[43, "analyzing-the-last-results"], [44, "analyzing-the-last-results"]], "And with loops": [[41, "and-with-loops"], [42, "and-with-loops"]], "And Logistic Regression": [[41, "and-logistic-regression"], [42, "and-logistic-regression"]], "And a corresponding example using scikit-learn": [[40, "and-a-corresponding-example-using-scikit-learn"], [41, "and-a-corresponding-example-using-scikit-learn"]], "And finally \\boldsymbol{X}\\boldsymbol{X}^T": [[37, "and-finally-boldsymbol-x-boldsymbol-x-t"], [38, "and-finally-boldsymbol-x-boldsymbol-x-t"]], "And finally ADAM": [[22, "and-finally-adam"], [41, "and-finally-adam"], [42, "and-finally-adam"]], "And what about using neural networks?": [[36, "and-what-about-using-neural-networks"]], "Another Example from Scikit-Learn\u2019s Repository": [[39, "another-example-from-scikit-learn-s-repository"]], "Another Example, now with a polynomial fit": [[38, "another-example-now-with-a-polynomial-fit"]], "Another example, the moons again": [[10, "another-example-the-moons-again"]], "Any other topics, impressions, ideas etc you would like to share with us?": [[26, "any-other-topics-impressions-ideas-etc-you-would-like-to-share-with-us"]], "Applied Data Analysis and Machine Learning": [[27, null]], "Artificial neurons": [[42, "artificial-neurons"], [43, "artificial-neurons"]], "Assumptions made": [[38, "assumptions-made"], [39, "assumptions-made"]], "Autocorrelation function": [[33, "autocorrelation-function"]], "Autoencoders: Overarching view": [[50, "autoencoders-overarching-view"]], "Autograd with more complicated functions": [[41, "autograd-with-more-complicated-functions"], [42, "autograd-with-more-complicated-functions"]], "Automatic differentiation": [[14, "automatic-differentiation"], [41, "automatic-differentiation"], [42, "automatic-differentiation"], [43, "automatic-differentiation"]], "Automatic differentiation through examples": [[43, "automatic-differentiation-through-examples"]], "Back propagation": [[45, "back-propagation"]], "Back propagation and automatic differentiation": [[45, "back-propagation-and-automatic-differentiation"]], "Back propagation in time in equations": [[47, "back-propagation-in-time-in-equations"]], "Back propagation in time through figures, part 1": [[47, "back-propagation-in-time-through-figures-part-1"]], "Back propagation in time, part 2": [[47, "back-propagation-in-time-part-2"]], "Back propagation in time, part 3": [[47, "back-propagation-in-time-part-3"]], "Back propagation in time, part 4": [[47, "back-propagation-in-time-part-4"]], "Back to the Cancer Data": [[12, "back-to-the-cancer-data"]], "Background literature": [[29, "background-literature"], [30, "background-literature"]], "Backpropagation in the convolutional layer": [[46, "backpropagation-in-the-convolutional-layer"], [47, "backpropagation-in-the-convolutional-layer"]], "Backpropagation through time": [[47, "backpropagation-through-time"]], "Bagging": [[11, "bagging"], [48, "bagging"], [49, "bagging"]], "Bagging Examples": [[11, "bagging-examples"]], "Basic Matrix Features": [[28, "basic-matrix-features"]], "Basic Steps of AdaBoost": [[48, "basic-steps-of-adaboost"], [49, "basic-steps-of-adaboost"], [50, "basic-steps-of-adaboost"]], "Basic ideas of the Principal Component Analysis (PCA)": [[12, null]], "Basic layout": [[47, "basic-layout"]], "Basic layout, Figures from Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch": [[47, "basic-layout-figures-from-sebastian-rashcka-et-al-machine-learning-with-sickit-learn-and-pytorch"]], "Basic math of the SVD": [[6, "basic-math-of-the-svd"], [37, "basic-math-of-the-svd"]], "Basics": [[8, "basics"], [40, "basics"]], "Basics of a tree": [[10, "basics-of-a-tree"], [48, "basics-of-a-tree"]], "Basics of an NN": [[43, "basics-of-an-nn"]], "Batch Normalization": [[2, "batch-normalization"], [43, "batch-normalization"], [44, "batch-normalization"]], "Batches and mini-batches": [[41, "batches-and-mini-batches"], [42, "batches-and-mini-batches"]], "Bayesian Machine Learning": [[50, "bayesian-machine-learning"]], "Bayes\u2019 Theorem": [[38, "bayes-theorem"], [39, "bayes-theorem"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[6, "bayes-theorem-and-ridge-and-lasso-regression"], [38, "bayes-theorem-and-ridge-and-lasso-regression"], [39, "bayes-theorem-and-ridge-and-lasso-regression"]], "Best wishes to you all and thanks so much for your heroic efforts this semester": [[50, "best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester"]], "Boltzmann Machines": [[50, "boltzmann-machines"]], "Boltzmann machines (BM)": [[50, "boltzmann-machines-bm"]], "Boosting, a Bird\u2019s Eye View": [[11, "boosting-a-bird-s-eye-view"], [48, "boosting-a-bird-s-eye-view"], [49, "boosting-a-bird-s-eye-view"], [50, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[7, "bootstrap"]], "Brief reminder on Newton-Raphson\u2019s method": [[40, "brief-reminder-on-newton-raphson-s-method"], [41, "brief-reminder-on-newton-raphson-s-method"]], "Bringing it together": [[43, "bringing-it-together"], [44, "bringing-it-together"]], "Bringing it together, first back propagation equation": [[13, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[2, null]], "Building a neural network code": [[44, "building-a-neural-network-code"], [45, "building-a-neural-network-code"]], "Building a tree, regression": [[10, "building-a-tree-regression"], [48, "building-a-tree-regression"], [49, "building-a-tree-regression"]], "Building convolutional neural networks in Tensorflow and Keras": [[46, "building-convolutional-neural-networks-in-tensorflow-and-keras"], [47, "building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Building neural networks in Tensorflow and Keras": [[2, "building-neural-networks-in-tensorflow-and-keras"], [44, "building-neural-networks-in-tensorflow-and-keras"], [45, "building-neural-networks-in-tensorflow-and-keras"]], "Building our own CNN code": [[46, "building-our-own-cnn-code"], [47, "building-our-own-cnn-code"]], "Building up AdaBoost": [[48, "building-up-adaboost"], [49, "building-up-adaboost"], [50, "building-up-adaboost"]], "But noen of these can compete with Newton\u2019s method": [[22, "but-noen-of-these-can-compete-with-newton-s-method"]], "But none of these can compete with Newton\u2019s method": [[41, "but-none-of-these-can-compete-with-newton-s-method"], [42, "but-none-of-these-can-compete-with-newton-s-method"]], "CNNs in brief": [[46, "cnns-in-brief"], [47, "cnns-in-brief"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[4, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "CNNs in more detail, simple example": [[46, "cnns-in-more-detail-simple-example"]], "Cancer Data again now with Decision Trees and other Methods": [[10, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Chain rule": [[43, "chain-rule"]], "Chain rule again": [[47, "chain-rule-again"]], "Chain rule, forward and reverse modes": [[43, "chain-rule-forward-and-reverse-modes"]], "Challenge yourself the coming weekend": [[40, "challenge-yourself-the-coming-weekend"]], "Choose a Model and Algorithm": [[50, "choose-a-model-and-algorithm"]], "Choose cost function and optimizer": [[2, "choose-cost-function-and-optimizer"], [44, "choose-cost-function-and-optimizer"], [45, "choose-cost-function-and-optimizer"]], "Class of functions we can approximate": [[43, "class-of-functions-we-can-approximate"]], "Classical PCA Theorem": [[12, "classical-pca-theorem"]], "Classification and Regression, from linear and logistic regression to neural networks": [[30, "classification-and-regression-from-linear-and-logistic-regression-to-neural-networks"]], "Classification problems": [[40, "classification-problems"]], "Classification tree, how to split nodes": [[48, "classification-tree-how-to-split-nodes"], [49, "classification-tree-how-to-split-nodes"]], "Clustering and Unsupervised Learning": [[15, null]], "Code Example for Cross-validation and k-fold Cross-validation": [[39, "code-example-for-cross-validation-and-k-fold-cross-validation"], [40, "code-example-for-cross-validation-and-k-fold-cross-validation"]], "Code example": [[43, "code-example"], [44, "code-example"]], "Code example for the Bootstrap method": [[39, "code-example-for-the-bootstrap-method"]], "Code examples from week 39 and 40": [[22, "code-examples-from-week-39-and-40"]], "Code for SVD and Inversion of Matrices": [[6, "code-for-svd-and-inversion-of-matrices"], [38, "code-for-svd-and-inversion-of-matrices"]], "Code with a Number of Minibatches which varies": [[41, "code-with-a-number-of-minibatches-which-varies"], [42, "code-with-a-number-of-minibatches-which-varies"]], "Code with a Number of Minibatches which varies, analytical gradient": [[22, "code-with-a-number-of-minibatches-which-varies-analytical-gradient"]], "Codes and Approaches": [[15, "codes-and-approaches"]], "Codes for the SVD": [[6, "codes-for-the-svd"], [37, "codes-for-the-svd"]], "Collect and pre-process data": [[2, "collect-and-pre-process-data"], [44, "collect-and-pre-process-data"], [44, "id2"], [45, "collect-and-pre-process-data"], [45, "id2"]], "Communication channels": [[36, "communication-channels"]], "Commutative process": [[46, "commutative-process"]], "Compact expressions": [[43, "compact-expressions"], [44, "compact-expressions"]], "Compare Bagging on Trees with Random Forests": [[11, "compare-bagging-on-trees-with-random-forests"], [48, "compare-bagging-on-trees-with-random-forests"], [49, "compare-bagging-on-trees-with-random-forests"], [50, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[3, "comparing-with-a-numerical-scheme"], [45, "comparing-with-a-numerical-scheme"]], "Comparison with OLS": [[38, "comparison-with-ols"]], "Compile and train the model": [[46, "compile-and-train-the-model"], [47, "compile-and-train-the-model"]], "Completing the list": [[43, "completing-the-list"], [44, "completing-the-list"]], "Computation of gradients": [[41, "computation-of-gradients"], [42, "computation-of-gradients"]], "Computing a Tree using the Gini Index": [[48, "computing-a-tree-using-the-gini-index"], [49, "computing-a-tree-using-the-gini-index"]], "Computing the Gini Factor": [[48, "computing-the-gini-factor"], [49, "computing-the-gini-factor"]], "Computing the Gini index": [[10, "computing-the-gini-index"]], "Computing the various Gini Indices": [[48, "computing-the-various-gini-indices"], [49, "computing-the-various-gini-indices"]], "Computing the various Gini Indices, Hours slept": [[48, "computing-the-various-gini-indices-hours-slept"]], "Computing the various Gini Indices, Hours studied": [[48, "computing-the-various-gini-indices-hours-studied"]], "Conditional Probability": [[38, "conditional-probability"], [39, "conditional-probability"]], "Conditions on convex functions": [[40, "conditions-on-convex-functions"], [41, "conditions-on-convex-functions"]], "Confidence Intervals": [[39, "confidence-intervals"]], "Conjugate gradient method": [[14, "conjugate-gradient-method"], [41, "conjugate-gradient-method"], [41, "id2"], [41, "id3"], [41, "id4"], [41, "id5"], [41, "id6"], [41, "id7"]], "Conjugate gradient method and iterations": [[41, "conjugate-gradient-method-and-iterations"]], "Convex function": [[40, "convex-function"], [41, "convex-function"]], "Convex functions": [[14, "convex-functions"], [40, "convex-functions"], [41, "convex-functions"]], "Convolution": [[46, "convolution"], [47, "convolution"]], "Convolution Examples: Polynomial multiplication": [[4, "convolution-examples-polynomial-multiplication"], [46, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[4, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolution in the Fourier domain": [[46, "convolution-in-the-fourier-domain"], [47, "convolution-in-the-fourier-domain"]], "Convolution using separable kernels": [[46, "convolution-using-separable-kernels"], [47, "convolution-using-separable-kernels"]], "Convolution2DLayer: convolution in a hidden layer": [[46, "convolution2dlayer-convolution-in-a-hidden-layer"], [47, "convolution2dlayer-convolution-in-a-hidden-layer"]], "Convolutional Neural Network": [[13, "convolutional-neural-network"], [42, "convolutional-neural-network"], [43, "convolutional-neural-network"]], "Convolutional Neural Networks": [[4, null]], "Convolutional Neural Networks (recognizing images)": [[46, "convolutional-neural-networks-recognizing-images"], [47, "convolutional-neural-networks-recognizing-images"]], "Correlation Function and Design/Feature Matrix": [[37, "correlation-function-and-design-feature-matrix"]], "Correlation Matrix": [[12, "correlation-matrix"], [37, "correlation-matrix"]], "Correlation Matrix with Pandas": [[37, "correlation-matrix-with-pandas"]], "Correlation Matrix with Pandas and the Franke function": [[37, "correlation-matrix-with-pandas-and-the-franke-function"]], "Cost complexity pruning": [[48, "cost-complexity-pruning"], [49, "cost-complexity-pruning"]], "Cost functions": [[44, "cost-functions"], [45, "cost-functions"], [46, "cost-functions"], [47, "cost-functions"]], "Counting the number of floating point operations": [[43, "counting-the-number-of-floating-point-operations"]], "Course Format": [[36, "course-format"]], "Covariance Matrix Examples": [[37, "covariance-matrix-examples"]], "Covariance and Correlation Matrix": [[37, "covariance-and-correlation-matrix"]], "Cross correlation": [[46, "cross-correlation"]], "Cross-validation": [[7, "cross-validation"]], "Cross-validation in brief": [[39, "cross-validation-in-brief"], [40, "cross-validation-in-brief"]], "Deadlines for projects (tentative)": [[36, "deadlines-for-projects-tentative"]], "Decision trees and Regression": [[48, "decision-trees-and-regression"]], "Decision trees, overarching aims": [[10, null], [48, "decision-trees-overarching-aims"]], "Deep learning methods": [[36, "deep-learning-methods"]], "Define model and architecture": [[2, "define-model-and-architecture"], [44, "define-model-and-architecture"], [45, "define-model-and-architecture"]], "Defining different types of RBMs": [[50, "defining-different-types-of-rbms"]], "Defining intermediate operations": [[43, "defining-intermediate-operations"]], "Defining the cost function": [[2, "defining-the-cost-function"], [44, "defining-the-cost-function"], [45, "defining-the-cost-function"]], "Defining the data sets to analyze yourself": [[31, "defining-the-data-sets-to-analyze-yourself"]], "Defining the problem": [[45, "defining-the-problem"]], "Definitions": [[43, "definitions"], [44, "definitions"]], "Demonstration": [[46, "demonstration"], [47, "demonstration"]], "Derivative of the cost function": [[43, "derivative-of-the-cost-function"], [44, "derivative-of-the-cost-function"]], "Derivatives and the chain rule": [[13, "derivatives-and-the-chain-rule"], [43, "derivatives-and-the-chain-rule"], [44, "derivatives-and-the-chain-rule"]], "Derivatives in terms of z_j^L": [[43, "derivatives-in-terms-of-z-j-l"], [44, "derivatives-in-terms-of-z-j-l"]], "Derivatives of the hidden layer": [[43, "derivatives-of-the-hidden-layer"], [44, "derivatives-of-the-hidden-layer"]], "Derivatives, example 1": [[37, "derivatives-example-1"]], "Deriving OLS from a probability distribution": [[6, "deriving-ols-from-a-probability-distribution"], [38, "deriving-ols-from-a-probability-distribution"], [39, "deriving-ols-from-a-probability-distribution"]], "Deriving the Lasso Regression Equations": [[37, "deriving-the-lasso-regression-equations"], [38, "deriving-the-lasso-regression-equations"]], "Deriving the Ridge Regression Equations": [[37, "deriving-the-ridge-regression-equations"], [38, "deriving-the-ridge-regression-equations"]], "Deriving the back propagation code for a multilayer perceptron model": [[13, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Description of two-dimensional function": [[29, "description-of-two-dimensional-function"]], "Developing a code for doing neural networks with back propagation": [[2, "developing-a-code-for-doing-neural-networks-with-back-propagation"], [44, "developing-a-code-for-doing-neural-networks-with-back-propagation"], [45, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[12, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Did the projects and the teaching material allow you to deepen your insights about Machine Learning methods?": [[26, "did-the-projects-and-the-teaching-material-allow-you-to-deepen-your-insights-about-machine-learning-methods"]], "Different kernels and Mercer\u2019s theorem": [[9, "different-kernels-and-mercer-s-theorem"]], "Differential equations": [[47, "differential-equations"]], "Disadvantages": [[10, "disadvantages"], [48, "disadvantages"], [49, "disadvantages"]], "Discussing the correlation data": [[0, "discussing-the-correlation-data"], [40, "discussing-the-correlation-data"]], "Distributed machine learning": [[50, "distributed-machine-learning"]], "Does Logistic Regression do a better Job?": [[42, "does-logistic-regression-do-a-better-job"], [43, "does-logistic-regression-do-a-better-job"]], "Doing it correctly": [[38, "doing-it-correctly"], [39, "doing-it-correctly"]], "Domains and probabilities": [[33, "domains-and-probabilities"]], "Dropout": [[2, "dropout"], [43, "dropout"], [44, "dropout"]], "Dual learning": [[50, "dual-learning"]], "ELU function": [[43, "elu-function"], [44, "elu-function"], [45, "elu-function"]], "Economy-size SVD": [[37, "economy-size-svd"]], "Efficient Polynomial Multiplication": [[46, "efficient-polynomial-multiplication"]], "Elements of Probability Theory and Statistical Data Analysis": [[33, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[11, null], [48, "ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods"], [49, "ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods"]], "Entropy and the ID3 algorithm": [[10, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[36, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[2, "evaluate-model-performance-on-test-data"], [44, "evaluate-model-performance-on-test-data"], [45, "evaluate-model-performance-on-test-data"]], "Example 2": [[37, "example-2"]], "Example 3": [[37, "example-3"]], "Example 4": [[37, "example-4"]], "Example Matrix": [[37, "example-matrix"]], "Example code for Bias-Variance tradeoff": [[39, "example-code-for-bias-variance-tradeoff"]], "Example of Usage of Bayes\u2019 theorem": [[38, "example-of-usage-of-bayes-theorem"], [39, "example-of-usage-of-bayes-theorem"]], "Example of own Standard scaling": [[37, "example-of-own-standard-scaling"]], "Example relevant for the exercises": [[37, "example-relevant-for-the-exercises"]], "Example: Exponential decay": [[3, "example-exponential-decay"], [45, "example-exponential-decay"]], "Example: Population growth": [[3, "example-population-growth"], [45, "example-population-growth"]], "Example: Solving the one dimensional Poisson equation": [[45, "example-solving-the-one-dimensional-poisson-equation"]], "Example: Solving the wave equation with Neural Networks": [[45, "example-solving-the-wave-equation-with-neural-networks"]], "Example: The diffusion equation": [[3, "example-the-diffusion-equation"], [45, "example-the-diffusion-equation"]], "Example: binary classification problem": [[2, "example-binary-classification-problem"], [44, "example-binary-classification-problem"], [45, "example-binary-classification-problem"]], "Examples": [[36, "examples"]], "Examples of CNN setups": [[46, "examples-of-cnn-setups"]], "Examples of XOR, OR and AND gates": [[42, "examples-of-xor-or-and-and-gates"], [43, "examples-of-xor-or-and-and-gates"]], "Examples of likelihood functions used in logistic regression and neural networks": [[8, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Examples of likelihood functions used in logistic regression and nueral networks": [[0, "examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks"], [40, "examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks"]], "Exercise 1": [[23, "exercise-1"]], "Exercise 1 - Understand the feed forward pass": [[24, "exercise-1-understand-the-feed-forward-pass"]], "Exercise 1: Analytical exercises": [[17, "exercise-1-analytical-exercises"], [18, "exercise-1-analytical-exercises"]], "Exercise 1: Expectation values for ordinary least squares expressions": [[19, "exercise-1-expectation-values-for-ordinary-least-squares-expressions"]], "Exercise 1: Including more data": [[43, "exercise-1-including-more-data"]], "Exercise 1: Linear and logistic regression methods": [[25, "exercise-1-linear-and-logistic-regression-methods"]], "Exercise 1: Setting up various Python environments": [[1, "exercise-1-setting-up-various-python-environments"], [16, "exercise-1-setting-up-various-python-environments"], [36, "exercise-1-setting-up-various-python-environments"]], "Exercise 2": [[23, "exercise-2"]], "Exercise 2 - Gradient with one layer using autograd": [[24, "exercise-2-gradient-with-one-layer-using-autograd"]], "Exercise 2: Adding Ridge Regression": [[18, "exercise-2-adding-ridge-regression"]], "Exercise 2: Deep learning": [[25, "exercise-2-deep-learning"]], "Exercise 2: Expectation values for Ridge regression": [[19, "exercise-2-expectation-values-for-ridge-regression"]], "Exercise 2: Extended program": [[43, "exercise-2-extended-program"]], "Exercise 2: making your own data and exploring scikit-learn": [[1, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [16, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [17, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [36, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3": [[23, "exercise-3"]], "Exercise 3 - Gradient with one layer writing backpropagation by hand": [[24, "exercise-3-gradient-with-one-layer-writing-backpropagation-by-hand"]], "Exercise 3: Decision trees and ensemble methods": [[25, "exercise-3-decision-trees-and-ensemble-methods"]], "Exercise 3: Normalizing our data": [[1, "exercise-3-normalizing-our-data"]], "Exercise 3: Split data in test and training data": [[16, "exercise-3-split-data-in-test-and-training-data"], [17, "exercise-3-split-data-in-test-and-training-data"], [36, "exercise-3-split-data-in-test-and-training-data"]], "Exercise 4 - Custom activation for each layer": [[23, "exercise-4-custom-activation-for-each-layer"]], "Exercise 4 - Gradient with two layers writing backpropagation by hand": [[24, "exercise-4-gradient-with-two-layers-writing-backpropagation-by-hand"]], "Exercise 4: Adding Ridge Regression": [[1, "exercise-4-adding-ridge-regression"]], "Exercise 4: Optimization part": [[25, "exercise-4-optimization-part"]], "Exercise 5 - Gradient with any number of layers writing backpropagation by hand": [[24, "exercise-5-gradient-with-any-number-of-layers-writing-backpropagation-by-hand"]], "Exercise 5 - Processing multiple inputs at once": [[23, "exercise-5-processing-multiple-inputs-at-once"]], "Exercise 5: Analysis of results": [[25, "exercise-5-analysis-of-results"]], "Exercise 5: Analytical exercises": [[1, "exercise-5-analytical-exercises"]], "Exercise 6 - Batched inputs": [[24, "exercise-6-batched-inputs"]], "Exercise 6 - Predicting on real data": [[23, "exercise-6-predicting-on-real-data"]], "Exercise 7 - Training": [[24, "exercise-7-training"]], "Exercise 7 - Training on real data (Optional)": [[23, "exercise-7-training-on-real-data-optional"]], "Exercise 8 (Optional) - Object orientation": [[24, "exercise-8-optional-object-orientation"]], "Exercise week 47": [[25, null]], "Exercise: Cross-validation as resampling techniques, adding more complexity": [[7, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[7, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[7, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function with resampling": [[7, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[7, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function with resampling": [[7, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[1, "exercises"], [16, "exercises"], [36, "exercises"]], "Exercises Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40": [[0, null]], "Exercises and Projects": [[7, "exercises-and-projects"]], "Exercises and lab session week 43": [[45, "exercises-and-lab-session-week-43"]], "Exercises week 34": [[16, null]], "Exercises week 35": [[17, null]], "Exercises week 36": [[18, null]], "Exercises week 37": [[19, null]], "Exercises week 38": [[20, null]], "Exercises week 39": [[21, null]], "Exercises week 41": [[22, null]], "Exercises week 42": [[23, null]], "Exercises week 43": [[24, null]], "Exercises week 48": [[26, null]], "Expectation value and variance": [[38, "expectation-value-and-variance"], [39, "expectation-value-and-variance"]], "Expectation value and variance for \\boldsymbol{\\beta}": [[38, "expectation-value-and-variance-for-boldsymbol-beta"], [39, "expectation-value-and-variance-for-boldsymbol-beta"]], "Expectation values": [[33, "expectation-values"]], "Explainable machine learning": [[50, "explainable-machine-learning"]], "Explicit derivatives": [[43, "explicit-derivatives"], [44, "explicit-derivatives"]], "Exploding gradients": [[43, "exploding-gradients"], [44, "exploding-gradients"]], "Extending to more predictors": [[0, "extending-to-more-predictors"], [40, "extending-to-more-predictors"]], "Extending to more than one variable": [[40, "extending-to-more-than-one-variable"], [41, "extending-to-more-than-one-variable"]], "Extremely useful tools, strongly recommended": [[36, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[13, "feed-forward-neural-networks"], [42, "feed-forward-neural-networks"], [43, "feed-forward-neural-networks"]], "Feed-forward pass": [[2, "feed-forward-pass"], [44, "feed-forward-pass"], [45, "feed-forward-pass"]], "Feedback connections": [[47, "feedback-connections"]], "Final back propagating equation": [[13, "final-back-propagating-equation"], [43, "final-back-propagating-equation"], [44, "final-back-propagating-equation"]], "Final derivatives": [[43, "final-derivatives"]], "Final expression": [[43, "final-expression"], [44, "final-expression"]], "Final expressions": [[41, "final-expressions"]], "Final expressions for the biases of the hidden layer": [[43, "final-expressions-for-the-biases-of-the-hidden-layer"], [44, "final-expressions-for-the-biases-of-the-hidden-layer"]], "Final part": [[46, "final-part"], [47, "final-part"]], "Final regressor code": [[48, "final-regressor-code"], [49, "final-regressor-code"]], "Final technicalities I": [[45, "final-technicalities-i"]], "Final technicalities II": [[45, "final-technicalities-ii"]], "Final technicalities III": [[45, "final-technicalities-iii"]], "Final technicalities IV": [[45, "final-technicalities-iv"]], "Final visualization": [[46, "final-visualization"], [47, "final-visualization"]], "Finally, evaluate the model": [[46, "finally-evaluate-the-model"], [47, "finally-evaluate-the-model"]], "Finding the Limit": [[39, "finding-the-limit"]], "Finding the number of parameters": [[46, "finding-the-number-of-parameters"]], "Fine-tuning neural network hyperparameters": [[2, "fine-tuning-neural-network-hyperparameters"], [43, "fine-tuning-neural-network-hyperparameters"], [44, "fine-tuning-neural-network-hyperparameters"]], "First network example, simple percepetron with one input": [[43, "first-network-example-simple-percepetron-with-one-input"], [44, "first-network-example-simple-percepetron-with-one-input"]], "Fitting an Equation of State for Dense Nuclear Matter": [[1, "fitting-an-equation-of-state-for-dense-nuclear-matter"], [36, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "Fixing the singularity": [[37, "fixing-the-singularity"]], "Flattening Layer": [[46, "flattening-layer"], [47, "flattening-layer"]], "For exercise sessions: Why Linear Regression (aka Ordinary Least Squares and family), repeat from last week": [[37, "for-exercise-sessions-why-linear-regression-aka-ordinary-least-squares-and-family-repeat-from-last-week"]], "Forget and input": [[47, "forget-and-input"]], "Format for electronic delivery of report and programs": [[29, "format-for-electronic-delivery-of-report-and-programs"], [30, "format-for-electronic-delivery-of-report-and-programs"], [31, "format-for-electronic-delivery-of-report-and-programs"]], "Forward and reverse modes": [[43, "forward-and-reverse-modes"]], "Four effective ways to learn an RNN and preparing for next week": [[47, "four-effective-ways-to-learn-an-rnn-and-preparing-for-next-week"]], "Fourier series and Toeplitz matrices": [[46, "fourier-series-and-toeplitz-matrices"]], "Frequently used scaling functions": [[37, "frequently-used-scaling-functions"]], "From FFNNs and CNNs to recurrent neural networks (RNNs)": [[47, "from-ffnns-and-cnns-to-recurrent-neural-networks-rnns"]], "From OLS to Ridge and Lasso": [[38, "from-ols-to-ridge-and-lasso"]], "From one to many layers, the universal approximation theorem": [[13, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Full object-oriented implementation": [[44, "full-object-oriented-implementation"], [45, "full-object-oriented-implementation"]], "Fully Connected Layers": [[46, "fully-connected-layers"], [47, "fully-connected-layers"]], "Functionality in Scikit-Learn": [[37, "functionality-in-scikit-learn"]], "Functions using mathematical functions from Numpy": [[41, "functions-using-mathematical-functions-from-numpy"], [42, "functions-using-mathematical-functions-from-numpy"]], "Further Dimensionality Remarks": [[4, "further-dimensionality-remarks"]], "Further Manipulations": [[37, "further-manipulations"]], "Further example: Computing the Gini index": [[48, "further-example-computing-the-gini-index"], [49, "further-example-computing-the-gini-index"]], "Further properties (important for our analyses later)": [[6, "further-properties-important-for-our-analyses-later"], [37, "further-properties-important-for-our-analyses-later"]], "Further remarks": [[46, "further-remarks"]], "Further simplification": [[46, "further-simplification"]], "Gating mechanism: Long Short Term Memory (LSTM)": [[47, "gating-mechanism-long-short-term-memory-lstm"]], "Gaussian Elimination": [[28, "gaussian-elimination"]], "General Features": [[10, "general-features"], [48, "general-features"]], "General linear models and linear algebra": [[36, "general-linear-models-and-linear-algebra"]], "Generalizing the above one-dimensional case": [[46, "generalizing-the-above-one-dimensional-case"]], "Generalizing the fitting procedure as a linear algebra problem": [[36, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [36, "id1"]], "Generative Adversarial Networks": [[5, "generative-adversarial-networks"]], "Generative Models": [[5, "generative-models"]], "Geometric Interpretation and link with Singular Value Decomposition": [[12, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Getting serious, the back propagation equations for a neural network": [[43, "getting-serious-the-back-propagation-equations-for-a-neural-network"]], "Getting started with Jax, note the way we import numpy": [[22, "getting-started-with-jax-note-the-way-we-import-numpy"], [42, "getting-started-with-jax-note-the-way-we-import-numpy"]], "Goals": [[50, "goals"]], "Gradient Boosting, Classification Example": [[11, "gradient-boosting-classification-example"], [49, "gradient-boosting-classification-example"], [50, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[11, "gradient-boosting-examples-of-regression"], [49, "gradient-boosting-examples-of-regression"], [50, "gradient-boosting-examples-of-regression"]], "Gradient Boosting, algorithm": [[49, "gradient-boosting-algorithm"], [50, "gradient-boosting-algorithm"]], "Gradient Clipping": [[2, "gradient-clipping"], [43, "gradient-clipping"], [44, "gradient-clipping"]], "Gradient Descent Example": [[40, "id1"], [41, "id8"]], "Gradient boosting, making our own code for a regression case": [[49, "gradient-boosting-making-our-own-code-for-a-regression-case"], [50, "gradient-boosting-making-our-own-code-for-a-regression-case"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[11, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"], [49, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"], [50, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[3, "gradient-descent"], [45, "gradient-descent"]], "Gradient descent and Logistic regression": [[0, "gradient-descent-and-logistic-regression"], [42, "gradient-descent-and-logistic-regression"]], "Gradient descent and Ridge": [[40, "gradient-descent-and-ridge"], [41, "gradient-descent-and-ridge"]], "Gradient descent example": [[40, "gradient-descent-example"], [41, "gradient-descent-example"]], "Gradient expressions": [[43, "gradient-expressions"], [44, "gradient-expressions"]], "Gradient method": [[41, "gradient-method"]], "Gradients of loss functions": [[47, "gradients-of-loss-functions"]], "Grading": [[34, "grading"], [36, "grading"]], "Grid Search": [[40, "grid-search"]], "Growing a classification tree": [[48, "growing-a-classification-tree"], [49, "growing-a-classification-tree"]], "Hidden layers": [[43, "hidden-layers"], [44, "hidden-layers"]], "Homogeneous data": [[43, "homogeneous-data"], [44, "homogeneous-data"]], "Housing data, the code": [[1, "housing-data-the-code"]], "How do we set it up?": [[48, "how-do-we-set-it-up"]], "How do you judge your own level of knowledge on machine learning before and after this course?": [[26, "how-do-you-judge-your-own-level-of-knowledge-on-machine-learning-before-and-after-this-course"]], "How to do image compression before the era of deep learning": [[46, "how-to-do-image-compression-before-the-era-of-deep-learning"]], "How to set up the cross-validation for Ridge and/or Lasso": [[40, "how-to-set-up-the-cross-validation-for-ridge-and-or-lasso"]], "How would you improve this course?": [[26, "how-would-you-improve-this-course"]], "Hyperplanes and all that": [[9, "hyperplanes-and-all-that"]], "Identifying Terms": [[39, "identifying-terms"]], "If you did not attend the lectures or the lab sessions, which resources did you use?": [[26, "if-you-did-not-attend-the-lectures-or-the-lab-sessions-which-resources-did-you-use"]], "Illustration of a single perceptron model and a multi-perceptron model": [[42, "illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model"], [43, "illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model"]], "Implementing a memory cell in a neural network": [[47, "implementing-a-memory-cell-in-a-neural-network"]], "Important Matrix and vector handling packages": [[28, "important-matrix-and-vector-handling-packages"]], "Important observations": [[43, "important-observations"], [44, "important-observations"]], "Important technicalities: More on Rescaling data": [[38, "important-technicalities-more-on-rescaling-data"]], "Importing Keras and Tensorflow": [[46, "importing-keras-and-tensorflow"], [47, "importing-keras-and-tensorflow"]], "Improving gradient descent with momentum": [[41, "improving-gradient-descent-with-momentum"]], "Improving performance": [[2, "improving-performance"], [44, "improving-performance"], [45, "improving-performance"]], "In general not this simple": [[43, "in-general-not-this-simple"]], "In summary": [[34, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[14, "including-stochastic-gradient-descent-with-autograd"], [22, "including-stochastic-gradient-descent-with-autograd"], [41, "including-stochastic-gradient-descent-with-autograd"], [42, "including-stochastic-gradient-descent-with-autograd"]], "Including more classes": [[0, "including-more-classes"], [40, "including-more-classes"]], "Incremental PCA": [[12, "incremental-pca"]], "Independent and Identically Distributed (iid)": [[38, "independent-and-identically-distributed-iid"]], "Independent and Identically Distrubuted (iid)": [[39, "independent-and-identically-distrubuted-iid"]], "Input gate": [[47, "input-gate"]], "Inputs to the activation function": [[43, "inputs-to-the-activation-function"], [44, "inputs-to-the-activation-function"]], "Insights from the paper by Glorot and Bengio": [[43, "insights-from-the-paper-by-glorot-and-bengio"], [44, "insights-from-the-paper-by-glorot-and-bengio"]], "Installing R, C++, cython or Julia": [[36, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[36, "installing-r-c-cython-numba-etc"]], "Instructor information": [[34, "instructor-information"]], "Interpretations and optimizing our parameters": [[36, "interpretations-and-optimizing-our-parameters"], [36, "id2"], [36, "id3"], [37, "interpretations-and-optimizing-our-parameters"], [37, "id1"], [37, "id2"]], "Interpretations of Bayes\u2019 Theorem": [[38, "interpretations-of-bayes-theorem"], [39, "interpretations-of-bayes-theorem"]], "Interpreting the Ridge results": [[37, "interpreting-the-ridge-results"], [38, "interpreting-the-ridge-results"]], "Introducing JAX": [[14, "introducing-jax"], [22, "introducing-jax"], [41, "introducing-jax"], [42, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[12, "introducing-the-covariance-and-correlation-functions"], [37, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[1, "introduction"], [7, "introduction"], [27, "introduction"], [28, "introduction"]], "Introduction to Neural networks": [[42, "introduction-to-neural-networks"], [43, "introduction-to-neural-networks"]], "Introduction to numerical projects": [[29, "introduction-to-numerical-projects"], [30, "introduction-to-numerical-projects"], [31, "introduction-to-numerical-projects"]], "Inverse of Rectangular Matrix": [[38, "inverse-of-rectangular-matrix"]], "Is the Logistic activation function (Sigmoid) our choice?": [[43, "is-the-logistic-activation-function-sigmoid-our-choice"], [44, "is-the-logistic-activation-function-sigmoid-our-choice"]], "Iterative Fitting, Classification and AdaBoost": [[11, "iterative-fitting-classification-and-adaboost"], [48, "iterative-fitting-classification-and-adaboost"], [49, "iterative-fitting-classification-and-adaboost"], [50, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[11, "iterative-fitting-regression-and-squared-error-cost-function"], [48, "iterative-fitting-regression-and-squared-error-cost-function"], [49, "iterative-fitting-regression-and-squared-error-cost-function"], [50, "iterative-fitting-regression-and-squared-error-cost-function"]], "Joint distribution": [[50, "joint-distribution"]], "Kernel PCA": [[12, "kernel-pca"]], "Kernels and non-linearity": [[9, "kernels-and-non-linearity"]], "Key Idea": [[46, "key-idea"], [47, "key-idea"]], "LSTM details": [[47, "lstm-details"]], "LU Decomposition, the inverse of a matrix": [[28, "lu-decomposition-the-inverse-of-a-matrix"]], "Lab sessions on Tuesday and Wednesday": [[46, "lab-sessions-on-tuesday-and-wednesday"]], "Lab session: Material from last week and relevant for the first project": [[40, "lab-session-material-from-last-week-and-relevant-for-the-first-project"]], "Lab sessions": [[50, "lab-sessions"]], "Lab sessions Tuesday and Wednesday": [[42, "lab-sessions-tuesday-and-wednesday"]], "Lab sessions and lectures": [[26, "lab-sessions-and-lectures"]], "Lab sessions week 39": [[41, "lab-sessions-week-39"]], "Lasso Regression": [[38, "lasso-regression"]], "Lasso and Bayes": [[38, "lasso-and-bayes"], [39, "lasso-and-bayes"]], "Lasso case": [[38, "lasso-case"]], "Layers": [[2, "layers"], [44, "layers"], [45, "layers"], [46, "layers"], [47, "layers"]], "Layers of a CNN": [[46, "layers-of-a-cnn"], [47, "layers-of-a-cnn"]], "Layers used to build CNNs": [[4, "layers-used-to-build-cnns"], [46, "layers-used-to-build-cnns"], [47, "layers-used-to-build-cnns"]], "Layout of a neural network with three hidden layers": [[43, "layout-of-a-neural-network-with-three-hidden-layers"]], "Layout of a neural network with three hidden layers (last later = l=L=4, first layer l=0)": [[44, "layout-of-a-neural-network-with-three-hidden-layers-last-later-l-l-4-first-layer-l-0"]], "Layout of a simple neural network with no hidden layer": [[43, "layout-of-a-simple-neural-network-with-no-hidden-layer"], [44, "layout-of-a-simple-neural-network-with-no-hidden-layer"]], "Layout of a simple neural network with one hidden layer": [[43, "layout-of-a-simple-neural-network-with-one-hidden-layer"], [44, "layout-of-a-simple-neural-network-with-one-hidden-layer"]], "Layout of a simple neural network with two input nodes, one hidden layer and one output node": [[43, "layout-of-a-simple-neural-network-with-two-input-nodes-one-hidden-layer-and-one-output-node"]], "Layout of a simple neural network with two input nodes, one hidden layer with two hidden noeds and one output node": [[44, "layout-of-a-simple-neural-network-with-two-input-nodes-one-hidden-layer-with-two-hidden-noeds-and-one-output-node"]], "Layout of input to first hidden layer l=1 from input layer l=0": [[44, "layout-of-input-to-first-hidden-layer-l-1-from-input-layer-l-0"]], "Learning outcomes": [[27, "learning-outcomes"], [36, "learning-outcomes"]], "Learning outcomes and overarching aims of this course": [[50, "learning-outcomes-and-overarching-aims-of-this-course"]], "Learning rate methods": [[44, "learning-rate-methods"], [45, "learning-rate-methods"]], "Lecture Monday October 21": [[45, "lecture-monday-october-21"]], "Lecture Monday October 7": [[43, "lecture-monday-october-7"]], "Lecture Monday September 23": [[41, "lecture-monday-september-23"]], "Lecture Monday September 23, Optimization, the central part of any Machine Learning algortithm": [[41, "lecture-monday-september-23-optimization-the-central-part-of-any-machine-learning-algortithm"]], "Lecture Monday September 30, 2024": [[42, "lecture-monday-september-30-2024"]], "Lecture Monday, November 25": [[50, "lecture-monday-november-25"]], "Lecture October 14, 2024": [[44, "lecture-october-14-2024"]], "Lectures and ComputerLab": [[36, "lectures-and-computerlab"]], "Limitations of NNs": [[43, "limitations-of-nns"], [44, "limitations-of-nns"]], "Limitations of supervised learning with deep networks": [[2, "limitations-of-supervised-learning-with-deep-networks"], [43, "limitations-of-supervised-learning-with-deep-networks"], [44, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[28, null]], "Linear Regression": [[1, null]], "Linear Regression Problems": [[37, "linear-regression-problems"]], "Linear Regression and the SVD": [[38, "linear-regression-and-the-svd"]], "Linear Regression code, Intercept handling first": [[37, "linear-regression-code-intercept-handling-first"]], "Linear Regression, basic elements": [[1, "linear-regression-basic-elements"]], "Linear classifier": [[40, "linear-classifier"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[6, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[6, "linking-the-regression-analysis-with-a-statistical-interpretation"], [38, "linking-the-regression-analysis-with-a-statistical-interpretation"], [39, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with RNNs": [[47, "linking-with-rnns"]], "Linking with the SVD": [[6, "linking-with-the-svd"], [37, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[35, "links-to-relevant-courses-at-the-university-of-oslo"]], "List of contents:": [[46, "list-of-contents"], [47, "list-of-contents"]], "Logistic Regression": [[8, null], [8, "id1"], [40, "logistic-regression"]], "Logistic function as the root of problems": [[43, "logistic-function-as-the-root-of-problems"], [44, "logistic-function-as-the-root-of-problems"]], "MNIST and GANs": [[5, "mnist-and-gans"]], "Machine Learning": [[36, "machine-learning"]], "Machine Learning Research": [[50, "machine-learning-research"]], "Machine learning": [[27, "machine-learning"], [50, "machine-learning"]], "Main textbooks": [[36, "main-textbooks"]], "Making a tree": [[10, "making-a-tree"], [48, "making-a-tree"], [49, "making-a-tree"]], "Making an ADAboost code yourself": [[49, "making-an-adaboost-code-yourself"], [50, "making-an-adaboost-code-yourself"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[11, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"], [48, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"], [49, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Making your own test-train splitting": [[37, "making-your-own-test-train-splitting"]], "Marginal Probability": [[38, "marginal-probability"], [39, "marginal-probability"]], "Material for Lecture Monday November 4": [[47, "material-for-lecture-monday-november-4"]], "Material for Lecture Monday October 28": [[46, "material-for-lecture-monday-october-28"]], "Material for lecture Monday September 16": [[40, "material-for-lecture-monday-september-16"]], "Material for lecture Monday September 2": [[38, "material-for-lecture-monday-september-2"]], "Material for lecture Monday September 9": [[39, "material-for-lecture-monday-september-9"]], "Material for lecture Monday, August 26": [[37, "material-for-lecture-monday-august-26"]], "Material for the active learning sessions Tuesday and Wednesday": [[38, "material-for-the-active-learning-sessions-tuesday-and-wednesday"]], "Material for the active learning sessions on Tuesday and Wednesday": [[43, "material-for-the-active-learning-sessions-on-tuesday-and-wednesday"], [44, "material-for-the-active-learning-sessions-on-tuesday-and-wednesday"]], "Material for the lab sessions": [[39, "material-for-the-lab-sessions"]], "Material for the lab sessions, additional ways to present classification results and other practicalities": [[47, "material-for-the-lab-sessions-additional-ways-to-present-classification-results-and-other-practicalities"]], "Material for the lecture on Monday October 7, 2024": [[43, "material-for-the-lecture-on-monday-october-7-2024"]], "Mathematical Interpretation of Ordinary Least Squares": [[6, "mathematical-interpretation-of-ordinary-least-squares"], [37, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical model": [[42, "mathematical-model"], [42, "id2"], [42, "id3"], [42, "id4"], [42, "id5"]], "Mathematical optimization of convex functions": [[9, "mathematical-optimization-of-convex-functions"]], "Mathematical setup": [[47, "mathematical-setup"]], "Mathematics of CNNs": [[4, "mathematics-of-cnns"], [46, "mathematics-of-cnns"]], "Mathematics of deep learning": [[43, "mathematics-of-deep-learning"], [44, "mathematics-of-deep-learning"], [45, "mathematics-of-deep-learning"]], "Mathematics of deep learning and neural networks": [[43, "mathematics-of-deep-learning-and-neural-networks"]], "Mathematics of the SVD and implications": [[6, "mathematics-of-the-svd-and-implications"], [37, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[36, "matrices-in-python"]], "Matrix multiplication": [[2, "matrix-multiplication"], [44, "matrix-multiplication"], [45, "matrix-multiplication"]], "Matrix multiplications": [[44, "matrix-multiplications"], [45, "matrix-multiplications"]], "Matrix-vector notation": [[42, "matrix-vector-notation"]], "Matrix-vector notation and activation": [[13, "matrix-vector-notation-and-activation"], [42, "matrix-vector-notation-and-activation"]], "Maximum Likelihood Estimation (MLE)": [[38, "maximum-likelihood-estimation-mle"], [39, "maximum-likelihood-estimation-mle"]], "Maximum likelihood": [[40, "maximum-likelihood"]], "Meet the covariance!": [[33, "meet-the-covariance"]], "Meet the Covariance Matrix": [[6, "meet-the-covariance-matrix"], [37, "meet-the-covariance-matrix"]], "Meet the Hessian Matrix": [[37, "meet-the-hessian-matrix"]], "Meet the Pandas": [[36, "meet-the-pandas"]], "Memory considerations": [[46, "memory-considerations"]], "Meta learning": [[50, "meta-learning"]], "Min-Max Scaling": [[37, "min-max-scaling"]], "Minimization process": [[45, "minimization-process"]], "Minimizing the cost function using gradient descent and automatic differentiation": [[45, "minimizing-the-cost-function-using-gradient-descent-and-automatic-differentiation"]], "Minimizing the cross entropy": [[0, "minimizing-the-cross-entropy"], [40, "minimizing-the-cross-entropy"]], "Minor rewrite": [[47, "minor-rewrite"]], "Momentum based GD": [[14, "momentum-based-gd"], [22, "momentum-based-gd"], [41, "momentum-based-gd"], [42, "momentum-based-gd"]], "Momentum parameter": [[41, "momentum-parameter"], [42, "momentum-parameter"]], "More LSTM details": [[47, "more-lstm-details"]], "More about RBMs": [[50, "more-about-rbms"]], "More autograd": [[41, "more-autograd"], [42, "more-autograd"]], "More bagging": [[48, "more-bagging"], [49, "more-bagging"]], "More basic Statistics and Bayes\u2019 theorem": [[38, "more-basic-statistics-and-bayes-theorem"], [39, "more-basic-statistics-and-bayes-theorem"]], "More classes": [[0, "more-classes"], [40, "more-classes"]], "More complicated Example: The Ising model": [[7, "more-complicated-example-the-ising-model"]], "More complicated function": [[43, "more-complicated-function"]], "More complicated functions using the elements of their arguments directly": [[41, "more-complicated-functions-using-the-elements-of-their-arguments-directly"], [42, "more-complicated-functions-using-the-elements-of-their-arguments-directly"]], "More considerations": [[43, "more-considerations"], [44, "more-considerations"]], "More details": [[45, "more-details"], [45, "id6"]], "More examples on bootstrap and cross-validation and errors": [[39, "more-examples-on-bootstrap-and-cross-validation-and-errors"]], "More interpretations": [[37, "more-interpretations"], [38, "more-interpretations"]], "More limitations": [[43, "more-limitations"], [44, "more-limitations"]], "More on Dimensionalities": [[4, "more-on-dimensionalities"], [46, "more-on-dimensionalities"]], "More on Rescaling data": [[7, "more-on-rescaling-data"]], "More on Steepest descent": [[40, "more-on-steepest-descent"], [41, "more-on-steepest-descent"]], "More on activation functions, output layers": [[43, "more-on-activation-functions-output-layers"], [44, "more-on-activation-functions-output-layers"], [45, "more-on-activation-functions-output-layers"]], "More on convex functions": [[40, "more-on-convex-functions"], [41, "more-on-convex-functions"]], "More on momentum based approaches": [[41, "more-on-momentum-based-approaches"], [42, "more-on-momentum-based-approaches"]], "More on the general approximation theorem": [[43, "more-on-the-general-approximation-theorem"]], "More preprocessing": [[37, "more-preprocessing"]], "More preprocessing examples, two-dimensional example, the Franke function": [[37, "more-preprocessing-examples-two-dimensional-example-the-franke-function"]], "More technicalities": [[45, "more-technicalities"]], "More thinking": [[37, "more-thinking"]], "More top-down perspectives": [[43, "more-top-down-perspectives"], [44, "more-top-down-perspectives"]], "Multiclass classification": [[44, "multiclass-classification"], [45, "multiclass-classification"]], "Multilayer perceptrons": [[13, "multilayer-perceptrons"], [42, "multilayer-perceptrons"], [43, "multilayer-perceptrons"]], "Multivariable functions": [[43, "multivariable-functions"]], "Network Elements, the energy function": [[50, "network-elements-the-energy-function"]], "Network requirements": [[3, "network-requirements"], [45, "network-requirements"]], "Neural Networks vs CNNs": [[4, "neural-networks-vs-cnns"], [46, "neural-networks-vs-cnns"], [47, "neural-networks-vs-cnns"]], "Neural network types": [[42, "neural-network-types"], [43, "neural-network-types"]], "Neural networks": [[13, null]], "New expression for the derivative": [[43, "new-expression-for-the-derivative"]], "New image (or volume)": [[46, "new-image-or-volume"]], "New vector": [[46, "new-vector"]], "Note about SVD Calculations": [[37, "note-about-svd-calculations"]], "Note on Scikit-Learn": [[38, "note-on-scikit-learn"]], "Numerical experiments and the covariance, central limit theorem": [[33, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[28, "numpy-and-arrays"], [36, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[36, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization Methods and Hyperparameters": [[50, "optimization-methods-and-hyperparameters"]], "Optimization and Deep learning": [[40, "optimization-and-deep-learning"]], "Optimization, the central part of any Machine Learning algortithm": [[14, null], [40, "optimization-the-central-part-of-any-machine-learning-algortithm"]], "Optimized Convolution2DLayer": [[46, "optimized-convolution2dlayer"], [47, "optimized-convolution2dlayer"]], "Optimizing our parameters": [[36, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[36, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[2, "optimizing-the-cost-function"], [44, "optimizing-the-cost-function"], [45, "optimizing-the-cost-function"]], "Optimizing the parameters": [[43, "optimizing-the-parameters"], [44, "optimizing-the-parameters"]], "Ordinary Differential Equations first": [[45, "ordinary-differential-equations-first"]], "Organizing our data": [[1, "organizing-our-data"], [36, "organizing-our-data"]], "Other Matrix and Vector Operations": [[28, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[5, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning at UiO": [[36, "other-courses-on-data-science-and-machine-learning-at-uio"], [50, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning at UiO, contn": [[36, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other ingredients of a neural network": [[43, "other-ingredients-of-a-neural-network"]], "Other measures in classification studies: Cancer Data again": [[0, "other-measures-in-classification-studies-cancer-data-again"], [40, "other-measures-in-classification-studies-cancer-data-again"]], "Other parameters": [[43, "other-parameters"]], "Other popular texts": [[36, "other-popular-texts"]], "Other techniques": [[12, "other-techniques"]], "Other types of networks": [[13, "other-types-of-networks"], [42, "other-types-of-networks"], [43, "other-types-of-networks"]], "Other useful relations": [[37, "other-useful-relations"]], "Other ways of visualizing the trees": [[10, "other-ways-of-visualizing-the-trees"], [48, "other-ways-of-visualizing-the-trees"], [49, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[36, "our-model-for-the-nuclear-binding-energies"]], "Output gate": [[47, "output-gate"]], "Output layer": [[43, "output-layer"], [44, "output-layer"]], "Overarching aims of the exercises this week": [[18, "overarching-aims-of-the-exercises-this-week"], [19, "overarching-aims-of-the-exercises-this-week"], [20, "overarching-aims-of-the-exercises-this-week"], [21, "overarching-aims-of-the-exercises-this-week"], [22, "overarching-aims-of-the-exercises-this-week"], [23, "overarching-aims-of-the-exercises-this-week"], [24, "overarching-aims-of-the-exercises-this-week"], [25, "overarching-aims-of-the-exercises-this-week"], [26, "overarching-aims-of-the-exercises-this-week"]], "Overarching view of a neural network": [[43, "overarching-view-of-a-neural-network"]], "Overview of first week": [[36, "overview-of-first-week"]], "Overview of week 48": [[50, "overview-of-week-48"]], "Overview video on Stochastic Gradient Descent": [[41, "overview-video-on-stochastic-gradient-descent"], [42, "overview-video-on-stochastic-gradient-descent"]], "Own code for Ordinary Least Squares": [[36, "own-code-for-ordinary-least-squares"], [37, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[12, "pca-and-scikit-learn"]], "Padding": [[46, "padding"]], "Pandas AI": [[36, "pandas-ai"]], "Parameters of neural networks": [[43, "parameters-of-neural-networks"]], "Parameters to train, common settings": [[46, "parameters-to-train-common-settings"]], "Part a)": [[31, "part-a"]], "Part a) : Ordinary Least Square (OLS) on the Franke function": [[29, "part-a-ordinary-least-square-ols-on-the-franke-function"]], "Part a), setting up the problem": [[31, "part-a-setting-up-the-problem"]], "Part a): Write your own Stochastic Gradient Descent code, first step": [[30, "part-a-write-your-own-stochastic-gradient-descent-code-first-step"]], "Part b)": [[31, "part-b"], [31, "id1"]], "Part b): Adding Ridge regression for the Franke function": [[29, "part-b-adding-ridge-regression-for-the-franke-function"]], "Part b): Writing your own Neural Network code": [[30, "part-b-writing-your-own-neural-network-code"]], "Part c)": [[31, "part-c"]], "Part c) Neural networks": [[31, "part-c-neural-networks"]], "Part c): Adding Lasso for the Franke function": [[29, "part-c-adding-lasso-for-the-franke-function"]], "Part c): Testing different activation functions": [[30, "part-c-testing-different-activation-functions"]], "Part d)": [[31, "part-d"]], "Part d) Neural network complexity": [[31, "part-d-neural-network-complexity"]], "Part d): Classification analysis using neural networks": [[30, "part-d-classification-analysis-using-neural-networks"]], "Part d): Paper and pencil part": [[29, "part-d-paper-and-pencil-part"]], "Part e)": [[31, "part-e"], [31, "id2"]], "Part e): Bias-variance trade-off and resampling techniques": [[29, "part-e-bias-variance-trade-off-and-resampling-techniques"]], "Part e): Write your Logistic Regression code, final step": [[30, "part-e-write-your-logistic-regression-code-final-step"]], "Part f) Critical evaluation of the various algorithms": [[30, "part-f-critical-evaluation-of-the-various-algorithms"]], "Part f): Cross-validation as resampling techniques, adding more complexity": [[29, "part-f-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Part g): Analysis of real data": [[29, "part-g-analysis-of-real-data"]], "Partial Differential Equations": [[3, "partial-differential-equations"], [45, "partial-differential-equations"]], "Paths for project 3": [[31, "paths-for-project-3"]], "Perspective on Machine Learning": [[50, "perspective-on-machine-learning"]], "Plan for week 39, September 23-27, 2024": [[41, "plan-for-week-39-september-23-27-2024"]], "Plan for week 41, October 7-11": [[43, "plan-for-week-41-october-7-11"]], "Plan for week 44": [[46, "plan-for-week-44"]], "Plan for week 46": [[48, "plan-for-week-46"]], "Plan for week 47": [[49, "plan-for-week-47"]], "Plans for the lab sessions": [[40, "plans-for-the-lab-sessions"]], "Plans for week 35": [[37, "plans-for-week-35"]], "Plans for week 36": [[38, "plans-for-week-36"]], "Plans for week 37, lab sessions": [[39, "plans-for-week-37-lab-sessions"]], "Plans for week 37, lecture Monday": [[39, "plans-for-week-37-lecture-monday"]], "Plans for week 38, lecture Monday September 16": [[40, "plans-for-week-38-lecture-monday-september-16"]], "Plans for week 40": [[42, "plans-for-week-40"]], "Plans for week 43": [[45, "plans-for-week-43"]], "Plans for week 45": [[47, "plans-for-week-45"]], "Plotting the Histogram": [[39, "plotting-the-histogram"]], "Plotting the mean value for each group": [[40, "plotting-the-mean-value-for-each-group"]], "Pooling": [[46, "pooling"]], "Pooling Layer": [[46, "pooling-layer"], [47, "pooling-layer"]], "Pooling arithmetic": [[46, "pooling-arithmetic"]], "Pooling types (From Raschka et al)": [[46, "pooling-types-from-raschka-et-al"]], "Practical tips": [[14, "practical-tips"], [22, "practical-tips"], [41, "practical-tips"], [42, "practical-tips"]], "Practicalities": [[34, "practicalities"]], "Preamble: Note on writing reports, using reference material, AI and other tools": [[29, "preamble-note-on-writing-reports-using-reference-material-ai-and-other-tools"]], "Predicting New Points With A Trained Recurrent Neural Network": [[5, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Preparing Your Data": [[50, "preparing-your-data"]], "Preprocessing our data": [[37, "preprocessing-our-data"]], "Prerequisites": [[36, "prerequisites"]], "Prerequisites and background": [[27, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[4, "prerequisites-collect-and-pre-process-data"], [46, "prerequisites-collect-and-pre-process-data"], [47, "prerequisites-collect-and-pre-process-data"]], "Printing out as text": [[48, "printing-out-as-text"], [49, "printing-out-as-text"]], "Probability Distribution Functions": [[33, "probability-distribution-functions"]], "Program example for gradient descent with Ridge Regression": [[40, "program-example-for-gradient-descent-with-ridge-regression"], [41, "program-example-for-gradient-descent-with-ridge-regression"]], "Program for stochastic gradient": [[14, "program-for-stochastic-gradient"]], "Project 1 on Machine Learning, deadline October 7 (midnight), 2024": [[29, null]], "Project 2 on Machine Learning, deadline November 4 (Midnight)": [[30, null]], "Project 3 on Machine Learning, deadline December 9 (midnight), 2024": [[31, null]], "Project based teaching and active learning": [[26, "project-based-teaching-and-active-learning"]], "Properties of PDFs": [[33, "properties-of-pdfs"]], "Pros and cons of trees, pros": [[10, "pros-and-cons-of-trees-pros"], [48, "pros-and-cons-of-trees-pros"], [49, "pros-and-cons-of-trees-pros"]], "Pruning the tree": [[48, "pruning-the-tree"], [49, "pruning-the-tree"]], "Python installers": [[27, "python-installers"], [36, "python-installers"]], "Quantum deep learning": [[50, "quantum-deep-learning"]], "Quantum machine learning": [[50, "quantum-machine-learning"]], "Quantum machine learning algorithms based on linear algebra": [[50, "quantum-machine-learning-algorithms-based-on-linear-algebra"]], "Quantum reinforcement learning": [[50, "quantum-reinforcement-learning"]], "RMS prop": [[14, "rms-prop"], [41, "rms-prop"], [42, "rms-prop"]], "RMSProp algorithm, taken from Goodfellow et al": [[22, "rmsprop-algorithm-taken-from-goodfellow-et-al"], [42, "rmsprop-algorithm-taken-from-goodfellow-et-al"]], "RMSprop for adaptive learning rate with Stochastic Gradient Descent": [[22, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"], [41, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"], [42, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"]], "RNNs": [[47, "rnns"]], "RNNs in more detail": [[47, "rnns-in-more-detail"]], "RNNs in more detail, part 2": [[47, "rnns-in-more-detail-part-2"]], "RNNs in more detail, part 3": [[47, "rnns-in-more-detail-part-3"]], "RNNs in more detail, part 4": [[47, "rnns-in-more-detail-part-4"]], "RNNs in more detail, part 5": [[47, "rnns-in-more-detail-part-5"]], "RNNs in more detail, part 6": [[47, "rnns-in-more-detail-part-6"]], "RNNs in more detail, part 7": [[47, "rnns-in-more-detail-part-7"]], "Random Forest Algorithm": [[48, "random-forest-algorithm"], [49, "random-forest-algorithm"]], "Random Forest Algorithm, reminder from last week": [[50, "random-forest-algorithm-reminder-from-last-week"]], "Random Forests Compared with other Methods on the Cancer Data": [[48, "random-forests-compared-with-other-methods-on-the-cancer-data"], [49, "random-forests-compared-with-other-methods-on-the-cancer-data"], [50, "random-forests-compared-with-other-methods-on-the-cancer-data"]], "Random Numbers": [[33, "random-numbers"]], "Random forests": [[11, "random-forests"], [48, "random-forests"], [49, "random-forests"]], "Randomized Grid Search": [[40, "randomized-grid-search"]], "Randomized PCA": [[12, "randomized-pca"]], "Reading material": [[36, "reading-material"]], "Reading recommendations": [[43, "reading-recommendations"], [44, "reading-recommendations"], [45, "reading-recommendations"]], "Reading recommendations:": [[37, "reading-recommendations"]], "Reading suggestions week 34": [[36, "reading-suggestions-week-34"]], "Recurrent neural networks": [[13, "recurrent-neural-networks"], [42, "recurrent-neural-networks"], [43, "recurrent-neural-networks"]], "Recurrent neural networks (RNNs): Overarching view": [[47, "recurrent-neural-networks-rnns-overarching-view"]], "Recurrent neural networks: Overarching view": [[5, null]], "Reducing the number of degrees of freedom, overarching view": [[1, "reducing-the-number-of-degrees-of-freedom-overarching-view"], [37, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reducing the number of operations": [[43, "reducing-the-number-of-operations"]], "Reformulating the problem": [[3, "reformulating-the-problem"], [45, "reformulating-the-problem"]], "Regression Case": [[11, "regression-case"], [49, "regression-case"]], "Regression analysis and resampling methods": [[29, "regression-analysis-and-resampling-methods"]], "Regression analysis, overarching aims": [[36, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[36, "regression-analysis-overarching-aims-ii"]], "Regression trees": [[48, "regression-trees"], [49, "regression-trees"]], "Regular NNs don\u2019t scale well to full images": [[46, "regular-nns-dont-scale-well-to-full-images"], [47, "regular-nns-dont-scale-well-to-full-images"]], "Regularization": [[2, "regularization"], [44, "regularization"], [45, "regularization"]], "Reinforcement Learning": [[50, "reinforcement-learning"]], "Relevance": [[42, "relevance"], [43, "relevance"], [44, "relevance"]], "Remarks on the speed": [[46, "remarks-on-the-speed"], [47, "remarks-on-the-speed"]], "Reminder on Statistics": [[7, "reminder-on-statistics"]], "Reminder on books with hands-on material and codes": [[43, "reminder-on-books-with-hands-on-material-and-codes"], [44, "reminder-on-books-with-hands-on-material-and-codes"], [45, "reminder-on-books-with-hands-on-material-and-codes"]], "Reminder on the chain rule and gradients": [[43, "reminder-on-the-chain-rule-and-gradients"]], "Replace or not": [[14, "replace-or-not"], [41, "replace-or-not"], [42, "replace-or-not"]], "Required Technologies": [[27, "required-technologies"]], "Resampling": [[50, "resampling"]], "Resampling Methods": [[7, null]], "Resampling approaches can be computationally expensive": [[39, "resampling-approaches-can-be-computationally-expensive"]], "Resampling methods": [[7, "id1"], [39, "resampling-methods"], [39, "id2"]], "Resampling methods: Bootstrap": [[39, "resampling-methods-bootstrap"]], "Resampling methods: Bootstrap approach": [[39, "resampling-methods-bootstrap-approach"]], "Resampling methods: Bootstrap background": [[39, "resampling-methods-bootstrap-background"]], "Resampling methods: Bootstrap steps": [[39, "resampling-methods-bootstrap-steps"]], "Resampling methods: More Bootstrap background": [[39, "resampling-methods-more-bootstrap-background"]], "Residual Error": [[37, "residual-error"]], "Resources on differential equations and deep learning": [[3, "resources-on-differential-equations-and-deep-learning"], [45, "resources-on-differential-equations-and-deep-learning"]], "Revisiting our Linear Regression Solvers": [[14, "revisiting-our-linear-regression-solvers"]], "Revisiting our Logistic Regression case": [[40, "revisiting-our-logistic-regression-case"], [41, "revisiting-our-logistic-regression-case"]], "Revisiting our first homework": [[40, "revisiting-our-first-homework"], [41, "revisiting-our-first-homework"]], "Rewriting as dot products": [[46, "rewriting-as-dot-products"]], "Rewriting the Covariance and/or Correlation Matrix": [[37, "rewriting-the-covariance-and-or-correlation-matrix"]], "Rewriting the \\delta-function": [[39, "rewriting-the-delta-function"]], "Rewriting the fitting procedure as a linear algebra problem": [[36, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[36, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge Regression": [[38, "ridge-regression"]], "Ridge and Bayes": [[38, "ridge-and-bayes"], [39, "ridge-and-bayes"]], "Ridge and LASSO Regression": [[37, "ridge-and-lasso-regression"], [38, "ridge-and-lasso-regression"]], "Ridge and Lasso Regression": [[6, null], [6, "id1"]], "Running with Keras": [[46, "running-with-keras"], [47, "running-with-keras"]], "SGD example": [[41, "sgd-example"], [42, "sgd-example"]], "SVD analysis": [[38, "svd-analysis"]], "Same code but now with momentum gradient descent": [[14, "same-code-but-now-with-momentum-gradient-descent"], [22, "same-code-but-now-with-momentum-gradient-descent"], [22, "id1"], [41, "same-code-but-now-with-momentum-gradient-descent"], [41, "id9"], [41, "id10"], [42, "same-code-but-now-with-momentum-gradient-descent"], [42, "id1"]], "Schedule first week": [[36, "schedule-first-week"]], "Schedulers": [[46, "schedulers"], [47, "schedulers"]], "Schematic Regression Procedure": [[10, "schematic-regression-procedure"], [48, "schematic-regression-procedure"], [49, "schematic-regression-procedure"]], "Searching for Optimal Regularization Parameters \\lambda": [[40, "searching-for-optimal-regularization-parameters-lambda"]], "Second moment of the gradient": [[41, "second-moment-of-the-gradient"], [42, "second-moment-of-the-gradient"]], "Sequential data only?": [[47, "sequential-data-only"]], "Set up the model": [[46, "set-up-the-model"], [47, "set-up-the-model"]], "Setting it up": [[46, "setting-it-up"], [47, "setting-it-up"]], "Setting up a Multi-layer perceptron model for classification": [[44, "setting-up-a-multi-layer-perceptron-model-for-classification"], [45, "setting-up-a-multi-layer-perceptron-model-for-classification"]], "Setting up the Back propagation algorithm": [[13, "setting-up-the-back-propagation-algorithm"]], "Setting up the Back propagation algorithm, part 3": [[43, "setting-up-the-back-propagation-algorithm-part-3"], [44, "setting-up-the-back-propagation-algorithm-part-3"], [45, "setting-up-the-back-propagation-algorithm-part-3"]], "Setting up the Matrix to be inverted": [[37, "setting-up-the-matrix-to-be-inverted"]], "Setting up the back propagation algorithm": [[43, "setting-up-the-back-propagation-algorithm"]], "Setting up the back propagation algorithm and algorithm for a feed forward NN, initalizations": [[44, "setting-up-the-back-propagation-algorithm-and-algorithm-for-a-feed-forward-nn-initalizations"], [45, "setting-up-the-back-propagation-algorithm-and-algorithm-for-a-feed-forward-nn-initalizations"]], "Setting up the back propagation algorithm, part 1": [[44, "setting-up-the-back-propagation-algorithm-part-1"], [45, "setting-up-the-back-propagation-algorithm-part-1"]], "Setting up the back propagation algorithm, part 2": [[43, "setting-up-the-back-propagation-algorithm-part-2"], [44, "setting-up-the-back-propagation-algorithm-part-2"], [45, "setting-up-the-back-propagation-algorithm-part-2"]], "Setting up the code": [[45, "setting-up-the-code"]], "Setting up the equations for a neural network": [[43, "setting-up-the-equations-for-a-neural-network"], [44, "setting-up-the-equations-for-a-neural-network"]], "Setting up the network using Autograd": [[45, "setting-up-the-network-using-autograd"]], "Setting up the network using Autograd; The full program": [[3, "setting-up-the-network-using-autograd-the-full-program"], [45, "setting-up-the-network-using-autograd-the-full-program"]], "Setting up the network using Autograd; The trial solution": [[45, "setting-up-the-network-using-autograd-the-trial-solution"]], "Setting up the problem": [[45, "setting-up-the-problem"]], "Setup of Network": [[45, "setup-of-network"]], "Similar (second order function now) problem but now with AdaGrad": [[14, "similar-second-order-function-now-problem-but-now-with-adagrad"], [22, "similar-second-order-function-now-problem-but-now-with-adagrad"], [41, "similar-second-order-function-now-problem-but-now-with-adagrad"], [42, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[10, "simple-python-code-to-read-in-data-and-perform-classification"], [48, "simple-python-code-to-read-in-data-and-perform-classification"], [49, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple Voting Example, head or tail": [[48, "simple-voting-example-head-or-tail"], [49, "simple-voting-example-head-or-tail"]], "Simple case": [[37, "simple-case"]], "Simple code for solving the above problem": [[38, "simple-code-for-solving-the-above-problem"]], "Simple example": [[40, "simple-example"], [43, "simple-example"]], "Simple example code": [[41, "simple-example-code"], [42, "simple-example-code"]], "Simple example to illustrate Ordinary Least Squares, Ridge and Lasso Regression": [[38, "simple-example-to-illustrate-ordinary-least-squares-ridge-and-lasso-regression"]], "Simple geometric interpretation": [[40, "simple-geometric-interpretation"], [41, "simple-geometric-interpretation"]], "Simple implementation of GD for OLS, Ridge and Lasso": [[42, "simple-implementation-of-gd-for-ols-ridge-and-lasso"]], "Simple linear regression model using scikit-learn": [[1, "simple-linear-regression-model-using-scikit-learn"], [36, "simple-linear-regression-model-using-scikit-learn"]], "Simple neural network and the back propagation equations": [[43, "simple-neural-network-and-the-back-propagation-equations"], [44, "simple-neural-network-and-the-back-propagation-equations"]], "Simple program": [[40, "simple-program"], [41, "simple-program"]], "Simpler examples first, and automatic differentiation": [[43, "simpler-examples-first-and-automatic-differentiation"]], "Slightly different approach": [[41, "slightly-different-approach"], [42, "slightly-different-approach"]], "Smarter way of evaluating the above function": [[43, "smarter-way-of-evaluating-the-above-function"]], "Social machine learning": [[50, "social-machine-learning"]], "Software and needed installations": [[29, "software-and-needed-installations"], [31, "software-and-needed-installations"], [36, "software-and-needed-installations"]], "Solving Differential Equations with Deep Learning": [[3, null]], "Solving differential equations with Deep Learning": [[45, "solving-differential-equations-with-deep-learning"]], "Solving differential equations with RNNs": [[47, "solving-differential-equations-with-rnns"]], "Solving partial differential equations with neural networks": [[31, "solving-partial-differential-equations-with-neural-networks"]], "Solving the equation using Autograd": [[45, "solving-the-equation-using-autograd"]], "Solving the one dimensional Poisson equation": [[3, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation - the full program using Autograd": [[45, "solving-the-wave-equation-the-full-program-using-autograd"]], "Solving the wave equation with Neural Networks": [[3, "solving-the-wave-equation-with-neural-networks"]], "Solving using Newton-Raphson\u2019s method": [[40, "solving-using-newton-raphson-s-method"], [41, "solving-using-newton-raphson-s-method"]], "Some famous Matrices": [[28, "some-famous-matrices"]], "Some parallels from real analysis": [[43, "some-parallels-from-real-analysis"]], "Some selected properties": [[40, "some-selected-properties"]], "Some similarities and differences from DNNs": [[50, "some-similarities-and-differences-from-dnns"]], "Some simple problems": [[14, "some-simple-problems"], [40, "some-simple-problems"], [41, "some-simple-problems"]], "Some useful matrix and vector expressions": [[37, "some-useful-matrix-and-vector-expressions"]], "Splitting our Data in Training and Test data": [[1, "splitting-our-data-in-training-and-test-data"], [36, "splitting-our-data-in-training-and-test-data"], [37, "splitting-our-data-in-training-and-test-data"]], "Squared-Error Example and Iterative Fitting": [[48, "squared-error-example-and-iterative-fitting"], [49, "squared-error-example-and-iterative-fitting"], [50, "squared-error-example-and-iterative-fitting"]], "Standard Approach based on the Normal Distribution": [[39, "standard-approach-based-on-the-normal-distribution"]], "Standard imports first": [[48, "standard-imports-first"], [49, "standard-imports-first"]], "Standard steepest descent": [[14, "standard-steepest-descent"], [41, "standard-steepest-descent"]], "Starting your Machine Learning Project": [[50, "starting-your-machine-learning-project"]], "Statistical analysis": [[39, "statistical-analysis"]], "Statistical analysis and optimization of data": [[27, "statistical-analysis-and-optimization-of-data"], [36, "statistical-analysis-and-optimization-of-data"], [50, "statistical-analysis-and-optimization-of-data"]], "Steepest Descent Example": [[49, "steepest-descent-example"], [50, "steepest-descent-example"]], "Steepest descent": [[14, "steepest-descent"], [40, "steepest-descent"], [41, "steepest-descent"]], "Steepest descent method": [[41, "steepest-descent-method"], [41, "id1"]], "Steepest descent example": [[41, "steepest-descent-example"]], "Still thinking": [[37, "still-thinking"]], "Stochastic Gradient Descent": [[41, "stochastic-gradient-descent"], [42, "stochastic-gradient-descent"]], "Stochastic Gradient Descent (SGD)": [[14, "stochastic-gradient-descent-sgd"], [41, "stochastic-gradient-descent-sgd"], [42, "stochastic-gradient-descent-sgd"]], "Stochastic variables and the main concepts, the discrete case": [[33, "stochastic-variables-and-the-main-concepts-the-discrete-case"]], "Strong correlations": [[46, "strong-correlations"], [47, "strong-correlations"]], "Suggested reading and videos": [[40, "suggested-reading-and-videos"]], "Suggested readings and videos": [[42, "suggested-readings-and-videos"]], "Summarizing: Performing a general discrete convolution (From Raschka et al)": [[46, "summarizing-performing-a-general-discrete-convolution-from-raschka-et-al"]], "Summary from last week, using gradient descent methods, limitations": [[42, "summary-from-last-week-using-gradient-descent-methods-limitations"]], "Summary of RNNs": [[47, "summary-of-rnns"]], "Summary of a typical RNN": [[47, "summary-of-a-typical-rnn"]], "Summary of course": [[50, "summary-of-course"]], "Summing up": [[39, "summing-up"]], "Support Vector Machines, overarching aims": [[9, null]], "Systematic reduction": [[4, "systematic-reduction"], [46, "systematic-reduction"], [47, "systematic-reduction"]], "Teachers": [[36, "teachers"]], "Teachers and Grading": [[34, null]], "Teaching Assistants Fall semester 2023": [[34, "teaching-assistants-fall-semester-2023"]], "Teaching schedule with links to material": [[32, null]], "Technicalities": [[45, "technicalities"]], "Tensorflow": [[44, "tensorflow"], [45, "tensorflow"]], "Test Function for what happens with OLS, Ridge and Lasso": [[38, "test-function-for-what-happens-with-ols-ridge-and-lasso"]], "Testing the Means Squared Error as function of Complexity": [[1, "testing-the-means-squared-error-as-function-of-complexity"], [37, "testing-the-means-squared-error-as-function-of-complexity"]], "Testing the XOR gate and other gates": [[44, "testing-the-xor-gate-and-other-gates"], [45, "testing-the-xor-gate-and-other-gates"]], "Textbooks": [[35, null]], "The back propagation equations for a neural network": [[44, "the-back-propagation-equations-for-a-neural-network"]], "The Algorithm before theorem": [[12, "the-algorithm-before-theorem"]], "The Boston housing data example": [[1, "the-boston-housing-data-example"]], "The Breast Cancer Data, now with Keras": [[2, "the-breast-cancer-data-now-with-keras"], [44, "the-breast-cancer-data-now-with-keras"], [45, "the-breast-cancer-data-now-with-keras"]], "The CART algorithm for Classification": [[10, "the-cart-algorithm-for-classification"], [48, "the-cart-algorithm-for-classification"], [49, "the-cart-algorithm-for-classification"]], "The CART algorithm for Regression": [[10, "the-cart-algorithm-for-regression"], [48, "the-cart-algorithm-for-regression"], [49, "the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[4, "the-cifar01-data-set"], [46, "the-cifar01-data-set"], [47, "the-cifar01-data-set"]], "The Central Limit Theorem": [[39, "the-central-limit-theorem"]], "The Challenges Facing Machine Learning": [[50, "the-challenges-facing-machine-learning"]], "The Convolutional Neural Network (CNN)": [[46, "the-convolutional-neural-network-cnn"], [47, "the-convolutional-neural-network-cnn"]], "The Hessian matrix": [[40, "the-hessian-matrix"], [41, "the-hessian-matrix"]], "The Hessian matrix for Ridge Regression": [[40, "the-hessian-matrix-for-ridge-regression"], [41, "the-hessian-matrix-for-ridge-regression"]], "The Jacobian": [[37, "the-jacobian"]], "The MNIST dataset again": [[4, "the-mnist-dataset-again"], [46, "the-mnist-dataset-again"], [47, "the-mnist-dataset-again"]], "The Neural Network": [[44, "the-neural-network"], [45, "the-neural-network"]], "The OLS case": [[38, "the-ols-case"]], "The RELU function family": [[2, "the-relu-function-family"], [43, "the-relu-function-family"], [44, "the-relu-function-family"], [45, "the-relu-function-family"]], "The Ridge case": [[38, "the-ridge-case"]], "The SVD example": [[46, "the-svd-example"]], "The SVD, a Fantastic Algorithm": [[37, "the-svd-a-fantastic-algorithm"]], "The Softmax function": [[2, "the-softmax-function"], [44, "the-softmax-function"], [45, "the-softmax-function"]], "The Squared-Error again! Steepest Descent": [[49, "the-squared-error-again-steepest-descent"], [50, "the-squared-error-again-steepest-descent"]], "The Table": [[48, "the-table"], [49, "the-table"]], "The \\chi^2 function": [[1, "the-chi-2-function"], [36, "the-chi-2-function"], [36, "id4"], [36, "id5"], [36, "id6"], [36, "id7"], [36, "id8"]], "The analytical solution": [[45, "the-analytical-solution"]], "The approximation theorem in words": [[43, "the-approximation-theorem-in-words"]], "The backward pass is linear": [[47, "the-backward-pass-is-linear"]], "The basic structure of your project": [[31, "the-basic-structure-of-your-project"]], "The bias-variance tradeoff": [[7, "the-bias-variance-tradeoff"], [39, "the-bias-variance-tradeoff"]], "The code": [[36, "the-code"]], "The code for solving the ODE": [[3, "the-code-for-solving-the-ode"], [45, "the-code-for-solving-the-ode"]], "The complete code with a simple data set": [[37, "the-complete-code-with-a-simple-data-set"]], "The convolution stage": [[46, "the-convolution-stage"]], "The cost function": [[0, "the-cost-function"]], "The cost function rewritten": [[40, "the-cost-function-rewritten"]], "The cost/loss function": [[37, "the-cost-loss-function"]], "The course has two central parts": [[27, "the-course-has-two-central-parts"]], "The derivative of the Logistic funtion": [[43, "the-derivative-of-the-logistic-funtion"], [44, "the-derivative-of-the-logistic-funtion"]], "The derivative of the cost/loss function": [[40, "the-derivative-of-the-cost-loss-function"], [41, "the-derivative-of-the-cost-loss-function"]], "The derivatives": [[43, "the-derivatives"], [44, "the-derivatives"]], "The equations": [[40, "the-equations"], [41, "the-equations"]], "The equations for ordinary least squares": [[37, "the-equations-for-ordinary-least-squares"]], "The equations to solve": [[40, "the-equations-to-solve"], [41, "the-equations-to-solve"]], "The first Case": [[38, "the-first-case"]], "The forget gate": [[47, "the-forget-gate"]], "The function to solve for": [[45, "the-function-to-solve-for"]], "The gradient step": [[41, "the-gradient-step"], [42, "the-gradient-step"]], "The ideal": [[40, "the-ideal"], [41, "the-ideal"]], "The last words?": [[50, "the-last-words"]], "The logistic function": [[0, "the-logistic-function"], [8, "the-logistic-function"], [40, "the-logistic-function"]], "The mean squared error and its derivative": [[37, "the-mean-squared-error-and-its-derivative"]], "The moons example": [[9, "the-moons-example"]], "The multilayer perceptron (MLP)": [[13, "the-multilayer-perceptron-mlp"]], "The network": [[50, "the-network"]], "The network with one input layer, specified number of hidden layers, and one output layer": [[3, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"], [45, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"]], "The optimization problem": [[43, "the-optimization-problem"]], "The ouput layer": [[43, "the-ouput-layer"], [44, "the-ouput-layer"]], "The problem of exploding or vanishing gradients": [[47, "the-problem-of-exploding-or-vanishing-gradients"]], "The problem to solve for": [[45, "the-problem-to-solve-for"]], "The program using Autograd": [[45, "the-program-using-autograd"]], "The same example but now with cross-validation": [[39, "the-same-example-but-now-with-cross-validation"]], "The sensitiveness of the gradient descent": [[40, "the-sensitiveness-of-the-gradient-descent"], [41, "the-sensitiveness-of-the-gradient-descent"]], "The singular value decomposition": [[6, "the-singular-value-decomposition"], [37, "the-singular-value-decomposition"]], "The specific equation to solve for": [[45, "the-specific-equation-to-solve-for"]], "The structure of the RBM network": [[50, "the-structure-of-the-rbm-network"]], "The syntax a.dot(b) when finding the dot product": [[41, "the-syntax-a-dot-b-when-finding-the-dot-product"]], "The training": [[43, "the-training"], [44, "the-training"]], "The trial solution": [[45, "the-trial-solution"], [45, "id4"], [45, "id5"], [45, "id7"]], "The two-dimensional case": [[9, "the-two-dimensional-case"]], "Then some basic questions": [[26, "then-some-basic-questions"]], "Time decay rate": [[41, "time-decay-rate"], [42, "time-decay-rate"]], "To our real data: nuclear binding energies. Brief reminder on masses and binding energies": [[36, "to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies"]], "To think about, first part": [[37, "to-think-about-first-part"]], "Toeplitz matrices": [[46, "toeplitz-matrices"]], "Topics covered in this course: Statistical analysis and optimization of data": [[36, "topics-covered-in-this-course-statistical-analysis-and-optimization-of-data"]], "Topics we have covered this year": [[50, "topics-we-have-covered-this-year"]], "Tossing coins": [[48, "tossing-coins"], [49, "tossing-coins"]], "Towards the PCA theorem": [[12, "towards-the-pca-theorem"]], "Train and test datasets": [[2, "train-and-test-datasets"], [44, "train-and-test-datasets"], [45, "train-and-test-datasets"]], "Transfer learning": [[50, "transfer-learning"]], "Transforming images": [[46, "transforming-images"]], "Two first-order differential equations": [[47, "two-first-order-differential-equations"]], "Two parameters": [[0, "two-parameters"], [40, "two-parameters"]], "Two-dimensional Objects": [[4, "two-dimensional-objects"]], "Two-dimensional objects": [[46, "two-dimensional-objects"]], "Type of problem": [[3, "type-of-problem"], [45, "type-of-problem"]], "Types of Machine Learning": [[36, "types-of-machine-learning"]], "Types of Machine Learning, a repetition": [[50, "types-of-machine-learning-a-repetition"]], "Understanding what happens": [[39, "understanding-what-happens"]], "Universal approximation theorem": [[43, "universal-approximation-theorem"]], "Unsupported functions": [[41, "unsupported-functions"]], "Updating the gradients": [[43, "updating-the-gradients"], [44, "updating-the-gradients"], [45, "updating-the-gradients"]], "Usage of CNN code": [[46, "usage-of-cnn-code"], [47, "usage-of-cnn-code"]], "Usage of activation functions": [[46, "usage-of-activation-functions"], [47, "usage-of-activation-functions"]], "Usage of cost functions": [[46, "usage-of-cost-functions"], [47, "usage-of-cost-functions"]], "Usage of schedulers": [[46, "usage-of-schedulers"], [47, "usage-of-schedulers"]], "Usage of the above learning rate schedulers": [[44, "usage-of-the-above-learning-rate-schedulers"], [45, "usage-of-the-above-learning-rate-schedulers"]], "Useful Python libraries": [[27, "useful-python-libraries"], [36, "useful-python-libraries"]], "Usefulness of the weekly exercises": [[26, "usefulness-of-the-weekly-exercises"]], "Using Autograd": [[14, "using-autograd"]], "Using Autograd with OLS": [[41, "using-autograd-with-ols"], [42, "using-autograd-with-ols"]], "Using Automatic differentation with OLS": [[22, "using-automatic-differentation-with-ols"]], "Using Automatic differentiation": [[45, "using-automatic-differentiation"]], "Using Keras": [[44, "using-keras"], [45, "using-keras"]], "Using autograd": [[41, "using-autograd"], [42, "using-autograd"]], "Using forward Euler to solve the ODE": [[3, "using-forward-euler-to-solve-the-ode"], [45, "using-forward-euler-to-solve-the-ode"]], "Using gradient descent methods, limitations": [[14, "using-gradient-descent-methods-limitations"], [40, "using-gradient-descent-methods-limitations"], [41, "using-gradient-descent-methods-limitations"]], "Using recursion": [[41, "using-recursion"], [42, "using-recursion"]], "Using the Voting Classifier": [[48, "using-the-voting-classifier"], [49, "using-the-voting-classifier"]], "Using the chain rule and summing over all k entries": [[43, "using-the-chain-rule-and-summing-over-all-k-entries"], [44, "using-the-chain-rule-and-summing-over-all-k-entries"]], "Using the correlation matrix": [[0, "using-the-correlation-matrix"], [40, "using-the-correlation-matrix"]], "Vanishing gradients": [[43, "vanishing-gradients"], [44, "vanishing-gradients"], [47, "vanishing-gradients"]], "Various steps in cross-validation": [[39, "various-steps-in-cross-validation"], [40, "various-steps-in-cross-validation"]], "Velocity only": [[47, "velocity-only"]], "Verifying the data set": [[46, "verifying-the-data-set"], [47, "verifying-the-data-set"]], "Visualization": [[2, "visualization"], [2, "id1"], [44, "visualization"], [44, "id1"], [45, "visualization"], [45, "id1"]], "Visualizing the Tree, Classification": [[10, "visualizing-the-tree-classification"], [48, "visualizing-the-tree-classification"], [49, "visualizing-the-tree-classification"]], "Visualizing the Tree, The Moons": [[48, "visualizing-the-tree-the-moons"], [49, "visualizing-the-tree-the-moons"]], "Voting and Bagging": [[48, "voting-and-bagging"], [49, "voting-and-bagging"]], "Was it easy to access the course material?": [[26, "was-it-easy-to-access-the-course-material"]], "Was the weekly update with plans etc useful?": [[26, "was-the-weekly-update-with-plans-etc-useful"]], "Week 34: Introduction to the course, Logistics and Practicalities": [[36, null]], "Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression": [[37, null]], "Week 36: Linear Regression and Statistical interpretations": [[38, null]], "Week 37: Statistical interpretations and Resampling Methods": [[39, null]], "Week 38: Logistic Regression and Optimization": [[40, null]], "Week 39: Optimization and Gradient Methods": [[41, null]], "Week 40: Gradient descent methods (continued) and start Neural networks": [[42, null]], "Week 41 Neural networks and constructing a neural network code": [[43, null]], "Week 42 Constructing a Neural Network code with examples": [[44, null]], "Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations": [[45, null]], "Week 44, Convolutional Neural Networks (CNN)": [[46, null]], "Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)": [[47, null]], "Week 46: Decision Trees, Ensemble methods and Random Forests": [[48, null]], "Week 47: From Decision Trees to Ensemble Methods, Random Forests and Boosting Methods": [[49, null]], "Week 48: Gradient boosting and summary of course": [[50, null]], "Weekly Schedule": [[32, "weekly-schedule"]], "Weights and biases": [[44, "weights-and-biases"], [45, "weights-and-biases"]], "What does centering (subtracting the mean values) mean mathematically?": [[37, "what-does-centering-subtracting-the-mean-values-mean-mathematically"]], "What does it mean?": [[37, "what-does-it-mean"], [38, "what-does-it-mean"]], "What is Machine Learning?": [[1, "what-is-machine-learning"]], "What is a good model?": [[1, "what-is-a-good-model"], [36, "what-is-a-good-model"]], "What is a good model? Can we define it?": [[36, "what-is-a-good-model-can-we-define-it"]], "What is boosting? Additive Modelling/Iterative Fitting": [[48, "what-is-boosting-additive-modelling-iterative-fitting"], [49, "what-is-boosting-additive-modelling-iterative-fitting"], [50, "what-is-boosting-additive-modelling-iterative-fitting"]], "What is the Difference": [[46, "what-is-the-difference"], [47, "what-is-the-difference"]], "What kinds of behaviour can RNNs exhibit?": [[47, "what-kinds-of-behaviour-can-rnns-exhibit"]], "What was your programming knowledge before you started?": [[26, "what-was-your-programming-knowledge-before-you-started"]], "What? Me worry? 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Linear Regression", "14. Building a Feed Forward Neural Network", "15. Solving Differential Equations with Deep Learning", "16. Convolutional Neural Networks", "17. Recurrent neural networks: Overarching view", "4. Ridge and Lasso Regression", "5. Resampling Methods", "6. Logistic Regression", "8. Support Vector Machines, overarching aims", "9. Decision trees, overarching aims", "10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods", "11. Basic ideas of the Principal Component Analysis (PCA)", "13. Neural networks", "7. Optimization, the central part of any Machine Learning algortithm", "12. Clustering and Unsupervised Learning", "Exercises week 34", "Exercises week 35", "Exercises week 36", "Exercises week 37", "Exercises week 38", "Exercises week 39", "Exercises week 41", "Exercises week 42", "Exercises week 43", "Exercise week 47", "Exercises week 48", "Applied Data Analysis and Machine Learning", "2. Linear Algebra, Handling of Arrays and more Python Features", "Project 1 on Machine Learning, deadline October 7 (midnight), 2024", "Project 2 on Machine Learning, deadline November 4 (Midnight)", "Project 3 on Machine Learning, deadline December 9 (midnight), 2024", "Teaching schedule with links to material", "1. Elements of Probability Theory and Statistical Data Analysis", "Teachers and Grading", "Textbooks", "Week 34: Introduction to the course, Logistics and Practicalities", "Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression", "Week 36: Linear Regression and Statistical interpretations", "Week 37: Statistical interpretations and Resampling Methods", "Week 38: Logistic Regression and Optimization", "Week 39: Optimization and Gradient Methods", "Week 40: Gradient descent methods (continued) and start Neural networks", "Week 41 Neural networks and constructing a neural network code", "Week 42 Constructing a Neural Network code with examples", "Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations", "Week 44, Convolutional Neural Networks (CNN)", "Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)", "Week 46: Decision Trees, Ensemble methods and Random Forests", "Week 47: From Decision Trees to Ensemble 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"a-deep-cnn-model-from-raschka-et-al"]], "A first summary": [[36, "a-first-summary"]], "A more advanced example": [[22, "a-more-advanced-example"], [42, "a-more-advanced-example"]], "A more compact expression": [[0, "a-more-compact-expression"], [40, "a-more-compact-expression"]], "A more efficient way of coding the above Convolution": [[46, "a-more-efficient-way-of-coding-the-above-convolution"]], "A new Cost Function": [[38, "a-new-cost-function"], [39, "a-new-cost-function"]], "A possible code using Scikit-Learn": [[48, "a-possible-code-using-scikit-learn"], [49, "a-possible-code-using-scikit-learn"]], "A possible implementation of a neural network": [[45, "a-possible-implementation-of-a-neural-network"]], "A quick Reminder on Lagrangian Multipliers": [[9, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[5, "a-simple-example"], [47, "a-simple-example"]], "A soft classifier": [[9, "a-soft-classifier"]], "A standard BM setup": [[50, "a-standard-bm-setup"]], "A 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"activation-functions"], [43, "activation-functions"], [44, "activation-functions"], [44, "id3"], [45, "activation-functions"], [45, "id3"], [46, "activation-functions"], [47, "activation-functions"]], "Activation functions, Logistic and Hyperbolic ones": [[42, "activation-functions-logistic-and-hyperbolic-ones"], [43, "activation-functions-logistic-and-hyperbolic-ones"], [44, "activation-functions-logistic-and-hyperbolic-ones"]], "Activation functions, examples": [[45, "activation-functions-examples"]], "AdaBoost Examples": [[48, "adaboost-examples"], [49, "adaboost-examples"], [50, "adaboost-examples"]], "AdaGrad algorithm, taken from Goodfellow et al": [[22, "adagrad-algorithm-taken-from-goodfellow-et-al"], [42, "adagrad-algorithm-taken-from-goodfellow-et-al"]], "Adaptive Boosting, AdaBoost": [[48, "adaptive-boosting-adaboost"], [49, "adaptive-boosting-adaboost"], [50, "adaptive-boosting-adaboost"]], "Adaptive boosting: AdaBoost, Basic Algorithm": [[11, "adaptive-boosting-adaboost-basic-algorithm"], [48, "adaptive-boosting-adaboost-basic-algorithm"], [49, "adaptive-boosting-adaboost-basic-algorithm"], [50, "adaptive-boosting-adaboost-basic-algorithm"]], "Add Dense layers on top": [[46, "add-dense-layers-on-top"], [47, "add-dense-layers-on-top"]], "Adding Neural Networks": [[42, "adding-neural-networks"], [43, "adding-neural-networks"]], "Adding a hidden layer": [[43, "adding-a-hidden-layer"], [44, "adding-a-hidden-layer"]], "Adding error analysis and training set up": [[36, "adding-error-analysis-and-training-set-up"], [37, "adding-error-analysis-and-training-set-up"]], "Additional Remarks": [[46, "additional-remarks"], [47, "additional-remarks"]], "Additional courses of interest": [[50, "additional-courses-of-interest"]], "Adjust hyperparameters": [[2, "adjust-hyperparameters"], [44, "adjust-hyperparameters"], [45, "adjust-hyperparameters"]], "Adversarial learning": [[50, "adversarial-learning"]], "Algorithms and codes for Adagrad, RMSprop and Adam": [[22, "algorithms-and-codes-for-adagrad-rmsprop-and-adam"], [41, "algorithms-and-codes-for-adagrad-rmsprop-and-adam"], [42, "algorithms-and-codes-for-adagrad-rmsprop-and-adam"]], "Algorithms for Setting up Decision Trees": [[10, "algorithms-for-setting-up-decision-trees"], [48, "algorithms-for-setting-up-decision-trees"], [49, "algorithms-for-setting-up-decision-trees"]], "Alternative differential equations": [[31, "alternative-differential-equations"]], "An Overview of Ensemble Methods": [[11, "an-overview-of-ensemble-methods"], [48, "an-overview-of-ensemble-methods"], [49, "an-overview-of-ensemble-methods"]], "An extrapolation example": [[5, "an-extrapolation-example"]], "An optimization/minimization problem": [[36, "an-optimization-minimization-problem"]], "Analyzing the last results": [[43, "analyzing-the-last-results"], [44, "analyzing-the-last-results"]], "And with loops": [[41, "and-with-loops"], [42, "and-with-loops"]], "And Logistic Regression": [[41, "and-logistic-regression"], [42, "and-logistic-regression"]], "And a corresponding example using scikit-learn": [[40, "and-a-corresponding-example-using-scikit-learn"], [41, "and-a-corresponding-example-using-scikit-learn"]], "And finally \\boldsymbol{X}\\boldsymbol{X}^T": [[37, "and-finally-boldsymbol-x-boldsymbol-x-t"], [38, "and-finally-boldsymbol-x-boldsymbol-x-t"]], "And finally ADAM": [[22, "and-finally-adam"], [41, "and-finally-adam"], [42, "and-finally-adam"]], "And what about using neural networks?": [[36, "and-what-about-using-neural-networks"]], "Another Example from Scikit-Learn\u2019s Repository": [[39, "another-example-from-scikit-learn-s-repository"]], "Another Example, now with a polynomial fit": [[38, "another-example-now-with-a-polynomial-fit"]], "Another example, the moons again": [[10, "another-example-the-moons-again"]], "Any other topics, impressions, ideas etc you would like to share with us?": [[26, "any-other-topics-impressions-ideas-etc-you-would-like-to-share-with-us"]], "Applied Data Analysis and Machine Learning": [[27, null]], "Artificial neurons": [[42, "artificial-neurons"], [43, "artificial-neurons"]], "Assumptions made": [[38, "assumptions-made"], [39, "assumptions-made"]], "Autocorrelation function": [[33, "autocorrelation-function"]], "Autoencoders: Overarching view": [[50, "autoencoders-overarching-view"]], "Autograd with more complicated functions": [[41, "autograd-with-more-complicated-functions"], [42, "autograd-with-more-complicated-functions"]], "Automatic differentiation": [[14, "automatic-differentiation"], [41, "automatic-differentiation"], [42, "automatic-differentiation"], [43, "automatic-differentiation"]], "Automatic differentiation through examples": [[43, "automatic-differentiation-through-examples"]], "Back propagation": [[45, "back-propagation"]], "Back propagation and automatic differentiation": [[45, "back-propagation-and-automatic-differentiation"]], "Back propagation in time in equations": [[47, "back-propagation-in-time-in-equations"]], "Back propagation in time through figures, part 1": [[47, "back-propagation-in-time-through-figures-part-1"]], "Back propagation in time, part 2": [[47, "back-propagation-in-time-part-2"]], "Back propagation in time, part 3": [[47, "back-propagation-in-time-part-3"]], "Back propagation in time, part 4": [[47, "back-propagation-in-time-part-4"]], "Back to the Cancer Data": [[12, "back-to-the-cancer-data"]], "Background literature": [[29, "background-literature"], [30, "background-literature"]], "Backpropagation in the convolutional layer": [[46, "backpropagation-in-the-convolutional-layer"], [47, "backpropagation-in-the-convolutional-layer"]], "Backpropagation through time": [[47, "backpropagation-through-time"]], "Bagging": [[11, "bagging"], [48, "bagging"], [49, "bagging"]], "Bagging Examples": [[11, "bagging-examples"]], "Basic Matrix Features": [[28, "basic-matrix-features"]], "Basic Steps of AdaBoost": [[48, "basic-steps-of-adaboost"], [49, "basic-steps-of-adaboost"], [50, "basic-steps-of-adaboost"]], "Basic ideas of the Principal Component Analysis (PCA)": [[12, null]], "Basic layout": [[47, "basic-layout"]], "Basic layout, Figures from Sebastian Rashcka et al, Machine learning with Sickit-Learn and PyTorch": [[47, "basic-layout-figures-from-sebastian-rashcka-et-al-machine-learning-with-sickit-learn-and-pytorch"]], "Basic math of the SVD": [[6, "basic-math-of-the-svd"], [37, "basic-math-of-the-svd"]], "Basics": [[8, "basics"], [40, "basics"]], "Basics of a tree": [[10, "basics-of-a-tree"], [48, "basics-of-a-tree"]], "Basics of an NN": [[43, "basics-of-an-nn"]], "Batch Normalization": [[2, "batch-normalization"], [43, "batch-normalization"], [44, "batch-normalization"]], "Batches and mini-batches": [[41, "batches-and-mini-batches"], [42, "batches-and-mini-batches"]], "Bayesian Machine Learning": [[50, "bayesian-machine-learning"]], "Bayes\u2019 Theorem": [[38, "bayes-theorem"], [39, "bayes-theorem"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[6, "bayes-theorem-and-ridge-and-lasso-regression"], [38, "bayes-theorem-and-ridge-and-lasso-regression"], [39, "bayes-theorem-and-ridge-and-lasso-regression"]], "Best wishes to you all and thanks so much for your heroic efforts this semester": [[50, "best-wishes-to-you-all-and-thanks-so-much-for-your-heroic-efforts-this-semester"]], "Boltzmann Machines": [[50, "boltzmann-machines"]], "Boltzmann machines (BM)": [[50, "boltzmann-machines-bm"]], "Boosting, a Bird\u2019s Eye View": [[11, "boosting-a-bird-s-eye-view"], [48, "boosting-a-bird-s-eye-view"], [49, "boosting-a-bird-s-eye-view"], [50, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[7, "bootstrap"]], "Brief reminder on Newton-Raphson\u2019s method": [[40, "brief-reminder-on-newton-raphson-s-method"], [41, "brief-reminder-on-newton-raphson-s-method"]], "Bringing it together": [[43, "bringing-it-together"], [44, "bringing-it-together"]], "Bringing it together, first back propagation equation": [[13, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[2, null]], "Building a neural network code": [[44, "building-a-neural-network-code"], [45, "building-a-neural-network-code"]], "Building a tree, regression": [[10, "building-a-tree-regression"], [48, "building-a-tree-regression"], [49, "building-a-tree-regression"]], "Building convolutional neural networks in Tensorflow and Keras": [[46, "building-convolutional-neural-networks-in-tensorflow-and-keras"], [47, "building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Building neural networks in Tensorflow and Keras": [[2, "building-neural-networks-in-tensorflow-and-keras"], [44, "building-neural-networks-in-tensorflow-and-keras"], [45, "building-neural-networks-in-tensorflow-and-keras"]], "Building our own CNN code": [[46, "building-our-own-cnn-code"], [47, "building-our-own-cnn-code"]], "Building up AdaBoost": [[48, "building-up-adaboost"], [49, "building-up-adaboost"], [50, "building-up-adaboost"]], "But noen of these can compete with Newton\u2019s method": [[22, "but-noen-of-these-can-compete-with-newton-s-method"]], "But none of these can compete with Newton\u2019s method": [[41, "but-none-of-these-can-compete-with-newton-s-method"], [42, "but-none-of-these-can-compete-with-newton-s-method"]], "CNNs in brief": [[46, "cnns-in-brief"], [47, "cnns-in-brief"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[4, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "CNNs in more detail, simple example": [[46, "cnns-in-more-detail-simple-example"]], "Cancer Data again now with Decision Trees and other Methods": [[10, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Chain rule": [[43, "chain-rule"]], "Chain rule again": [[47, "chain-rule-again"]], "Chain rule, forward and reverse modes": [[43, "chain-rule-forward-and-reverse-modes"]], "Challenge yourself the coming weekend": [[40, "challenge-yourself-the-coming-weekend"]], "Choose a Model and Algorithm": [[50, "choose-a-model-and-algorithm"]], "Choose cost function and optimizer": [[2, "choose-cost-function-and-optimizer"], [44, "choose-cost-function-and-optimizer"], [45, "choose-cost-function-and-optimizer"]], "Class of functions we can approximate": [[43, "class-of-functions-we-can-approximate"]], "Classical PCA Theorem": [[12, "classical-pca-theorem"]], "Classification and Regression, from linear and logistic regression to neural networks": [[30, "classification-and-regression-from-linear-and-logistic-regression-to-neural-networks"]], "Classification problems": [[40, "classification-problems"]], "Classification tree, how to split nodes": [[48, "classification-tree-how-to-split-nodes"], [49, "classification-tree-how-to-split-nodes"]], "Clustering and Unsupervised Learning": [[15, null]], "Code Example for Cross-validation and k-fold Cross-validation": [[39, "code-example-for-cross-validation-and-k-fold-cross-validation"], [40, "code-example-for-cross-validation-and-k-fold-cross-validation"]], "Code example": [[43, "code-example"], [44, "code-example"]], "Code example for the Bootstrap method": [[39, "code-example-for-the-bootstrap-method"]], "Code examples from week 39 and 40": [[22, "code-examples-from-week-39-and-40"]], "Code for SVD and Inversion of Matrices": [[6, "code-for-svd-and-inversion-of-matrices"], [38, "code-for-svd-and-inversion-of-matrices"]], "Code with a Number of Minibatches which varies": [[41, "code-with-a-number-of-minibatches-which-varies"], [42, "code-with-a-number-of-minibatches-which-varies"]], "Code with a Number of Minibatches which varies, analytical gradient": [[22, "code-with-a-number-of-minibatches-which-varies-analytical-gradient"]], "Codes and Approaches": [[15, "codes-and-approaches"]], "Codes for the SVD": [[6, "codes-for-the-svd"], [37, "codes-for-the-svd"]], "Collect and pre-process data": [[2, "collect-and-pre-process-data"], [44, "collect-and-pre-process-data"], [44, "id2"], [45, "collect-and-pre-process-data"], [45, "id2"]], "Communication channels": [[36, "communication-channels"]], "Commutative process": [[46, "commutative-process"]], "Compact expressions": [[43, "compact-expressions"], [44, "compact-expressions"]], "Compare Bagging on Trees with Random Forests": [[11, "compare-bagging-on-trees-with-random-forests"], [48, "compare-bagging-on-trees-with-random-forests"], [49, "compare-bagging-on-trees-with-random-forests"], [50, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[3, "comparing-with-a-numerical-scheme"], [45, "comparing-with-a-numerical-scheme"]], "Comparison with OLS": [[38, "comparison-with-ols"]], "Compile and train the model": [[46, "compile-and-train-the-model"], [47, "compile-and-train-the-model"]], "Completing the list": [[43, "completing-the-list"], [44, "completing-the-list"]], "Computation of gradients": [[41, "computation-of-gradients"], [42, "computation-of-gradients"]], "Computing a Tree using the Gini Index": [[48, "computing-a-tree-using-the-gini-index"], [49, "computing-a-tree-using-the-gini-index"]], "Computing the Gini Factor": [[48, "computing-the-gini-factor"], [49, "computing-the-gini-factor"]], "Computing the Gini index": [[10, "computing-the-gini-index"]], "Computing the various Gini Indices": [[48, "computing-the-various-gini-indices"], [49, "computing-the-various-gini-indices"]], "Computing the various Gini Indices, Hours slept": [[48, "computing-the-various-gini-indices-hours-slept"]], "Computing the various Gini Indices, Hours studied": [[48, "computing-the-various-gini-indices-hours-studied"]], "Conditional Probability": [[38, "conditional-probability"], [39, "conditional-probability"]], "Conditions on convex functions": [[40, "conditions-on-convex-functions"], [41, "conditions-on-convex-functions"]], "Confidence Intervals": [[39, "confidence-intervals"]], "Conjugate gradient method": [[14, "conjugate-gradient-method"], [41, "conjugate-gradient-method"], [41, "id2"], [41, "id3"], [41, "id4"], [41, "id5"], [41, "id6"], [41, "id7"]], "Conjugate gradient method and iterations": [[41, "conjugate-gradient-method-and-iterations"]], "Convex function": [[40, "convex-function"], [41, "convex-function"]], "Convex functions": [[14, "convex-functions"], [40, "convex-functions"], [41, "convex-functions"]], "Convolution": [[46, "convolution"], [47, "convolution"]], "Convolution Examples: Polynomial multiplication": [[4, "convolution-examples-polynomial-multiplication"], [46, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[4, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolution in the Fourier domain": [[46, "convolution-in-the-fourier-domain"], [47, "convolution-in-the-fourier-domain"]], "Convolution using separable kernels": [[46, "convolution-using-separable-kernels"], [47, "convolution-using-separable-kernels"]], "Convolution2DLayer: convolution in a hidden layer": [[46, "convolution2dlayer-convolution-in-a-hidden-layer"], [47, "convolution2dlayer-convolution-in-a-hidden-layer"]], "Convolutional Neural Network": [[13, "convolutional-neural-network"], [42, "convolutional-neural-network"], [43, "convolutional-neural-network"]], "Convolutional Neural Networks": [[4, null]], "Convolutional Neural Networks (recognizing images)": [[46, "convolutional-neural-networks-recognizing-images"], [47, "convolutional-neural-networks-recognizing-images"]], "Correlation Function and Design/Feature Matrix": [[37, "correlation-function-and-design-feature-matrix"]], "Correlation Matrix": [[12, "correlation-matrix"], [37, "correlation-matrix"]], "Correlation Matrix with Pandas": [[37, "correlation-matrix-with-pandas"]], "Correlation Matrix with Pandas and the Franke function": [[37, "correlation-matrix-with-pandas-and-the-franke-function"]], "Cost complexity pruning": [[48, "cost-complexity-pruning"], [49, "cost-complexity-pruning"]], "Cost functions": [[44, "cost-functions"], [45, "cost-functions"], [46, "cost-functions"], [47, "cost-functions"]], "Counting the number of floating point operations": [[43, "counting-the-number-of-floating-point-operations"]], "Course Format": [[36, "course-format"]], "Covariance Matrix Examples": [[37, "covariance-matrix-examples"]], "Covariance and Correlation Matrix": [[37, "covariance-and-correlation-matrix"]], "Cross correlation": [[46, "cross-correlation"]], "Cross-validation": [[7, "cross-validation"]], "Cross-validation in brief": [[39, "cross-validation-in-brief"], [40, "cross-validation-in-brief"]], "Deadlines for projects (tentative)": [[36, "deadlines-for-projects-tentative"]], "Decision trees and Regression": [[48, "decision-trees-and-regression"]], "Decision trees, overarching aims": [[10, null], [48, "decision-trees-overarching-aims"]], "Deep learning methods": [[36, "deep-learning-methods"]], "Define model and architecture": [[2, "define-model-and-architecture"], [44, "define-model-and-architecture"], [45, "define-model-and-architecture"]], "Defining different types of RBMs": [[50, "defining-different-types-of-rbms"]], "Defining intermediate operations": [[43, "defining-intermediate-operations"]], "Defining the cost function": [[2, "defining-the-cost-function"], [44, "defining-the-cost-function"], [45, "defining-the-cost-function"]], "Defining the data sets to analyze yourself": [[31, "defining-the-data-sets-to-analyze-yourself"]], "Defining the problem": [[45, "defining-the-problem"]], "Definitions": [[43, "definitions"], [44, "definitions"]], "Demonstration": [[46, "demonstration"], [47, "demonstration"]], "Derivative of the cost function": [[43, "derivative-of-the-cost-function"], [44, "derivative-of-the-cost-function"]], "Derivatives and the chain rule": [[13, "derivatives-and-the-chain-rule"], [43, "derivatives-and-the-chain-rule"], [44, "derivatives-and-the-chain-rule"]], "Derivatives in terms of z_j^L": [[43, "derivatives-in-terms-of-z-j-l"], [44, "derivatives-in-terms-of-z-j-l"]], "Derivatives of the hidden layer": [[43, "derivatives-of-the-hidden-layer"], [44, "derivatives-of-the-hidden-layer"]], "Derivatives, example 1": [[37, "derivatives-example-1"]], "Deriving OLS from a probability distribution": [[6, "deriving-ols-from-a-probability-distribution"], [38, "deriving-ols-from-a-probability-distribution"], [39, "deriving-ols-from-a-probability-distribution"]], "Deriving the Lasso Regression Equations": [[37, "deriving-the-lasso-regression-equations"], [38, "deriving-the-lasso-regression-equations"]], "Deriving the Ridge Regression Equations": [[37, "deriving-the-ridge-regression-equations"], [38, "deriving-the-ridge-regression-equations"]], "Deriving the back propagation code for a multilayer perceptron model": [[13, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Description of two-dimensional function": [[29, "description-of-two-dimensional-function"]], "Developing a code for doing neural networks with back propagation": [[2, "developing-a-code-for-doing-neural-networks-with-back-propagation"], [44, "developing-a-code-for-doing-neural-networks-with-back-propagation"], [45, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[12, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Did the projects and the teaching material allow you to deepen your insights about Machine Learning methods?": [[26, "did-the-projects-and-the-teaching-material-allow-you-to-deepen-your-insights-about-machine-learning-methods"]], "Different kernels and Mercer\u2019s theorem": [[9, "different-kernels-and-mercer-s-theorem"]], "Differential equations": [[47, "differential-equations"]], "Disadvantages": [[10, "disadvantages"], [48, "disadvantages"], [49, "disadvantages"]], "Discussing the correlation data": [[0, "discussing-the-correlation-data"], [40, "discussing-the-correlation-data"]], "Distributed machine learning": [[50, "distributed-machine-learning"]], "Does Logistic Regression do a better Job?": [[42, "does-logistic-regression-do-a-better-job"], [43, "does-logistic-regression-do-a-better-job"]], "Doing it correctly": [[38, "doing-it-correctly"], [39, "doing-it-correctly"]], "Domains and probabilities": [[33, "domains-and-probabilities"]], "Dropout": [[2, "dropout"], [43, "dropout"], [44, "dropout"]], "Dual learning": [[50, "dual-learning"]], "ELU function": [[43, "elu-function"], [44, "elu-function"], [45, "elu-function"]], "Economy-size SVD": [[37, "economy-size-svd"]], "Efficient Polynomial Multiplication": [[46, "efficient-polynomial-multiplication"]], "Elements of Probability Theory and Statistical Data Analysis": [[33, null]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[11, null], [48, "ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods"], [49, "ensemble-methods-from-a-single-tree-to-many-trees-and-extreme-boosting-meet-the-jungle-of-methods"]], "Entropy and the ID3 algorithm": [[10, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[36, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[2, "evaluate-model-performance-on-test-data"], [44, "evaluate-model-performance-on-test-data"], [45, "evaluate-model-performance-on-test-data"]], "Example 2": [[37, "example-2"]], "Example 3": [[37, "example-3"]], "Example 4": [[37, "example-4"]], "Example Matrix": [[37, "example-matrix"]], "Example code for Bias-Variance tradeoff": [[39, "example-code-for-bias-variance-tradeoff"]], "Example of Usage of Bayes\u2019 theorem": [[38, "example-of-usage-of-bayes-theorem"], [39, "example-of-usage-of-bayes-theorem"]], "Example of own Standard scaling": [[37, "example-of-own-standard-scaling"]], "Example relevant for the exercises": [[37, "example-relevant-for-the-exercises"]], "Example: Exponential decay": [[3, "example-exponential-decay"], [45, "example-exponential-decay"]], "Example: Population growth": [[3, "example-population-growth"], [45, "example-population-growth"]], "Example: Solving the one dimensional Poisson equation": [[45, "example-solving-the-one-dimensional-poisson-equation"]], "Example: Solving the wave equation with Neural Networks": [[45, "example-solving-the-wave-equation-with-neural-networks"]], "Example: The diffusion equation": [[3, "example-the-diffusion-equation"], [45, "example-the-diffusion-equation"]], "Example: binary classification problem": [[2, "example-binary-classification-problem"], [44, "example-binary-classification-problem"], [45, "example-binary-classification-problem"]], "Examples": [[36, "examples"]], "Examples of CNN setups": [[46, "examples-of-cnn-setups"]], "Examples of XOR, OR and AND gates": [[42, "examples-of-xor-or-and-and-gates"], [43, "examples-of-xor-or-and-and-gates"]], "Examples of likelihood functions used in logistic regression and neural networks": [[8, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Examples of likelihood functions used in logistic regression and nueral networks": [[0, "examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks"], [40, "examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks"]], "Exercise 1": [[23, "exercise-1"]], "Exercise 1 - Understand the feed forward pass": [[24, "exercise-1-understand-the-feed-forward-pass"]], "Exercise 1: Analytical exercises": [[17, "exercise-1-analytical-exercises"], [18, "exercise-1-analytical-exercises"]], "Exercise 1: Expectation values for ordinary least squares expressions": [[19, "exercise-1-expectation-values-for-ordinary-least-squares-expressions"]], "Exercise 1: Including more data": [[43, "exercise-1-including-more-data"]], "Exercise 1: Linear and logistic regression methods": [[25, "exercise-1-linear-and-logistic-regression-methods"]], "Exercise 1: Setting up various Python environments": [[1, "exercise-1-setting-up-various-python-environments"], [16, "exercise-1-setting-up-various-python-environments"], [36, "exercise-1-setting-up-various-python-environments"]], "Exercise 2": [[23, "exercise-2"]], "Exercise 2 - Gradient with one layer using autograd": [[24, "exercise-2-gradient-with-one-layer-using-autograd"]], "Exercise 2: Adding Ridge Regression": [[18, "exercise-2-adding-ridge-regression"]], "Exercise 2: Deep learning": [[25, "exercise-2-deep-learning"]], "Exercise 2: Expectation values for Ridge regression": [[19, "exercise-2-expectation-values-for-ridge-regression"]], "Exercise 2: Extended program": [[43, "exercise-2-extended-program"]], "Exercise 2: making your own data and exploring scikit-learn": [[1, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [16, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [17, "exercise-2-making-your-own-data-and-exploring-scikit-learn"], [36, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3": [[23, "exercise-3"]], "Exercise 3 - Gradient with one layer writing backpropagation by hand": [[24, "exercise-3-gradient-with-one-layer-writing-backpropagation-by-hand"]], "Exercise 3: Decision trees and ensemble methods": [[25, "exercise-3-decision-trees-and-ensemble-methods"]], "Exercise 3: Normalizing our data": [[1, "exercise-3-normalizing-our-data"]], "Exercise 3: Split data in test and training data": [[16, "exercise-3-split-data-in-test-and-training-data"], [17, "exercise-3-split-data-in-test-and-training-data"], [36, "exercise-3-split-data-in-test-and-training-data"]], "Exercise 4 - Custom activation for each layer": [[23, "exercise-4-custom-activation-for-each-layer"]], "Exercise 4 - Gradient with two layers writing backpropagation by hand": [[24, "exercise-4-gradient-with-two-layers-writing-backpropagation-by-hand"]], "Exercise 4: Adding Ridge Regression": [[1, "exercise-4-adding-ridge-regression"]], "Exercise 4: Optimization part": [[25, "exercise-4-optimization-part"]], "Exercise 5 - Gradient with any number of layers writing backpropagation by hand": [[24, "exercise-5-gradient-with-any-number-of-layers-writing-backpropagation-by-hand"]], "Exercise 5 - Processing multiple inputs at once": [[23, "exercise-5-processing-multiple-inputs-at-once"]], "Exercise 5: Analysis of results": [[25, "exercise-5-analysis-of-results"]], "Exercise 5: Analytical exercises": [[1, "exercise-5-analytical-exercises"]], "Exercise 6 - Batched inputs": [[24, "exercise-6-batched-inputs"]], "Exercise 6 - Predicting on real data": [[23, "exercise-6-predicting-on-real-data"]], "Exercise 7 - Training": [[24, "exercise-7-training"]], "Exercise 7 - Training on real data (Optional)": [[23, "exercise-7-training-on-real-data-optional"]], "Exercise 8 (Optional) - Object orientation": [[24, "exercise-8-optional-object-orientation"]], "Exercise week 47": [[25, null]], "Exercise: Cross-validation as resampling techniques, adding more complexity": [[7, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[7, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[7, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function with resampling": [[7, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[7, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function with resampling": [[7, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[1, "exercises"], [16, "exercises"], [36, "exercises"]], "Exercises Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40": [[0, null]], "Exercises and Projects": [[7, "exercises-and-projects"]], "Exercises and lab session week 43": [[45, "exercises-and-lab-session-week-43"]], "Exercises week 34": [[16, null]], "Exercises week 35": [[17, null]], "Exercises week 36": [[18, null]], "Exercises week 37": [[19, null]], "Exercises week 38": [[20, null]], "Exercises week 39": [[21, null]], "Exercises week 41": [[22, null]], "Exercises week 42": [[23, null]], "Exercises week 43": [[24, null]], "Exercises week 48": [[26, null]], "Expectation value and variance": [[38, "expectation-value-and-variance"], [39, "expectation-value-and-variance"]], "Expectation value and variance for \\boldsymbol{\\beta}": [[38, "expectation-value-and-variance-for-boldsymbol-beta"], [39, "expectation-value-and-variance-for-boldsymbol-beta"]], "Expectation values": [[33, "expectation-values"]], "Explainable machine learning": [[50, "explainable-machine-learning"]], "Explicit derivatives": [[43, "explicit-derivatives"], [44, "explicit-derivatives"]], "Exploding gradients": [[43, "exploding-gradients"], [44, "exploding-gradients"]], "Extending to more predictors": [[0, "extending-to-more-predictors"], [40, "extending-to-more-predictors"]], "Extending to more than one variable": [[40, "extending-to-more-than-one-variable"], [41, "extending-to-more-than-one-variable"]], "Extremely useful tools, strongly recommended": [[36, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[13, "feed-forward-neural-networks"], [42, "feed-forward-neural-networks"], [43, "feed-forward-neural-networks"]], "Feed-forward pass": [[2, "feed-forward-pass"], [44, "feed-forward-pass"], [45, "feed-forward-pass"]], "Feedback connections": [[47, "feedback-connections"]], "Final back propagating equation": [[13, "final-back-propagating-equation"], [43, "final-back-propagating-equation"], [44, "final-back-propagating-equation"]], "Final derivatives": [[43, "final-derivatives"]], "Final expression": [[43, "final-expression"], [44, "final-expression"]], "Final expressions": [[41, "final-expressions"]], "Final expressions for the biases of the hidden layer": [[43, "final-expressions-for-the-biases-of-the-hidden-layer"], [44, "final-expressions-for-the-biases-of-the-hidden-layer"]], "Final part": [[46, "final-part"], [47, "final-part"]], "Final regressor code": [[48, "final-regressor-code"], [49, "final-regressor-code"]], "Final technicalities I": [[45, "final-technicalities-i"]], "Final technicalities II": [[45, "final-technicalities-ii"]], "Final technicalities III": [[45, "final-technicalities-iii"]], "Final technicalities IV": [[45, "final-technicalities-iv"]], "Final visualization": [[46, "final-visualization"], [47, "final-visualization"]], "Finally, evaluate the model": [[46, "finally-evaluate-the-model"], [47, "finally-evaluate-the-model"]], "Finding the Limit": [[39, "finding-the-limit"]], "Finding the number of parameters": [[46, "finding-the-number-of-parameters"]], "Fine-tuning neural network hyperparameters": [[2, "fine-tuning-neural-network-hyperparameters"], [43, "fine-tuning-neural-network-hyperparameters"], [44, "fine-tuning-neural-network-hyperparameters"]], "First network example, simple percepetron with one input": [[43, "first-network-example-simple-percepetron-with-one-input"], [44, "first-network-example-simple-percepetron-with-one-input"]], "Fitting an Equation of State for Dense Nuclear Matter": [[1, "fitting-an-equation-of-state-for-dense-nuclear-matter"], [36, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "Fixing the singularity": [[37, "fixing-the-singularity"]], "Flattening Layer": [[46, "flattening-layer"], [47, "flattening-layer"]], "For exercise sessions: Why Linear Regression (aka Ordinary Least Squares and family), repeat from last week": [[37, "for-exercise-sessions-why-linear-regression-aka-ordinary-least-squares-and-family-repeat-from-last-week"]], "Forget and input": [[47, "forget-and-input"]], "Format for electronic delivery of report and programs": [[29, "format-for-electronic-delivery-of-report-and-programs"], [30, "format-for-electronic-delivery-of-report-and-programs"], [31, "format-for-electronic-delivery-of-report-and-programs"]], "Forward and reverse modes": [[43, "forward-and-reverse-modes"]], "Four effective ways to learn an RNN and preparing for next week": [[47, "four-effective-ways-to-learn-an-rnn-and-preparing-for-next-week"]], "Fourier series and Toeplitz matrices": [[46, "fourier-series-and-toeplitz-matrices"]], "Frequently used scaling functions": [[37, "frequently-used-scaling-functions"]], "From FFNNs and CNNs to recurrent neural networks (RNNs)": [[47, "from-ffnns-and-cnns-to-recurrent-neural-networks-rnns"]], "From OLS to Ridge and Lasso": [[38, "from-ols-to-ridge-and-lasso"]], "From one to many layers, the universal approximation theorem": [[13, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Full object-oriented implementation": [[44, "full-object-oriented-implementation"], [45, "full-object-oriented-implementation"]], "Fully Connected Layers": [[46, "fully-connected-layers"], [47, "fully-connected-layers"]], "Functionality in Scikit-Learn": [[37, "functionality-in-scikit-learn"]], "Functions using mathematical functions from Numpy": [[41, "functions-using-mathematical-functions-from-numpy"], [42, "functions-using-mathematical-functions-from-numpy"]], "Further Dimensionality Remarks": [[4, "further-dimensionality-remarks"]], "Further Manipulations": [[37, "further-manipulations"]], "Further example: Computing the Gini index": [[48, "further-example-computing-the-gini-index"], [49, "further-example-computing-the-gini-index"]], "Further properties (important for our analyses later)": [[6, "further-properties-important-for-our-analyses-later"], [37, "further-properties-important-for-our-analyses-later"]], "Further remarks": [[46, "further-remarks"]], "Further simplification": [[46, "further-simplification"]], "Gating mechanism: Long Short Term Memory (LSTM)": [[47, "gating-mechanism-long-short-term-memory-lstm"]], "Gaussian Elimination": [[28, "gaussian-elimination"]], "General Features": [[10, "general-features"], [48, "general-features"]], "General linear models and linear algebra": [[36, "general-linear-models-and-linear-algebra"]], "Generalizing the above one-dimensional case": [[46, "generalizing-the-above-one-dimensional-case"]], "Generalizing the fitting procedure as a linear algebra problem": [[36, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [36, "id1"]], "Generative Adversarial Networks": [[5, "generative-adversarial-networks"]], "Generative Models": [[5, "generative-models"]], "Geometric Interpretation and link with Singular Value Decomposition": [[12, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Getting serious, the back propagation equations for a neural network": [[43, "getting-serious-the-back-propagation-equations-for-a-neural-network"]], "Getting started with Jax, note the way we import numpy": [[22, "getting-started-with-jax-note-the-way-we-import-numpy"], [42, "getting-started-with-jax-note-the-way-we-import-numpy"]], "Goals": [[50, "goals"]], "Gradient Boosting, Classification Example": [[11, "gradient-boosting-classification-example"], [49, "gradient-boosting-classification-example"], [50, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[11, "gradient-boosting-examples-of-regression"], [49, "gradient-boosting-examples-of-regression"], [50, "gradient-boosting-examples-of-regression"]], "Gradient Boosting, algorithm": [[49, "gradient-boosting-algorithm"], [50, "gradient-boosting-algorithm"]], "Gradient Clipping": [[2, "gradient-clipping"], [43, "gradient-clipping"], [44, "gradient-clipping"]], "Gradient Descent Example": [[40, "id1"], [41, "id8"]], "Gradient boosting, making our own code for a regression case": [[49, "gradient-boosting-making-our-own-code-for-a-regression-case"], [50, "gradient-boosting-making-our-own-code-for-a-regression-case"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[11, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"], [49, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"], [50, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[3, "gradient-descent"], [45, "gradient-descent"]], "Gradient descent and Logistic regression": [[0, "gradient-descent-and-logistic-regression"], [42, "gradient-descent-and-logistic-regression"]], "Gradient descent and Ridge": [[40, "gradient-descent-and-ridge"], [41, "gradient-descent-and-ridge"]], "Gradient descent example": [[40, "gradient-descent-example"], [41, "gradient-descent-example"]], "Gradient expressions": [[43, "gradient-expressions"], [44, "gradient-expressions"]], "Gradient method": [[41, "gradient-method"]], "Gradients of loss functions": [[47, "gradients-of-loss-functions"]], "Grading": [[34, "grading"], [36, "grading"]], "Grid Search": [[40, "grid-search"]], "Growing a classification tree": [[48, "growing-a-classification-tree"], [49, "growing-a-classification-tree"]], "Hidden layers": [[43, "hidden-layers"], [44, "hidden-layers"]], "Homogeneous data": [[43, "homogeneous-data"], [44, "homogeneous-data"]], "Housing data, the code": [[1, "housing-data-the-code"]], "How do we set it up?": [[48, "how-do-we-set-it-up"]], "How do you judge your own level of knowledge on machine learning before and after this course?": [[26, "how-do-you-judge-your-own-level-of-knowledge-on-machine-learning-before-and-after-this-course"]], "How to do image compression before the era of deep learning": [[46, "how-to-do-image-compression-before-the-era-of-deep-learning"]], "How to set up the cross-validation for Ridge and/or Lasso": [[40, "how-to-set-up-the-cross-validation-for-ridge-and-or-lasso"]], "How would you improve this course?": [[26, "how-would-you-improve-this-course"]], "Hyperplanes and all that": [[9, "hyperplanes-and-all-that"]], "Identifying Terms": [[39, "identifying-terms"]], "If you did not attend the lectures or the lab sessions, which resources did you use?": [[26, "if-you-did-not-attend-the-lectures-or-the-lab-sessions-which-resources-did-you-use"]], "Illustration of a single perceptron model and a multi-perceptron model": [[42, "illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model"], [43, "illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model"]], "Implementing a memory cell in a neural network": [[47, "implementing-a-memory-cell-in-a-neural-network"]], "Important Matrix and vector handling packages": [[28, "important-matrix-and-vector-handling-packages"]], "Important observations": [[43, "important-observations"], [44, "important-observations"]], "Important technicalities: More on Rescaling data": [[38, "important-technicalities-more-on-rescaling-data"]], "Importing Keras and Tensorflow": [[46, "importing-keras-and-tensorflow"], [47, "importing-keras-and-tensorflow"]], "Improving gradient descent with momentum": [[41, "improving-gradient-descent-with-momentum"]], "Improving performance": [[2, "improving-performance"], [44, "improving-performance"], [45, "improving-performance"]], "In general not this simple": [[43, "in-general-not-this-simple"]], "In summary": [[34, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[14, "including-stochastic-gradient-descent-with-autograd"], [22, "including-stochastic-gradient-descent-with-autograd"], [41, "including-stochastic-gradient-descent-with-autograd"], [42, "including-stochastic-gradient-descent-with-autograd"]], "Including more classes": [[0, "including-more-classes"], [40, "including-more-classes"]], "Incremental PCA": [[12, "incremental-pca"]], "Independent and Identically Distributed (iid)": [[38, "independent-and-identically-distributed-iid"]], "Independent and Identically Distrubuted (iid)": [[39, "independent-and-identically-distrubuted-iid"]], "Input gate": [[47, "input-gate"]], "Inputs to the activation function": [[43, "inputs-to-the-activation-function"], [44, "inputs-to-the-activation-function"]], "Insights from the paper by Glorot and Bengio": [[43, "insights-from-the-paper-by-glorot-and-bengio"], [44, "insights-from-the-paper-by-glorot-and-bengio"]], "Installing R, C++, cython or Julia": [[36, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[36, "installing-r-c-cython-numba-etc"]], "Instructor information": [[34, "instructor-information"]], "Interpretations and optimizing our parameters": [[36, "interpretations-and-optimizing-our-parameters"], [36, "id2"], [36, "id3"], [37, "interpretations-and-optimizing-our-parameters"], [37, "id1"], [37, "id2"]], "Interpretations of Bayes\u2019 Theorem": [[38, "interpretations-of-bayes-theorem"], [39, "interpretations-of-bayes-theorem"]], "Interpreting the Ridge results": [[37, "interpreting-the-ridge-results"], [38, "interpreting-the-ridge-results"]], "Introducing JAX": [[14, "introducing-jax"], [22, "introducing-jax"], [41, "introducing-jax"], [42, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[12, "introducing-the-covariance-and-correlation-functions"], [37, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[1, "introduction"], [7, "introduction"], [27, "introduction"], [28, "introduction"]], "Introduction to Neural networks": [[42, "introduction-to-neural-networks"], [43, "introduction-to-neural-networks"]], "Introduction to numerical projects": [[29, "introduction-to-numerical-projects"], [30, "introduction-to-numerical-projects"], [31, "introduction-to-numerical-projects"]], "Inverse of Rectangular Matrix": [[38, "inverse-of-rectangular-matrix"]], "Is the Logistic activation function (Sigmoid) our choice?": [[43, "is-the-logistic-activation-function-sigmoid-our-choice"], [44, "is-the-logistic-activation-function-sigmoid-our-choice"]], "Iterative Fitting, Classification and AdaBoost": [[11, "iterative-fitting-classification-and-adaboost"], [48, "iterative-fitting-classification-and-adaboost"], [49, "iterative-fitting-classification-and-adaboost"], [50, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[11, "iterative-fitting-regression-and-squared-error-cost-function"], [48, "iterative-fitting-regression-and-squared-error-cost-function"], [49, "iterative-fitting-regression-and-squared-error-cost-function"], [50, "iterative-fitting-regression-and-squared-error-cost-function"]], "Joint distribution": [[50, "joint-distribution"]], "Kernel PCA": [[12, "kernel-pca"]], "Kernels and non-linearity": [[9, "kernels-and-non-linearity"]], "Key Idea": [[46, "key-idea"], [47, "key-idea"]], "LSTM details": [[47, "lstm-details"]], "LU Decomposition, the inverse of a matrix": [[28, "lu-decomposition-the-inverse-of-a-matrix"]], "Lab sessions on Tuesday and Wednesday": [[46, "lab-sessions-on-tuesday-and-wednesday"]], "Lab session: Material from last week and relevant for the first project": [[40, "lab-session-material-from-last-week-and-relevant-for-the-first-project"]], "Lab sessions": [[50, "lab-sessions"]], "Lab sessions Tuesday and Wednesday": [[42, "lab-sessions-tuesday-and-wednesday"]], "Lab sessions and lectures": [[26, "lab-sessions-and-lectures"]], "Lab sessions week 39": [[41, "lab-sessions-week-39"]], "Lasso Regression": [[38, "lasso-regression"]], "Lasso and Bayes": [[38, "lasso-and-bayes"], [39, "lasso-and-bayes"]], "Lasso case": [[38, "lasso-case"]], "Layers": [[2, "layers"], [44, "layers"], [45, "layers"], [46, "layers"], [47, "layers"]], "Layers of a CNN": [[46, "layers-of-a-cnn"], [47, "layers-of-a-cnn"]], "Layers used to build CNNs": [[4, "layers-used-to-build-cnns"], [46, "layers-used-to-build-cnns"], [47, "layers-used-to-build-cnns"]], "Layout of a neural network with three hidden layers": [[43, "layout-of-a-neural-network-with-three-hidden-layers"]], "Layout of a neural network with three hidden layers (last later = l=L=4, first layer l=0)": [[44, "layout-of-a-neural-network-with-three-hidden-layers-last-later-l-l-4-first-layer-l-0"]], "Layout of a simple neural network with no hidden layer": [[43, "layout-of-a-simple-neural-network-with-no-hidden-layer"], [44, "layout-of-a-simple-neural-network-with-no-hidden-layer"]], "Layout of a simple neural network with one hidden layer": [[43, "layout-of-a-simple-neural-network-with-one-hidden-layer"], [44, "layout-of-a-simple-neural-network-with-one-hidden-layer"]], "Layout of a simple neural network with two input nodes, one hidden layer and one output node": [[43, "layout-of-a-simple-neural-network-with-two-input-nodes-one-hidden-layer-and-one-output-node"]], "Layout of a simple neural network with two input nodes, one hidden layer with two hidden noeds and one output node": [[44, "layout-of-a-simple-neural-network-with-two-input-nodes-one-hidden-layer-with-two-hidden-noeds-and-one-output-node"]], "Layout of input to first hidden layer l=1 from input layer l=0": [[44, "layout-of-input-to-first-hidden-layer-l-1-from-input-layer-l-0"]], "Learning outcomes": [[27, "learning-outcomes"], [36, "learning-outcomes"]], "Learning outcomes and overarching aims of this course": [[50, "learning-outcomes-and-overarching-aims-of-this-course"]], "Learning rate methods": [[44, "learning-rate-methods"], [45, "learning-rate-methods"]], "Lecture Monday October 21": [[45, "lecture-monday-october-21"]], "Lecture Monday October 7": [[43, "lecture-monday-october-7"]], "Lecture Monday September 23": [[41, "lecture-monday-september-23"]], "Lecture Monday September 23, Optimization, the central part of any Machine Learning algortithm": [[41, "lecture-monday-september-23-optimization-the-central-part-of-any-machine-learning-algortithm"]], "Lecture Monday September 30, 2024": [[42, "lecture-monday-september-30-2024"]], "Lecture Monday, November 25": [[50, "lecture-monday-november-25"]], "Lecture October 14, 2024": [[44, "lecture-october-14-2024"]], "Lectures and ComputerLab": [[36, "lectures-and-computerlab"]], "Limitations of NNs": [[43, "limitations-of-nns"], [44, "limitations-of-nns"]], "Limitations of supervised learning with deep networks": [[2, "limitations-of-supervised-learning-with-deep-networks"], [43, "limitations-of-supervised-learning-with-deep-networks"], [44, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[28, null]], "Linear Regression": [[1, null]], "Linear Regression Problems": [[37, "linear-regression-problems"]], "Linear Regression and the SVD": [[38, "linear-regression-and-the-svd"]], "Linear Regression code, Intercept handling first": [[37, "linear-regression-code-intercept-handling-first"]], "Linear Regression, basic elements": [[1, "linear-regression-basic-elements"]], "Linear classifier": [[40, "linear-classifier"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[6, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[6, "linking-the-regression-analysis-with-a-statistical-interpretation"], [38, "linking-the-regression-analysis-with-a-statistical-interpretation"], [39, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with RNNs": [[47, "linking-with-rnns"]], "Linking with the SVD": [[6, "linking-with-the-svd"], [37, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[35, "links-to-relevant-courses-at-the-university-of-oslo"]], "List of contents:": [[46, "list-of-contents"], [47, "list-of-contents"]], "Logistic Regression": [[8, null], [8, "id1"], [40, "logistic-regression"]], "Logistic function as the root of problems": [[43, "logistic-function-as-the-root-of-problems"], [44, "logistic-function-as-the-root-of-problems"]], "MNIST and GANs": [[5, "mnist-and-gans"]], "Machine Learning": [[36, "machine-learning"]], "Machine Learning Research": [[50, "machine-learning-research"]], "Machine learning": [[27, "machine-learning"], [50, "machine-learning"]], "Main textbooks": [[36, "main-textbooks"]], "Making a tree": [[10, "making-a-tree"], [48, "making-a-tree"], [49, "making-a-tree"]], "Making an ADAboost code yourself": [[49, "making-an-adaboost-code-yourself"], [50, "making-an-adaboost-code-yourself"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[11, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"], [48, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"], [49, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Making your own test-train splitting": [[37, "making-your-own-test-train-splitting"]], "Marginal Probability": [[38, "marginal-probability"], [39, "marginal-probability"]], "Material for Lecture Monday November 4": [[47, "material-for-lecture-monday-november-4"]], "Material for Lecture Monday October 28": [[46, "material-for-lecture-monday-october-28"]], "Material for lecture Monday September 16": [[40, "material-for-lecture-monday-september-16"]], "Material for lecture Monday September 2": [[38, "material-for-lecture-monday-september-2"]], "Material for lecture Monday September 9": [[39, "material-for-lecture-monday-september-9"]], "Material for lecture Monday, August 26": [[37, "material-for-lecture-monday-august-26"]], "Material for the active learning sessions Tuesday and Wednesday": [[38, "material-for-the-active-learning-sessions-tuesday-and-wednesday"]], "Material for the active learning sessions on Tuesday and Wednesday": [[43, "material-for-the-active-learning-sessions-on-tuesday-and-wednesday"], [44, "material-for-the-active-learning-sessions-on-tuesday-and-wednesday"]], "Material for the lab sessions": [[39, "material-for-the-lab-sessions"]], "Material for the lab sessions, additional ways to present classification results and other practicalities": [[47, "material-for-the-lab-sessions-additional-ways-to-present-classification-results-and-other-practicalities"]], "Material for the lecture on Monday October 7, 2024": [[43, "material-for-the-lecture-on-monday-october-7-2024"]], "Mathematical Interpretation of Ordinary Least Squares": [[6, "mathematical-interpretation-of-ordinary-least-squares"], [37, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical model": [[42, "mathematical-model"], [42, "id2"], [42, "id3"], [42, "id4"], [42, "id5"]], "Mathematical optimization of convex functions": [[9, "mathematical-optimization-of-convex-functions"]], "Mathematical setup": [[47, "mathematical-setup"]], "Mathematics of CNNs": [[4, "mathematics-of-cnns"], [46, "mathematics-of-cnns"]], "Mathematics of deep learning": [[43, "mathematics-of-deep-learning"], [44, "mathematics-of-deep-learning"], [45, "mathematics-of-deep-learning"]], "Mathematics of deep learning and neural networks": [[43, "mathematics-of-deep-learning-and-neural-networks"]], "Mathematics of the SVD and implications": [[6, "mathematics-of-the-svd-and-implications"], [37, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[36, "matrices-in-python"]], "Matrix multiplication": [[2, "matrix-multiplication"], [44, "matrix-multiplication"], [45, "matrix-multiplication"]], "Matrix multiplications": [[44, "matrix-multiplications"], [45, "matrix-multiplications"]], "Matrix-vector notation": [[42, "matrix-vector-notation"]], "Matrix-vector notation and activation": [[13, "matrix-vector-notation-and-activation"], [42, "matrix-vector-notation-and-activation"]], "Maximum Likelihood Estimation (MLE)": [[38, "maximum-likelihood-estimation-mle"], [39, "maximum-likelihood-estimation-mle"]], "Maximum likelihood": [[40, "maximum-likelihood"]], "Meet the covariance!": [[33, "meet-the-covariance"]], "Meet the Covariance Matrix": [[6, "meet-the-covariance-matrix"], [37, "meet-the-covariance-matrix"]], "Meet the Hessian Matrix": [[37, "meet-the-hessian-matrix"]], "Meet the Pandas": [[36, "meet-the-pandas"]], "Memory considerations": [[46, "memory-considerations"]], "Meta learning": [[50, "meta-learning"]], "Min-Max Scaling": [[37, "min-max-scaling"]], "Minimization process": [[45, "minimization-process"]], "Minimizing the cost function using gradient descent and automatic differentiation": [[45, "minimizing-the-cost-function-using-gradient-descent-and-automatic-differentiation"]], "Minimizing the cross entropy": [[0, "minimizing-the-cross-entropy"], [40, "minimizing-the-cross-entropy"]], "Minor rewrite": [[47, "minor-rewrite"]], "Momentum based GD": [[14, "momentum-based-gd"], [22, "momentum-based-gd"], [41, "momentum-based-gd"], [42, "momentum-based-gd"]], "Momentum parameter": [[41, "momentum-parameter"], [42, "momentum-parameter"]], "More LSTM details": [[47, "more-lstm-details"]], "More about RBMs": [[50, "more-about-rbms"]], "More autograd": [[41, "more-autograd"], [42, "more-autograd"]], "More bagging": [[48, "more-bagging"], [49, "more-bagging"]], "More basic Statistics and Bayes\u2019 theorem": [[38, "more-basic-statistics-and-bayes-theorem"], [39, "more-basic-statistics-and-bayes-theorem"]], "More classes": [[0, "more-classes"], [40, "more-classes"]], "More complicated Example: The Ising model": [[7, "more-complicated-example-the-ising-model"]], "More complicated function": [[43, "more-complicated-function"]], "More complicated functions using the elements of their arguments directly": [[41, "more-complicated-functions-using-the-elements-of-their-arguments-directly"], [42, "more-complicated-functions-using-the-elements-of-their-arguments-directly"]], "More considerations": [[43, "more-considerations"], [44, "more-considerations"]], "More details": [[45, "more-details"], [45, "id6"]], "More examples on bootstrap and cross-validation and errors": [[39, "more-examples-on-bootstrap-and-cross-validation-and-errors"]], "More interpretations": [[37, "more-interpretations"], [38, "more-interpretations"]], "More limitations": [[43, "more-limitations"], [44, "more-limitations"]], "More on Dimensionalities": [[4, "more-on-dimensionalities"], [46, "more-on-dimensionalities"]], "More on Rescaling data": [[7, "more-on-rescaling-data"]], "More on Steepest descent": [[40, "more-on-steepest-descent"], [41, "more-on-steepest-descent"]], "More on activation functions, output layers": [[43, "more-on-activation-functions-output-layers"], [44, "more-on-activation-functions-output-layers"], [45, "more-on-activation-functions-output-layers"]], "More on convex functions": [[40, "more-on-convex-functions"], [41, "more-on-convex-functions"]], "More on momentum based approaches": [[41, "more-on-momentum-based-approaches"], [42, "more-on-momentum-based-approaches"]], "More on the general approximation theorem": [[43, "more-on-the-general-approximation-theorem"]], "More preprocessing": [[37, "more-preprocessing"]], "More preprocessing examples, two-dimensional example, the Franke function": [[37, "more-preprocessing-examples-two-dimensional-example-the-franke-function"]], "More technicalities": [[45, "more-technicalities"]], "More thinking": [[37, "more-thinking"]], "More top-down perspectives": [[43, "more-top-down-perspectives"], [44, "more-top-down-perspectives"]], "Multiclass classification": [[44, "multiclass-classification"], [45, "multiclass-classification"]], "Multilayer perceptrons": [[13, "multilayer-perceptrons"], [42, "multilayer-perceptrons"], [43, "multilayer-perceptrons"]], "Multivariable functions": [[43, "multivariable-functions"]], "Network Elements, the energy function": [[50, "network-elements-the-energy-function"]], "Network requirements": [[3, "network-requirements"], [45, "network-requirements"]], "Neural Networks vs CNNs": [[4, "neural-networks-vs-cnns"], [46, "neural-networks-vs-cnns"], [47, "neural-networks-vs-cnns"]], "Neural network types": [[42, "neural-network-types"], [43, "neural-network-types"]], "Neural networks": [[13, null]], "New expression for the derivative": [[43, "new-expression-for-the-derivative"]], "New image (or volume)": [[46, "new-image-or-volume"]], "New vector": [[46, "new-vector"]], "Note about SVD Calculations": [[37, "note-about-svd-calculations"]], "Note on Scikit-Learn": [[38, "note-on-scikit-learn"]], "Numerical experiments and the covariance, central limit theorem": [[33, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[28, "numpy-and-arrays"], [36, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[36, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization Methods and Hyperparameters": [[50, "optimization-methods-and-hyperparameters"]], "Optimization and Deep learning": [[40, "optimization-and-deep-learning"]], "Optimization, the central part of any Machine Learning algortithm": [[14, null], [40, "optimization-the-central-part-of-any-machine-learning-algortithm"]], "Optimized Convolution2DLayer": [[46, "optimized-convolution2dlayer"], [47, "optimized-convolution2dlayer"]], "Optimizing our parameters": [[36, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[36, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[2, "optimizing-the-cost-function"], [44, "optimizing-the-cost-function"], [45, "optimizing-the-cost-function"]], "Optimizing the parameters": [[43, "optimizing-the-parameters"], [44, "optimizing-the-parameters"]], "Ordinary Differential Equations first": [[45, "ordinary-differential-equations-first"]], "Organizing our data": [[1, "organizing-our-data"], [36, "organizing-our-data"]], "Other Matrix and Vector Operations": [[28, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[5, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning at UiO": [[36, "other-courses-on-data-science-and-machine-learning-at-uio"], [50, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning at UiO, contn": [[36, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other ingredients of a neural network": [[43, "other-ingredients-of-a-neural-network"]], "Other measures in classification studies: Cancer Data again": [[0, "other-measures-in-classification-studies-cancer-data-again"], [40, "other-measures-in-classification-studies-cancer-data-again"]], "Other parameters": [[43, "other-parameters"]], "Other popular texts": [[36, "other-popular-texts"]], "Other techniques": [[12, "other-techniques"]], "Other types of networks": [[13, "other-types-of-networks"], [42, "other-types-of-networks"], [43, "other-types-of-networks"]], "Other useful relations": [[37, "other-useful-relations"]], "Other ways of visualizing the trees": [[10, "other-ways-of-visualizing-the-trees"], [48, "other-ways-of-visualizing-the-trees"], [49, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[36, "our-model-for-the-nuclear-binding-energies"]], "Output gate": [[47, "output-gate"]], "Output layer": [[43, "output-layer"], [44, "output-layer"]], "Overarching aims of the exercises this week": [[18, "overarching-aims-of-the-exercises-this-week"], [19, "overarching-aims-of-the-exercises-this-week"], [20, "overarching-aims-of-the-exercises-this-week"], [21, "overarching-aims-of-the-exercises-this-week"], [22, "overarching-aims-of-the-exercises-this-week"], [23, "overarching-aims-of-the-exercises-this-week"], [24, "overarching-aims-of-the-exercises-this-week"], [25, "overarching-aims-of-the-exercises-this-week"], [26, "overarching-aims-of-the-exercises-this-week"]], "Overarching view of a neural network": [[43, "overarching-view-of-a-neural-network"]], "Overview of first week": [[36, "overview-of-first-week"]], "Overview of week 48": [[50, "overview-of-week-48"]], "Overview video on Stochastic Gradient Descent": [[41, "overview-video-on-stochastic-gradient-descent"], [42, "overview-video-on-stochastic-gradient-descent"]], "Own code for Ordinary Least Squares": [[36, "own-code-for-ordinary-least-squares"], [37, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[12, "pca-and-scikit-learn"]], "Padding": [[46, "padding"]], "Pandas AI": [[36, "pandas-ai"]], "Parameters of neural networks": [[43, "parameters-of-neural-networks"]], "Parameters to train, common settings": [[46, "parameters-to-train-common-settings"]], "Part a)": [[31, "part-a"]], "Part a) : Ordinary Least Square (OLS) on the Franke function": [[29, "part-a-ordinary-least-square-ols-on-the-franke-function"]], "Part a), setting up the problem": [[31, "part-a-setting-up-the-problem"]], "Part a): Write your own Stochastic Gradient Descent code, first step": [[30, "part-a-write-your-own-stochastic-gradient-descent-code-first-step"]], "Part b)": [[31, "part-b"], [31, "id1"]], "Part b): Adding Ridge regression for the Franke function": [[29, "part-b-adding-ridge-regression-for-the-franke-function"]], "Part b): Writing your own Neural Network code": [[30, "part-b-writing-your-own-neural-network-code"]], "Part c)": [[31, "part-c"]], "Part c) Neural networks": [[31, "part-c-neural-networks"]], "Part c): Adding Lasso for the Franke function": [[29, "part-c-adding-lasso-for-the-franke-function"]], "Part c): Testing different activation functions": [[30, "part-c-testing-different-activation-functions"]], "Part d)": [[31, "part-d"]], "Part d) Neural network complexity": [[31, "part-d-neural-network-complexity"]], "Part d): Classification analysis using neural networks": [[30, "part-d-classification-analysis-using-neural-networks"]], "Part d): Paper and pencil part": [[29, "part-d-paper-and-pencil-part"]], "Part e)": [[31, "part-e"], [31, "id2"]], "Part e): Bias-variance trade-off and resampling techniques": [[29, "part-e-bias-variance-trade-off-and-resampling-techniques"]], "Part e): Write your Logistic Regression code, final step": [[30, "part-e-write-your-logistic-regression-code-final-step"]], "Part f) Critical evaluation of the various algorithms": [[30, "part-f-critical-evaluation-of-the-various-algorithms"]], "Part f): Cross-validation as resampling techniques, adding more complexity": [[29, "part-f-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Part g): Analysis of real data": [[29, "part-g-analysis-of-real-data"]], "Partial Differential Equations": [[3, "partial-differential-equations"], [45, "partial-differential-equations"]], "Paths for project 3": [[31, "paths-for-project-3"]], "Perspective on Machine Learning": [[50, "perspective-on-machine-learning"]], "Plan for week 39, September 23-27, 2024": [[41, "plan-for-week-39-september-23-27-2024"]], "Plan for week 41, October 7-11": [[43, "plan-for-week-41-october-7-11"]], "Plan for week 44": [[46, "plan-for-week-44"]], "Plan for week 46": [[48, "plan-for-week-46"]], "Plan for week 47": [[49, "plan-for-week-47"]], "Plans for the lab sessions": [[40, "plans-for-the-lab-sessions"]], "Plans for week 35": [[37, "plans-for-week-35"]], "Plans for week 36": [[38, "plans-for-week-36"]], "Plans for week 37, lab sessions": [[39, "plans-for-week-37-lab-sessions"]], "Plans for week 37, lecture Monday": [[39, "plans-for-week-37-lecture-monday"]], "Plans for week 38, lecture Monday September 16": [[40, "plans-for-week-38-lecture-monday-september-16"]], "Plans for week 40": [[42, "plans-for-week-40"]], "Plans for week 43": [[45, "plans-for-week-43"]], "Plans for week 45": [[47, "plans-for-week-45"]], "Plotting the Histogram": [[39, "plotting-the-histogram"]], "Plotting the mean value for each group": [[40, "plotting-the-mean-value-for-each-group"]], "Pooling": [[46, "pooling"]], "Pooling Layer": [[46, "pooling-layer"], [47, "pooling-layer"]], "Pooling arithmetic": [[46, "pooling-arithmetic"]], "Pooling types (From Raschka et al)": [[46, "pooling-types-from-raschka-et-al"]], "Practical tips": [[14, "practical-tips"], [22, "practical-tips"], [41, "practical-tips"], [42, "practical-tips"]], "Practicalities": [[34, "practicalities"]], "Preamble: Note on writing reports, using reference material, AI and other tools": [[29, "preamble-note-on-writing-reports-using-reference-material-ai-and-other-tools"]], "Predicting New Points With A Trained Recurrent Neural Network": [[5, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Preparing Your Data": [[50, "preparing-your-data"]], "Preprocessing our data": [[37, "preprocessing-our-data"]], "Prerequisites": [[36, "prerequisites"]], "Prerequisites and background": [[27, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[4, "prerequisites-collect-and-pre-process-data"], [46, "prerequisites-collect-and-pre-process-data"], [47, "prerequisites-collect-and-pre-process-data"]], "Printing out as text": [[48, "printing-out-as-text"], [49, "printing-out-as-text"]], "Probability Distribution Functions": [[33, "probability-distribution-functions"]], "Program example for gradient descent with Ridge Regression": [[40, "program-example-for-gradient-descent-with-ridge-regression"], [41, "program-example-for-gradient-descent-with-ridge-regression"]], "Program for stochastic gradient": [[14, "program-for-stochastic-gradient"]], "Project 1 on Machine Learning, deadline October 7 (midnight), 2024": [[29, null]], "Project 2 on Machine Learning, deadline November 4 (Midnight)": [[30, null]], "Project 3 on Machine Learning, deadline December 9 (midnight), 2024": [[31, null]], "Project based teaching and active learning": [[26, "project-based-teaching-and-active-learning"]], "Properties of PDFs": [[33, "properties-of-pdfs"]], "Pros and cons of trees, pros": [[10, "pros-and-cons-of-trees-pros"], [48, "pros-and-cons-of-trees-pros"], [49, "pros-and-cons-of-trees-pros"]], "Pruning the tree": [[48, "pruning-the-tree"], [49, "pruning-the-tree"]], "Python installers": [[27, "python-installers"], [36, "python-installers"]], "Quantum deep learning": [[50, "quantum-deep-learning"]], "Quantum machine learning": [[50, "quantum-machine-learning"]], "Quantum machine learning algorithms based on linear algebra": [[50, "quantum-machine-learning-algorithms-based-on-linear-algebra"]], "Quantum reinforcement learning": [[50, "quantum-reinforcement-learning"]], "RMS prop": [[14, "rms-prop"], [41, "rms-prop"], [42, "rms-prop"]], "RMSProp algorithm, taken from Goodfellow et al": [[22, "rmsprop-algorithm-taken-from-goodfellow-et-al"], [42, "rmsprop-algorithm-taken-from-goodfellow-et-al"]], "RMSprop for adaptive learning rate with Stochastic Gradient Descent": [[22, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"], [41, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"], [42, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"]], "RNNs": [[47, "rnns"]], "RNNs in more detail": [[47, "rnns-in-more-detail"]], "RNNs in more detail, part 2": [[47, "rnns-in-more-detail-part-2"]], "RNNs in more detail, part 3": [[47, "rnns-in-more-detail-part-3"]], "RNNs in more detail, part 4": [[47, "rnns-in-more-detail-part-4"]], "RNNs in more detail, part 5": [[47, "rnns-in-more-detail-part-5"]], "RNNs in more detail, part 6": [[47, "rnns-in-more-detail-part-6"]], "RNNs in more detail, part 7": [[47, "rnns-in-more-detail-part-7"]], "Random Forest Algorithm": [[48, "random-forest-algorithm"], [49, "random-forest-algorithm"]], "Random Forest Algorithm, reminder from last week": [[50, "random-forest-algorithm-reminder-from-last-week"]], "Random Forests Compared with other Methods on the Cancer Data": [[48, "random-forests-compared-with-other-methods-on-the-cancer-data"], [49, "random-forests-compared-with-other-methods-on-the-cancer-data"], [50, "random-forests-compared-with-other-methods-on-the-cancer-data"]], "Random Numbers": [[33, "random-numbers"]], "Random forests": [[11, "random-forests"], [48, "random-forests"], [49, "random-forests"]], "Randomized Grid Search": [[40, "randomized-grid-search"]], "Randomized PCA": [[12, "randomized-pca"]], "Reading material": [[36, "reading-material"]], "Reading recommendations": [[43, "reading-recommendations"], [44, "reading-recommendations"], [45, "reading-recommendations"]], "Reading recommendations:": [[37, "reading-recommendations"]], "Reading suggestions week 34": [[36, "reading-suggestions-week-34"]], "Recurrent neural networks": [[13, "recurrent-neural-networks"], [42, "recurrent-neural-networks"], [43, "recurrent-neural-networks"]], "Recurrent neural networks (RNNs): Overarching view": [[47, "recurrent-neural-networks-rnns-overarching-view"]], "Recurrent neural networks: Overarching view": [[5, null]], "Reducing the number of degrees of freedom, overarching view": [[1, "reducing-the-number-of-degrees-of-freedom-overarching-view"], [37, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reducing the number of operations": [[43, "reducing-the-number-of-operations"]], "Reformulating the problem": [[3, "reformulating-the-problem"], [45, "reformulating-the-problem"]], "Regression Case": [[11, "regression-case"], [49, "regression-case"]], "Regression analysis and resampling methods": [[29, "regression-analysis-and-resampling-methods"]], "Regression analysis, overarching aims": [[36, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[36, "regression-analysis-overarching-aims-ii"]], "Regression trees": [[48, "regression-trees"], [49, "regression-trees"]], "Regular NNs don\u2019t scale well to full images": [[46, "regular-nns-dont-scale-well-to-full-images"], [47, "regular-nns-dont-scale-well-to-full-images"]], "Regularization": [[2, "regularization"], [44, "regularization"], [45, "regularization"]], "Reinforcement Learning": [[50, "reinforcement-learning"]], "Relevance": [[42, "relevance"], [43, "relevance"], [44, "relevance"]], "Remarks on the speed": [[46, "remarks-on-the-speed"], [47, "remarks-on-the-speed"]], "Reminder on Statistics": [[7, "reminder-on-statistics"]], "Reminder on books with hands-on material and codes": [[43, "reminder-on-books-with-hands-on-material-and-codes"], [44, "reminder-on-books-with-hands-on-material-and-codes"], [45, "reminder-on-books-with-hands-on-material-and-codes"]], "Reminder on the chain rule and gradients": [[43, "reminder-on-the-chain-rule-and-gradients"]], "Replace or not": [[14, "replace-or-not"], [41, "replace-or-not"], [42, "replace-or-not"]], "Required Technologies": [[27, "required-technologies"]], "Resampling": [[50, "resampling"]], "Resampling Methods": [[7, null]], "Resampling approaches can be computationally expensive": [[39, "resampling-approaches-can-be-computationally-expensive"]], "Resampling methods": [[7, "id1"], [39, "resampling-methods"], [39, "id2"]], "Resampling methods: Bootstrap": [[39, "resampling-methods-bootstrap"]], "Resampling methods: Bootstrap approach": [[39, "resampling-methods-bootstrap-approach"]], "Resampling methods: Bootstrap background": [[39, "resampling-methods-bootstrap-background"]], "Resampling methods: Bootstrap steps": [[39, "resampling-methods-bootstrap-steps"]], "Resampling methods: More Bootstrap background": [[39, "resampling-methods-more-bootstrap-background"]], "Residual Error": [[37, "residual-error"]], "Resources on differential equations and deep learning": [[3, "resources-on-differential-equations-and-deep-learning"], [45, "resources-on-differential-equations-and-deep-learning"]], "Revisiting our Linear Regression Solvers": [[14, "revisiting-our-linear-regression-solvers"]], "Revisiting our Logistic Regression case": [[40, "revisiting-our-logistic-regression-case"], [41, "revisiting-our-logistic-regression-case"]], "Revisiting our first homework": [[40, "revisiting-our-first-homework"], [41, "revisiting-our-first-homework"]], "Rewriting as dot products": [[46, "rewriting-as-dot-products"]], "Rewriting the Covariance and/or Correlation Matrix": [[37, "rewriting-the-covariance-and-or-correlation-matrix"]], "Rewriting the \\delta-function": [[39, "rewriting-the-delta-function"]], "Rewriting the fitting procedure as a linear algebra problem": [[36, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[36, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge Regression": [[38, "ridge-regression"]], "Ridge and Bayes": [[38, "ridge-and-bayes"], [39, "ridge-and-bayes"]], "Ridge and LASSO Regression": [[37, "ridge-and-lasso-regression"], [38, "ridge-and-lasso-regression"]], "Ridge and Lasso Regression": [[6, null], [6, "id1"]], "Running with Keras": [[46, "running-with-keras"], [47, "running-with-keras"]], "SGD example": [[41, "sgd-example"], [42, "sgd-example"]], "SVD analysis": [[38, "svd-analysis"]], "Same code but now with momentum gradient descent": [[14, "same-code-but-now-with-momentum-gradient-descent"], [22, "same-code-but-now-with-momentum-gradient-descent"], [22, "id1"], [41, "same-code-but-now-with-momentum-gradient-descent"], [41, "id9"], [41, "id10"], [42, "same-code-but-now-with-momentum-gradient-descent"], [42, "id1"]], "Schedule first week": [[36, "schedule-first-week"]], "Schedulers": [[46, "schedulers"], [47, "schedulers"]], "Schematic Regression Procedure": [[10, "schematic-regression-procedure"], [48, "schematic-regression-procedure"], [49, "schematic-regression-procedure"]], "Searching for Optimal Regularization Parameters \\lambda": [[40, "searching-for-optimal-regularization-parameters-lambda"]], "Second moment of the gradient": [[41, "second-moment-of-the-gradient"], [42, "second-moment-of-the-gradient"]], "Sequential data only?": [[47, "sequential-data-only"]], "Set up the model": [[46, "set-up-the-model"], [47, "set-up-the-model"]], "Setting it up": [[46, "setting-it-up"], [47, "setting-it-up"]], "Setting up a Multi-layer perceptron model for classification": [[44, "setting-up-a-multi-layer-perceptron-model-for-classification"], [45, "setting-up-a-multi-layer-perceptron-model-for-classification"]], "Setting up the Back propagation algorithm": [[13, "setting-up-the-back-propagation-algorithm"]], "Setting up the Back propagation algorithm, part 3": [[43, "setting-up-the-back-propagation-algorithm-part-3"], [44, "setting-up-the-back-propagation-algorithm-part-3"], [45, "setting-up-the-back-propagation-algorithm-part-3"]], "Setting up the Matrix to be inverted": [[37, "setting-up-the-matrix-to-be-inverted"]], "Setting up the back propagation algorithm": [[43, "setting-up-the-back-propagation-algorithm"]], "Setting up the back propagation algorithm and algorithm for a feed forward NN, initalizations": [[44, "setting-up-the-back-propagation-algorithm-and-algorithm-for-a-feed-forward-nn-initalizations"], [45, "setting-up-the-back-propagation-algorithm-and-algorithm-for-a-feed-forward-nn-initalizations"]], "Setting up the back propagation algorithm, part 1": [[44, "setting-up-the-back-propagation-algorithm-part-1"], [45, "setting-up-the-back-propagation-algorithm-part-1"]], "Setting up the back propagation algorithm, part 2": [[43, "setting-up-the-back-propagation-algorithm-part-2"], [44, "setting-up-the-back-propagation-algorithm-part-2"], [45, "setting-up-the-back-propagation-algorithm-part-2"]], "Setting up the code": [[45, "setting-up-the-code"]], "Setting up the equations for a neural network": [[43, "setting-up-the-equations-for-a-neural-network"], [44, "setting-up-the-equations-for-a-neural-network"]], "Setting up the network using Autograd": [[45, "setting-up-the-network-using-autograd"]], "Setting up the network using Autograd; The full program": [[3, "setting-up-the-network-using-autograd-the-full-program"], [45, "setting-up-the-network-using-autograd-the-full-program"]], "Setting up the network using Autograd; The trial solution": [[45, "setting-up-the-network-using-autograd-the-trial-solution"]], "Setting up the problem": [[45, "setting-up-the-problem"]], "Setup of Network": [[45, "setup-of-network"]], "Similar (second order function now) problem but now with AdaGrad": [[14, "similar-second-order-function-now-problem-but-now-with-adagrad"], [22, "similar-second-order-function-now-problem-but-now-with-adagrad"], [41, "similar-second-order-function-now-problem-but-now-with-adagrad"], [42, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[10, "simple-python-code-to-read-in-data-and-perform-classification"], [48, "simple-python-code-to-read-in-data-and-perform-classification"], [49, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple Voting Example, head or tail": [[48, "simple-voting-example-head-or-tail"], [49, "simple-voting-example-head-or-tail"]], "Simple case": [[37, "simple-case"]], "Simple code for solving the above problem": [[38, "simple-code-for-solving-the-above-problem"]], "Simple example": [[40, "simple-example"], [43, "simple-example"]], "Simple example code": [[41, "simple-example-code"], [42, "simple-example-code"]], "Simple example to illustrate Ordinary Least Squares, Ridge and Lasso Regression": [[38, "simple-example-to-illustrate-ordinary-least-squares-ridge-and-lasso-regression"]], "Simple geometric interpretation": [[40, "simple-geometric-interpretation"], [41, "simple-geometric-interpretation"]], "Simple implementation of GD for OLS, Ridge and Lasso": [[42, "simple-implementation-of-gd-for-ols-ridge-and-lasso"]], "Simple linear regression model using scikit-learn": [[1, "simple-linear-regression-model-using-scikit-learn"], [36, "simple-linear-regression-model-using-scikit-learn"]], "Simple neural network and the back propagation equations": [[43, "simple-neural-network-and-the-back-propagation-equations"], [44, "simple-neural-network-and-the-back-propagation-equations"]], "Simple program": [[40, "simple-program"], [41, "simple-program"]], "Simpler examples first, and automatic differentiation": [[43, "simpler-examples-first-and-automatic-differentiation"]], "Slightly different approach": [[41, "slightly-different-approach"], [42, "slightly-different-approach"]], "Smarter way of evaluating the above function": [[43, "smarter-way-of-evaluating-the-above-function"]], "Social machine learning": [[50, "social-machine-learning"]], "Software and needed installations": [[29, "software-and-needed-installations"], [31, "software-and-needed-installations"], [36, "software-and-needed-installations"]], "Solving Differential Equations with Deep Learning": [[3, null]], "Solving differential equations with Deep Learning": [[45, "solving-differential-equations-with-deep-learning"]], "Solving differential equations with RNNs": [[47, "solving-differential-equations-with-rnns"]], "Solving partial differential equations with neural networks": [[31, "solving-partial-differential-equations-with-neural-networks"]], "Solving the equation using Autograd": [[45, "solving-the-equation-using-autograd"]], "Solving the one dimensional Poisson equation": [[3, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation - the full program using Autograd": [[45, "solving-the-wave-equation-the-full-program-using-autograd"]], "Solving the wave equation with Neural Networks": [[3, "solving-the-wave-equation-with-neural-networks"]], "Solving using Newton-Raphson\u2019s method": [[40, "solving-using-newton-raphson-s-method"], [41, "solving-using-newton-raphson-s-method"]], "Some famous Matrices": [[28, "some-famous-matrices"]], "Some parallels from real analysis": [[43, "some-parallels-from-real-analysis"]], "Some selected properties": [[40, "some-selected-properties"]], "Some similarities and differences from DNNs": [[50, "some-similarities-and-differences-from-dnns"]], "Some simple problems": [[14, "some-simple-problems"], [40, "some-simple-problems"], [41, "some-simple-problems"]], "Some useful matrix and vector expressions": [[37, "some-useful-matrix-and-vector-expressions"]], "Splitting our Data in Training and Test data": [[1, "splitting-our-data-in-training-and-test-data"], [36, "splitting-our-data-in-training-and-test-data"], [37, "splitting-our-data-in-training-and-test-data"]], "Squared-Error Example and Iterative Fitting": [[48, "squared-error-example-and-iterative-fitting"], [49, "squared-error-example-and-iterative-fitting"], [50, "squared-error-example-and-iterative-fitting"]], "Standard Approach based on the Normal Distribution": [[39, "standard-approach-based-on-the-normal-distribution"]], "Standard imports first": [[48, "standard-imports-first"], [49, "standard-imports-first"]], "Standard steepest descent": [[14, "standard-steepest-descent"], [41, "standard-steepest-descent"]], "Starting your Machine Learning Project": [[50, "starting-your-machine-learning-project"]], "Statistical analysis": [[39, "statistical-analysis"]], "Statistical analysis and optimization of data": [[27, "statistical-analysis-and-optimization-of-data"], [36, "statistical-analysis-and-optimization-of-data"], [50, "statistical-analysis-and-optimization-of-data"]], "Steepest Descent Example": [[49, "steepest-descent-example"], [50, "steepest-descent-example"]], "Steepest descent": [[14, "steepest-descent"], [40, "steepest-descent"], [41, "steepest-descent"]], "Steepest descent method": [[41, "steepest-descent-method"], [41, "id1"]], "Steepest descent example": [[41, "steepest-descent-example"]], "Still thinking": [[37, "still-thinking"]], "Stochastic Gradient Descent": [[41, "stochastic-gradient-descent"], [42, "stochastic-gradient-descent"]], "Stochastic Gradient Descent (SGD)": [[14, "stochastic-gradient-descent-sgd"], [41, "stochastic-gradient-descent-sgd"], [42, "stochastic-gradient-descent-sgd"]], "Stochastic variables and the main concepts, the discrete case": [[33, "stochastic-variables-and-the-main-concepts-the-discrete-case"]], "Strong correlations": [[46, "strong-correlations"], [47, "strong-correlations"]], "Suggested reading and videos": [[40, "suggested-reading-and-videos"]], "Suggested readings and videos": [[42, "suggested-readings-and-videos"]], "Summarizing: Performing a general discrete convolution (From Raschka et al)": [[46, "summarizing-performing-a-general-discrete-convolution-from-raschka-et-al"]], "Summary from last week, using gradient descent methods, limitations": [[42, "summary-from-last-week-using-gradient-descent-methods-limitations"]], "Summary of RNNs": [[47, "summary-of-rnns"]], "Summary of a typical RNN": [[47, "summary-of-a-typical-rnn"]], "Summary of course": [[50, "summary-of-course"]], "Summing up": [[39, "summing-up"]], "Support Vector Machines, overarching aims": [[9, null]], "Systematic reduction": [[4, "systematic-reduction"], [46, "systematic-reduction"], [47, "systematic-reduction"]], "Teachers": [[36, "teachers"]], "Teachers and Grading": [[34, null]], "Teaching Assistants Fall semester 2023": [[34, "teaching-assistants-fall-semester-2023"]], "Teaching schedule with links to material": [[32, null]], "Technicalities": [[45, "technicalities"]], "Tensorflow": [[44, "tensorflow"], [45, "tensorflow"]], "Test Function for what happens with OLS, Ridge and Lasso": [[38, "test-function-for-what-happens-with-ols-ridge-and-lasso"]], "Testing the Means Squared Error as function of Complexity": [[1, "testing-the-means-squared-error-as-function-of-complexity"], [37, "testing-the-means-squared-error-as-function-of-complexity"]], "Testing the XOR gate and other gates": [[44, "testing-the-xor-gate-and-other-gates"], [45, "testing-the-xor-gate-and-other-gates"]], "Textbooks": [[35, null]], "The back propagation equations for a neural network": [[44, "the-back-propagation-equations-for-a-neural-network"]], "The Algorithm before theorem": [[12, "the-algorithm-before-theorem"]], "The Boston housing data example": [[1, "the-boston-housing-data-example"]], "The Breast Cancer Data, now with Keras": [[2, "the-breast-cancer-data-now-with-keras"], [44, "the-breast-cancer-data-now-with-keras"], [45, "the-breast-cancer-data-now-with-keras"]], "The CART algorithm for Classification": [[10, "the-cart-algorithm-for-classification"], [48, "the-cart-algorithm-for-classification"], [49, "the-cart-algorithm-for-classification"]], "The CART algorithm for Regression": [[10, "the-cart-algorithm-for-regression"], [48, "the-cart-algorithm-for-regression"], [49, "the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[4, "the-cifar01-data-set"], [46, "the-cifar01-data-set"], [47, "the-cifar01-data-set"]], "The Central Limit Theorem": [[39, "the-central-limit-theorem"]], "The Challenges Facing Machine Learning": [[50, "the-challenges-facing-machine-learning"]], "The Convolutional Neural Network (CNN)": [[46, "the-convolutional-neural-network-cnn"], [47, "the-convolutional-neural-network-cnn"]], "The Hessian matrix": [[40, "the-hessian-matrix"], [41, "the-hessian-matrix"]], "The Hessian matrix for Ridge Regression": [[40, "the-hessian-matrix-for-ridge-regression"], [41, "the-hessian-matrix-for-ridge-regression"]], "The Jacobian": [[37, "the-jacobian"]], "The MNIST dataset again": [[4, "the-mnist-dataset-again"], [46, "the-mnist-dataset-again"], [47, "the-mnist-dataset-again"]], "The Neural Network": [[44, "the-neural-network"], [45, "the-neural-network"]], "The OLS case": [[38, "the-ols-case"]], "The RELU function family": [[2, "the-relu-function-family"], [43, "the-relu-function-family"], [44, "the-relu-function-family"], [45, "the-relu-function-family"]], "The Ridge case": [[38, "the-ridge-case"]], "The SVD example": [[46, "the-svd-example"]], "The SVD, a Fantastic Algorithm": [[37, "the-svd-a-fantastic-algorithm"]], "The Softmax function": [[2, "the-softmax-function"], [44, "the-softmax-function"], [45, "the-softmax-function"]], "The Squared-Error again! Steepest Descent": [[49, "the-squared-error-again-steepest-descent"], [50, "the-squared-error-again-steepest-descent"]], "The Table": [[48, "the-table"], [49, "the-table"]], "The \\chi^2 function": [[1, "the-chi-2-function"], [36, "the-chi-2-function"], [36, "id4"], [36, "id5"], [36, "id6"], [36, "id7"], [36, "id8"]], "The analytical solution": [[45, "the-analytical-solution"]], "The approximation theorem in words": [[43, "the-approximation-theorem-in-words"]], "The backward pass is linear": [[47, "the-backward-pass-is-linear"]], "The basic structure of your project": [[31, "the-basic-structure-of-your-project"]], "The bias-variance tradeoff": [[7, "the-bias-variance-tradeoff"], [39, "the-bias-variance-tradeoff"]], "The code": [[36, "the-code"]], "The code for solving the ODE": [[3, "the-code-for-solving-the-ode"], [45, "the-code-for-solving-the-ode"]], "The complete code with a simple data set": [[37, "the-complete-code-with-a-simple-data-set"]], "The convolution stage": [[46, "the-convolution-stage"]], "The cost function": [[0, "the-cost-function"]], "The cost function rewritten": [[40, "the-cost-function-rewritten"]], "The cost/loss function": [[37, "the-cost-loss-function"]], "The course has two central parts": [[27, "the-course-has-two-central-parts"]], "The derivative of the Logistic funtion": [[43, "the-derivative-of-the-logistic-funtion"], [44, "the-derivative-of-the-logistic-funtion"]], "The derivative of the cost/loss function": [[40, "the-derivative-of-the-cost-loss-function"], [41, "the-derivative-of-the-cost-loss-function"]], "The derivatives": [[43, "the-derivatives"], [44, "the-derivatives"]], "The equations": [[40, "the-equations"], [41, "the-equations"]], "The equations for ordinary least squares": [[37, "the-equations-for-ordinary-least-squares"]], "The equations to solve": [[40, "the-equations-to-solve"], [41, "the-equations-to-solve"]], "The first Case": [[38, "the-first-case"]], "The forget gate": [[47, "the-forget-gate"]], "The function to solve for": [[45, "the-function-to-solve-for"]], "The gradient step": [[41, "the-gradient-step"], [42, "the-gradient-step"]], "The ideal": [[40, "the-ideal"], [41, "the-ideal"]], "The last words?": [[50, "the-last-words"]], "The logistic function": [[0, "the-logistic-function"], [8, "the-logistic-function"], [40, "the-logistic-function"]], "The mean squared error and its derivative": [[37, "the-mean-squared-error-and-its-derivative"]], "The moons example": [[9, "the-moons-example"]], "The multilayer perceptron (MLP)": [[13, "the-multilayer-perceptron-mlp"]], "The network": [[50, "the-network"]], "The network with one input layer, specified number of hidden layers, and one output layer": [[3, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"], [45, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"]], "The optimization problem": [[43, "the-optimization-problem"]], "The ouput layer": [[43, "the-ouput-layer"], [44, "the-ouput-layer"]], "The problem of exploding or vanishing gradients": [[47, "the-problem-of-exploding-or-vanishing-gradients"]], "The problem to solve for": [[45, "the-problem-to-solve-for"]], "The program using Autograd": [[45, "the-program-using-autograd"]], "The same example but now with cross-validation": [[39, "the-same-example-but-now-with-cross-validation"]], "The sensitiveness of the gradient descent": [[40, "the-sensitiveness-of-the-gradient-descent"], [41, "the-sensitiveness-of-the-gradient-descent"]], "The singular value decomposition": [[6, "the-singular-value-decomposition"], [37, "the-singular-value-decomposition"]], "The specific equation to solve for": [[45, "the-specific-equation-to-solve-for"]], "The structure of the RBM network": [[50, "the-structure-of-the-rbm-network"]], "The syntax a.dot(b) when finding the dot product": [[41, "the-syntax-a-dot-b-when-finding-the-dot-product"]], "The training": [[43, "the-training"], [44, "the-training"]], "The trial solution": [[45, "the-trial-solution"], [45, "id4"], [45, "id5"], [45, "id7"]], "The two-dimensional case": [[9, "the-two-dimensional-case"]], "Then some basic questions": [[26, "then-some-basic-questions"]], "Time decay rate": [[41, "time-decay-rate"], [42, "time-decay-rate"]], "To our real data: nuclear binding energies. Brief reminder on masses and binding energies": [[36, "to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies"]], "To think about, first part": [[37, "to-think-about-first-part"]], "Toeplitz matrices": [[46, "toeplitz-matrices"]], "Topics covered in this course: Statistical analysis and optimization of data": [[36, "topics-covered-in-this-course-statistical-analysis-and-optimization-of-data"]], "Topics we have covered this year": [[50, "topics-we-have-covered-this-year"]], "Tossing coins": [[48, "tossing-coins"], [49, "tossing-coins"]], "Towards the PCA theorem": [[12, "towards-the-pca-theorem"]], "Train and test datasets": [[2, "train-and-test-datasets"], [44, "train-and-test-datasets"], [45, "train-and-test-datasets"]], "Transfer learning": [[50, "transfer-learning"]], "Transforming images": [[46, "transforming-images"]], "Two first-order differential equations": [[47, "two-first-order-differential-equations"]], "Two parameters": [[0, "two-parameters"], [40, "two-parameters"]], "Two-dimensional Objects": [[4, "two-dimensional-objects"]], "Two-dimensional objects": [[46, "two-dimensional-objects"]], "Type of problem": [[3, "type-of-problem"], [45, "type-of-problem"]], "Types of Machine Learning": [[36, "types-of-machine-learning"]], "Types of Machine Learning, a repetition": [[50, "types-of-machine-learning-a-repetition"]], "Understanding what happens": [[39, "understanding-what-happens"]], "Universal approximation theorem": [[43, "universal-approximation-theorem"]], "Unsupported functions": [[41, "unsupported-functions"]], "Updating the gradients": [[43, "updating-the-gradients"], [44, "updating-the-gradients"], [45, "updating-the-gradients"]], "Usage of CNN code": [[46, "usage-of-cnn-code"], [47, "usage-of-cnn-code"]], "Usage of activation functions": [[46, "usage-of-activation-functions"], [47, "usage-of-activation-functions"]], "Usage of cost functions": [[46, "usage-of-cost-functions"], [47, "usage-of-cost-functions"]], "Usage of schedulers": [[46, "usage-of-schedulers"], [47, "usage-of-schedulers"]], "Usage of the above learning rate schedulers": [[44, "usage-of-the-above-learning-rate-schedulers"], [45, "usage-of-the-above-learning-rate-schedulers"]], "Useful Python libraries": [[27, "useful-python-libraries"], [36, "useful-python-libraries"]], "Usefulness of the weekly exercises": [[26, "usefulness-of-the-weekly-exercises"]], "Using Autograd": [[14, "using-autograd"]], "Using Autograd with OLS": [[41, "using-autograd-with-ols"], [42, "using-autograd-with-ols"]], "Using Automatic differentation with OLS": [[22, "using-automatic-differentation-with-ols"]], "Using Automatic differentiation": [[45, "using-automatic-differentiation"]], "Using Keras": [[44, "using-keras"], [45, "using-keras"]], "Using autograd": [[41, "using-autograd"], [42, "using-autograd"]], "Using forward Euler to solve the ODE": [[3, "using-forward-euler-to-solve-the-ode"], [45, "using-forward-euler-to-solve-the-ode"]], "Using gradient descent methods, limitations": [[14, "using-gradient-descent-methods-limitations"], [40, "using-gradient-descent-methods-limitations"], [41, "using-gradient-descent-methods-limitations"]], "Using recursion": [[41, "using-recursion"], [42, "using-recursion"]], "Using the Voting Classifier": [[48, "using-the-voting-classifier"], [49, "using-the-voting-classifier"]], "Using the chain rule and summing over all k entries": [[43, "using-the-chain-rule-and-summing-over-all-k-entries"], [44, "using-the-chain-rule-and-summing-over-all-k-entries"]], "Using the correlation matrix": [[0, "using-the-correlation-matrix"], [40, "using-the-correlation-matrix"]], "Vanishing gradients": [[43, "vanishing-gradients"], [44, "vanishing-gradients"], [47, "vanishing-gradients"]], "Various steps in cross-validation": [[39, "various-steps-in-cross-validation"], [40, "various-steps-in-cross-validation"]], "Velocity only": [[47, "velocity-only"]], 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"zaman": 33, "zaxi": [7, 29], "zero": [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 18, 19, 20, 22, 23, 24, 28, 29, 33, 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48, 49, 50], "zeros_lik": 5, "zeroth": [37, 46], "zfill": 5, "zip": [5, 7, 23, 24, 49, 50], "zm_h": [1, 36], "zn": 1, "zone": 1, "zoom": 36, "zx": [28, 36], "zy": [28, 36], "zz": [28, 36], "\u00f8yvind": [7, 37, 38], "\u03b4": [44, 45]}, "titles": ["Exercises Week 42: Logistic Regression and Optimization, reminders from week 38 and week 40", "3. Linear Regression", "14. Building a Feed Forward Neural Network", "15. Solving Differential Equations with Deep Learning", "16. Convolutional Neural Networks", "17. Recurrent neural networks: Overarching view", "4. Ridge and Lasso Regression", "5. Resampling Methods", "6. Logistic Regression", "8. Support Vector Machines, overarching aims", "9. Decision trees, overarching aims", "10. Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods", "11. Basic ideas of the Principal Component Analysis (PCA)", "13. Neural networks", "7. Optimization, the central part of any Machine Learning algortithm", "12. Clustering and Unsupervised Learning", "Exercises week 34", "Exercises week 35", "Exercises week 36", "Exercises week 37", "Exercises week 38", "Exercises week 39", "Exercises week 41", "Exercises week 42", "Exercises week 43", "Exercise week 47", "Exercises week 48", "Applied Data Analysis and Machine Learning", "2. Linear Algebra, Handling of Arrays and more Python Features", "Project 1 on Machine Learning, deadline October 7 (midnight), 2024", "Project 2 on Machine Learning, deadline November 4 (Midnight)", "Project 3 on Machine Learning, deadline December 9 (midnight), 2024", "Teaching schedule with links to material", "1. Elements of Probability Theory and Statistical Data Analysis", "Teachers and Grading", "Textbooks", "Week 34: Introduction to the course, Logistics and Practicalities", "Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression", "Week 36: Linear Regression and Statistical interpretations", "Week 37: Statistical interpretations and Resampling Methods", "Week 38: Logistic Regression and Optimization", "Week 39: Optimization and Gradient Methods", "Week 40: Gradient descent methods (continued) and start Neural networks", "Week 41 Neural networks and constructing a neural network code", "Week 42 Constructing a Neural Network code with examples", "Week 43: Deep Learning: Constructing a Neural Network code and solving differential equations", "Week 44, Convolutional Neural Networks (CNN)", "Week 45, Convolutional Neural Networks (CCNs) and Recurrent Neural Networks (RNNs)", "Week 46: Decision Trees, Ensemble methods and Random Forests", "Week 47: From Decision Trees to Ensemble 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Week 48: Gradient boosting and summary of course#

Morten Hjorth-Jensen, Department of Physics and Center for Computing in Science Education, University of Oslo, Norway

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Date: Nov 24, 2024

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Date: Nov 25, 2024

Copyright 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license

Overview of week 48#

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a. These lecture notes at CompPhysics/MachineLearning

b. See also lecture notes from week 47 at CompPhysics/MachineLearning. The lecture on Monday starts with a repetition on AdaBoost before we move over to gradient boosting with examples

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c. Video on Decision trees https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn

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d. Video on boosting methods https://www.youtube.com/watch?v=wPqtzj5VZus&ab_channel=H2O.ai

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e. Video on AdaBoost https://www.youtube.com/watch?v=LsK-xG1cLYA

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f. Video on Gradient boost, part 1, parts 2-4 follow thereafter https://www.youtube.com/watch?v=3CC4N4z3GJc

-

g. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf.

+

c. Video of lecture at https://youtu.be/iTaRdAPQnDA

+

d. Whiteboard notes at CompPhysics/MachineLearning

+

e. Video on Decision trees https://www.youtube.com/watch?v=RmajweUFKvM&ab_channel=Simplilearn

+

f. Video on boosting methods https://www.youtube.com/watch?v=wPqtzj5VZus&ab_channel=H2O.ai

+

g. Video on AdaBoost https://www.youtube.com/watch?v=LsK-xG1cLYA

+

h. Video on Gradient boost, part 1, parts 2-4 follow thereafter https://www.youtube.com/watch?v=3CC4N4z3GJc

+

i. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at https://www.uio.no/studier/emner/matnat/math/STK-IN4300/h20/slides/lecture_7.pdf.

Lab sessions#

@@ -651,17 +651,17 @@ doconce format html week48.do.txt --no_mako --> (143, 30) Test set accuracy Logistic Regression with scaled data: 0.96 Test set accuracy SVM with scaled data: 0.96 -Test set accuracy with Decision Trees and scaled data: 0.91 +Test set accuracy with Decision Trees and scaled data: 0.92 -
[1.         0.8        0.93333333 1.         1.         0.92857143
+
[1.         0.73333333 0.93333333 1.         1.         0.92857143
  1.         0.92857143 0.92857143 0.92857143]
-Test set accuracy with Random Forests and scaled data: 0.98
+Test set accuracy with Random Forests and scaled data: 0.97
 

 

-_images/936367d3bdcae10aafd2cc903d30ce54287b55ddddaa7af46f455a620a3745cd.png
-_images/be8d5df8bb940da757ef8fd6eac65ebe0352641500f781f4284c949ce274e1ee.png
-_images/8f696a60652d0003039dd9a563eb80367f1d574ca15b61c7a3f9757b19083d26.png
+_images/90075505602c3f17740e87e303ddff0ae0ff2ec0245679468ad8fb7cb2ba3b3a.png
+_images/a318a67ffc1f6a60c2418c7f612568dd9b06752eb57ba2c4643dcc6e7d1a01dc.png
+_images/1d150add40cbaa91d293348b988a5fbd2e04c68c41f11a70ebf78ac5686e7e4a.png

Recall that the cumulative gains curve shows the percentage of the @@ -1173,30 +1173,30 @@ C(\boldsymbol{y},\boldsymbol{f})=\sum_{i=0}^{n-1}(y_i-f(x_i))^2. 

Max depth: 1
-Error: 0.4203129333425336
-Bias^2: 0.21226966048908316
-Var: 0.20804327285345042
-0.4203129333425336 >= 0.21226966048908316 + 0.20804327285345042 = 0.4203129333425336
+Error: 0.4010613825254484
+Bias^2: 0.2079593417034804
+Var: 0.19310204082196794
+0.4010613825254484 >= 0.2079593417034804 + 0.19310204082196794 = 0.4010613825254483
 Max depth: 2
-Error: 0.40767639731018696
-Bias^2: 0.21200998139721822
-Var: 0.19566641591296877
-0.40767639731018696 >= 0.21200998139721822 + 0.19566641591296877 = 0.407676397310187
+Error: 0.4250776117755916
+Bias^2: 0.2080984218270197
+Var: 0.21697918994857185
+0.4250776117755916 >= 0.2080984218270197 + 0.21697918994857185 = 0.42507761177559156
 Max depth: 3
-Error: 0.4076774836661818
-Bias^2: 0.2120099429256955
-Var: 0.19566754074048626
-0.4076774836661818 >= 0.2120099429256955 + 0.19566754074048626 = 0.40767748366618173
+Error: 0.4250796355306808
+Bias^2: 0.2080985447081304
+Var: 0.21698109082255032
+0.4250796355306808 >= 0.2080985447081304 + 0.21698109082255032 = 0.42507963553068073
 Max depth: 4
-Error: 0.4076774836661818
-Bias^2: 0.2120099429256955
-Var: 0.19566754074048626
-0.4076774836661818 >= 0.2120099429256955 + 0.19566754074048626 = 0.40767748366618173
+Error: 0.4250796355306808
+Bias^2: 0.2080985447081304
+Var: 0.21698109082255038
+0.4250796355306808 >= 0.2080985447081304 + 0.21698109082255038 = 0.4250796355306808
 Max depth: 5
-Error: 0.4076774836661816
-Bias^2: 0.2120099429256955
-Var: 0.1956675407404862
-0.4076774836661816 >= 0.2120099429256955 + 0.1956675407404862 = 0.40767748366618173
+Error: 0.42507963553068073
+Bias^2: 0.2080985447081304
+Var: 0.21698109082255032
+0.42507963553068073 >= 0.2080985447081304 + 0.21698109082255032 = 0.42507963553068073
 
/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/ensemble/_gb.py:424: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().
@@ -1211,7 +1211,7 @@ Var: 0.1956675407404862
   y = column_or_1d(y, warn=True)
-_images/82abcfe355d89bb3d7dd4eb6e9ffe48d1f3f3511015d80687fbe0db1b8fd5999.png +_images/801d16873af4f485d9c398ac11811a7d9c4f25dd40c995cf7565bd81af8071c5.png
@@ -1264,13 +1264,13 @@ Var: 0.1956675407404862 (143, 30) -
[0.93333333 0.93333333 0.93333333 0.92857143 1.         0.92857143
+
[0.93333333 0.93333333 0.86666667 1.         1.         0.92857143
  1.         0.92857143 0.85714286 0.92857143]
-Test set accuracy with Gradient boosting and scaled data: 0.99
+Test set accuracy with Gradient boosting and scaled data: 0.97
 

 

-_images/e0c9e5cfc32bfe482c04b2091d5d98aa80212c0eef5e22892d16f73e93be8afc.png
-_images/796ded1719cb864b630c37e7692028ee18f0df7fb5a0ed2de15817b0c2f36c0c.png
+_images/90075505602c3f17740e87e303ddff0ae0ff2ec0245679468ad8fb7cb2ba3b3a.png
+_images/de0019c6a0f8206c4c2b0f29eedfececf3b0ad5a07a733217a9ed5b8e4cae808.png
 _images/45972a93ed8e1f6ed66fe9c322a65b549b39dd2c80e16d5081151b2bc713b669.png
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a/doc/LectureNotes/_build/jupyter_execute/week48.ipynb b/doc/LectureNotes/_build/jupyter_execute/week48.ipynb index 5b915bbfe..b5b279a84 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week48.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week48.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d7c532d5", + "id": "da40c119", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "d27ab2b4", + "id": "82348ef6", "metadata": { "editable": true }, @@ -22,14 +22,14 @@ "# Week 48: Gradient boosting and summary of course\n", "**Morten Hjorth-Jensen**, Department of Physics and Center for Computing in Science Education, University of Oslo, Norway\n", "\n", - "Date: **Nov 24, 2024**\n", + "Date: **Nov 25, 2024**\n", "\n", "Copyright 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license" ] }, { "cell_type": "markdown", - "id": "9f55b05a", + "id": "61b3407d", "metadata": { "editable": true }, @@ -39,7 +39,7 @@ }, { "cell_type": "markdown", - "id": "e72578f8", + "id": "c40156de", "metadata": { "editable": true }, @@ -56,23 +56,25 @@ "a. These lecture notes at \n", "\n", "b. See also lecture notes from week 47 at . The lecture on Monday starts with a repetition on AdaBoost before we move over to gradient boosting with examples\n", - "\n", - "\n", "\n", - "c. Video on Decision trees \n", + "c. Video of lecture at \n", "\n", - "d. Video on boosting methods \n", + "d. Whiteboard notes at \n", "\n", - "e. Video on AdaBoost \n", + "e. Video on Decision trees \n", "\n", - "f. Video on Gradient boost, part 1, parts 2-4 follow thereafter \n", + "f. Video on boosting methods \n", "\n", - "g. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at ." + "g. Video on AdaBoost \n", + "\n", + "h. Video on Gradient boost, part 1, parts 2-4 follow thereafter \n", + "\n", + "i. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at ." ] }, { "cell_type": "markdown", - "id": "51ee8fb3", + "id": "53d9e808", "metadata": { "editable": true }, @@ -91,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "5b4ed2be", + "id": "9edfc128", "metadata": { "editable": true }, @@ -118,7 +120,7 @@ }, { "cell_type": "markdown", - "id": "3e76e282", + "id": "9278d8ee", "metadata": { "editable": true }, @@ -129,7 +131,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "acba62b3", + "id": "116422d2", "metadata": { "collapsed": false, "editable": true @@ -143,21 +145,21 @@ "(143, 30)\n", "Test set accuracy Logistic Regression with scaled data: 0.96\n", "Test set accuracy SVM with scaled data: 0.96\n", - "Test set accuracy with Decision Trees and scaled data: 0.91\n" + "Test set accuracy with Decision Trees and scaled data: 0.92\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "[1. 0.8 0.93333333 1. 1. 0.92857143\n", + "[1. 0.73333333 0.93333333 1. 1. 0.92857143\n", " 1. 0.92857143 0.92857143 0.92857143]\n", - "Test set accuracy with Random Forests and scaled data: 0.98\n" + "Test set accuracy with Random Forests and scaled data: 0.97\n" ] }, { "data": { - "image/png": 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cuTh06BD69OkDd3d3ZGVlYfv27UhNTVXdeW7ixIn4/vvv8dprr2HUqFFwdnbGmjVrcPXqVWzdurVS5UEfw4YNw4cffojevXujS5cuOHv2LPbt21dplOvv74+OHTsiKCgIzs7OOHnyJLZs2YKRI0dWue2a/EwFBAQgICDgiX38/PzQpEkTjBs3Djdv3oSDgwO2bt2q8Zx+UFAQAGDUqFEIDw+HhYUF3n33XZ3jGj16NO7evYsDBw7AwsIC3bp1w7BhwzBz5kz07NnzqTETVUuNzccng3r0crfHyeVyAYDa5W6CIAhlZWVCXFyc0KhRI6FWrVqCl5eXMGnSJKG4uFitn7e3t8ZLnyouv9q8ebNWsWi6/GjHjh1Cq1atBJlMJvj4+Aiff/65sGrVKgGAcPXqVVW/p13uVrFPTcvjl6cdOnRICA8PFxwdHQWZTCY0adJEiIiIEE6ePKnWb+vWrULz5s0Fa2trwd/fX/jhhx8EuVz+1MvdHrVlyxaha9eugrOzs2BpaSnUr19f6Nevn5CcnKzW7/Lly0KfPn0EJycnQSaTCcHBwcKuXbsqxa3p563pMquqLndTKBTChAkTBBcXF8HW1lYIDw8XMjIyKl3uNnPmTCE4OFhwcnISbGxsBD8/P2HWrFlqlzU+frmbIOj/mXr8/3NV8L/L3Z5E08/g/PnzQlhYmGBnZye4uLgIUVFRwtmzZyv9/MrLy4WPP/5YqFevniCRSFTHWfGznjNnTqX9Pf7/4ccffxQACHPnzlXrV1BQIHh7ewsBAQFqP08iQ5EIgh4zboiIiOhfhefYiYiIzAgTOxERkRlhYiciIjIjTOxERERmhImdiIjIjDCxExERmRGTvkGNUqnErVu3YG9vb9BbQBIR0bMhCALu378PDw8Pg96M6XHFxcUoLS3VeztWVlaQyWQGiMh4TDqx37p1q9ITxIiIyPTcuHEDDRo0MMq2i4uLYWNfFyh/oPe23N3dcfXq1X91cjfpxF7xvGsrfzkkFlZP6U1kmjKTv6zpEIiM5n5BAZo28lL9PTeG0tJSoPwBrP3lgD65QlGKrPNrUFpaysRuLBXld4mFFRM7ma2K57sTmbNncjrVUqZXrhAkpjEtzaQTOxERkdYkAPT5AmEiU7mY2ImISBwk0oeLPuubANOIkoiIiLTCETsREYmDRKJnKd40avFM7EREJA4sxRMREZGp4YidiIjEgaV4IiIic6JnKd5EitymESURERFphSN2IiISB5biiYiIzAhnxRMREZGp4YidiIjEgaV4IiIiMyKSUjwTOxERiYNIRuym8fWDiIiItMIROxERiQNL8URERGZEItEzsbMUT0RERM8YR+xERCQOUsnDRZ/1TQATOxERiYNIzrGbRpRERESkFY7YiYhIHERyHTsTOxERiQNL8URERGRqOGInIiJxYCmeiIjIjIikFM/ETkRE4iCSEbtpfP0gIiIirXDETkRE4sBSPBERkRlhKZ6IiIhMDUfsREQkEnqW4k1kLMzETkRE4sBSPBEREZkajtiJiEgcJBI9Z8WbxoidiZ2IiMRBJJe7mUaUREREpBWO2ImISBxEMnmOiZ2IiMRBJKV4JnYiIhIHkYzYTePrBxEREWmFI3YiIhIHluKJiIjMCEvxREREZGo4YiciIlGQSCSQiGDEzsRORESiIJbEzlI8ERGRGeGInYiIxEHyv0Wf9U0AEzsREYkCS/FERERkcjhiJyIiURDLiJ2JnYiIRIGJnYiIyIyIJbHzHDsREZEZ4YidiIjEgZe7ERERmQ+W4omIiMjkcMRORESi8PCprfqM2A0XizExsRMRkShIoGcp3kQyO0vxREREZoQjdiIiEgVOniMiIjInEgMs1ZCQkAAfHx/IZDKEhIQgNTX1if0XLFgAX19f2NjYwMvLC2PGjEFxcbHW+2NiJyIiMpKNGzciOjoasbGxSEtLQ0BAAMLDw5GTk6Ox/3fffYeJEyciNjYWFy5cwDfffIONGzdi8uTJWu+TiZ2IiMThf6X46i7VKcXPmzcPUVFRiIyMhL+/P5YtWwZbW1usWrVKY/9jx47hlVdewYABA+Dj44OuXbuif//+Tx3lP4qJnYiIREGfpP7o+fmCggK1paSkROP+SktLcerUKYSFhanapFIpwsLCkJKSonGdNm3a4NSpU6pEfuXKFezZswevv/661sfJyXNERCQK+k6eq1jXy8tLrT02NhbTp0+v1D83NxcKhQJubm5q7W5ubrh48aLGfQwYMAC5ublo27YtBEFAeXk5PvzwQ51K8UzsREREOrhx4wYcHBxUr62trQ227eTkZMyePRtfffUVQkJCkJGRgdGjR2PGjBmIiYnRahtM7EREJA4GegiMg4ODWmKviouLCywsLJCdna3Wnp2dDXd3d43rxMTEYPDgwRg2bBgAoGXLligqKsL777+PKVOmQCp9+hl0nmMnIiJRMNQ5dm1ZWVkhKCgISUlJqjalUomkpCSEhoZqXOfBgweVkreFhQUAQBAErfbLETsREZGRREdHQy6Xo3Xr1ggODsaCBQtQVFSEyMhIAMCQIUPg6emJ+Ph4AED37t0xb948vPDCC6pSfExMDLp3765K8E/DxE5ERKJgqMlzuujXrx/u3LmDadOmISsrC4GBgdi7d69qQl1mZqbaCH3q1KmQSCSYOnUqbt68iXr16qF79+6YNWuW9nEK2o7t/4UKCgrg6OgI65ZRkFhY1XQ4REbx13+W1HQIREZTUFAAt7qOyM/P1+q8dXX34ejoCFf5WkitbKu9HWXpA+SsGWLUWA2B59iJiIjMCEvxREQkCjVRiq8JTOxERCQOBrrc7d+OpXgiIiIzwhE7ERGJAkvxREREZoSJnYiIyIyIJbHzHDsREZEZ4YidiIjEQSSz4pnYiYhIFFiKJyIiIpPDxC5yH/Rtj4u74/DX8fk4vHYcWj/vXWVfS0spJr3fDed2xOKv4/NxYuNEdGnTXK2PVCrBtOFv4MKu6chLmYdzO2IxMaqbsQ+DqErLvkqAb1MfONnJ0K5NCP6TmvrE/lu3bEZACz842cnQOrAl9v60R/VeWVkZpkyagNaBLVHXsTYaNfTA0IghuHXrlrEPgwzgWT+2tab8KxJ7QkICfHx8IJPJEBISgtSn/OKRYfTp+iI+H/sWZn39E0IHfI7fLt3Ejq9GoF4dO439pw/vjmG92yL6i814ofdMrNxyFBvnRiHAt4Gqz9iILojq0w5jPtuMwLdnYuqiHxEtD8Pw/h2e1WERqWzetBETxkdjytRYpKSmoVWrAPR4Ixw5OTka+6ccOwb5oP6QRw7F8f+cRveevdC3dy+c++9/ATx8VvaZ02mYOCUGKalp2LDpB1y6lI533urxLA+LqkkCPRO7iZxkr/HEvnHjRkRHRyM2NhZpaWkICAhAeHjVv3hkOKMGdcbqH45h3Y7juHglCx/P2oC/i0sh7xWqsf+AN4PxxTc/Y9/R87h28y5WbD6Kfb+ex+jBnVV9Xg5ojF2//Ia9R88h83Yeth04g6TjF59YCSAylkUL5iFyaBSGRESiub8/Fn+1DDa2tliTuEpj/4QlC9E1vBuix46HX/PmiI2bgcAXXsSyrx4+Yc/R0RG79+5Hn3f6opmvL0JefhnzFy5BWtopZGZmPstDI6pSjSf2efPmISoqCpGRkfD398eyZctga2uLVas0/+KRYdSytMALzb1w8ES6qk0QBBw8kY7gVo00rmNVyxLFpWVqbX8Xl6LNC01Ur4+fvYJOwb5o2tAVANCymSdCAxvj51/PG+EoiKpWWlqK02mn0PnVMFWbVCpF585hSD2eonGdE8dT0KlzmFpbl67hOFFFfwAoKMiHRCKBk5OTQeIm4xFLKb5GZ8WXlpbi1KlTmDRpkqpNKpUiLCwMKSlV/yKR/lzq2MHS0gI5effV2nPuFsDXx03jOgdSLmDUoM44mpaBKzdy0SnYFz07B8LC4p8P+5er98PBToaz26ZCoRBgYSFBbMIubPjppFGPh+hxubm5UCgUcHVV/zy7urkhPf2ixnWys7Lg6vZYf1c3ZGdnaexfXFyMqZMmoG+//v/q53PT//ByN+Or+MVze+wXyc3NDRcvVv7FKykpQUlJiep1QUGB0WOkf4ybswVfxfTH2R9iIAgCrvyZi7U7jkPe82VVnz5dX8S7r72EiMlrcP7ybbTy9cSccX1w+04+1u88UYPRExlWWVkZBvXvC0EQsChhaU2HQ6RiUtexx8fHIy4urqbDMAu5fxWivFwBV2d7tXbXug7Iuqv5C1PuX4XoG70C1laWqOtYG7fu5GPmqJ64evOuqs/sT3rhy9X7sXnfKQDAuYxbaFjfGeMjuzCx0zPl4uICCwsL5ORkq7XnZGfD3d1d4zpu7u7IyX6sf0423NzU+5eVlWFg/77IvH4dP+0/yNG6ieB17M9AxS9e9mO/SNlV/OJNmjQJ+fn5quXGjRvPKlSzU1auwOkLN9ApxFfVJpFI0Cm4GVJ/u/rEdUtKy3HrTj4sLaXo9WogdiX/pnrPRmYFpaBU669QCpBKa3w6B4mMlZUVXngxCIcOJqnalEolDh1KQvDLmieIhrwciuRDSWptSQf2I+SR/hVJ/XLGH9i97wDq1q1rnAMgg+M59mfAysoKQUFBSEpKQq9evQA8/MVLSkrCyJEjK/W3traGtbX1M47SfC369iBWfDoYp85n4uR/r2HkgE6wtbHG2h+PAwBWzhiMWzn5mLZ4BwDgpRbe8HB1wtn0P+Hp6oQpH7wOqVSCeYkHVNvcc/h3TBgajhu3/8L5y7cR6NcAowZ1wtrtx2vkGEncRn0Sjaj35AgKao3WLwVjyaIFeFBUhCHySADA0Igh8PD0xIxZ8QCAESNHo+urHbBg/ly89tob2LxpA9JOnUTC0uUAHib1Af364PTpNPywfRcUCgWysh6ef3d2doaVlVXNHChpRSJ5uOizvimo8VJ8dHQ05HI5WrdujeDgYCxYsABFRUWIjIys6dDM3paf0+BSxw7TPnoDbnXt8Vv6TfQckaCaUOfl7gylUlD1t7auhdgRb6KRpwsKH5Rg36/nMDRmLfIL/1b1if58M2KHv4mFk/uhXh073L6Tj2+2/IrZy3965sdH9E7ffsi9cwefxk1DdlYWWgUE4sdde1Xzem7cyFSrJoW2aYPEdd8hLnYqYqdORtPnnsOmrdvxfIsWAIBbN29i186HX3RDWgeq7WvfgUNo36HjMzkuoieRCIIgPL2bcS1ZsgRz5sxBVlYWAgMDsWjRIoSEhDx1vYKCAjg6OsK6ZRQkFvymTObpr/8sqekQiIymoKAAbnUdkZ+fb7S5ChW5ovHHWyC1rl3t7ShLinBlcR+jxmoINT5iB4CRI0dqLL0TEREZjJ6leFO53I0zmoiIiMzIv2LETkREZGxiudyNiZ2IiERBLLPiWYonIiIyIxyxExGRKEilEkil1R92C3qs+ywxsRMRkSiwFE9EREQmhyN2IiISBc6KJyIiMiNiKcUzsRMRkSiIZcTOc+xERERmhCN2IiISBbGM2JnYiYhIFMRyjp2leCIiIjPCETsREYmCBHqW4k3kua1M7EREJAosxRMREZHJ4YidiIhEgbPiiYiIzAhL8URERGRyOGInIiJRYCmeiIjIjIilFM/ETkREoiCWETvPsRMREZkRjtiJiEgc9CzFm8iN55jYiYhIHFiKJyIiIpPDETsREYkCZ8UTERGZEZbiiYiIyORwxE5ERKLAUjwREZEZYSmeiIiITA5H7EREJApiGbEzsRMRkSjwHDsREZEZEcuInefYiYiIzAhH7EREJAosxRMREZkRluKJiIjI5HDETkREoiCBnqV4g0ViXEzsREQkClKJBFI9Mrs+6z5LLMUTERGZESZ2IiIShYpZ8fos1ZGQkAAfHx/IZDKEhIQgNTX1if3v3buHESNGoH79+rC2tkazZs2wZ88erffHUjwREYlCTcyK37hxI6Kjo7Fs2TKEhIRgwYIFCA8PR3p6OlxdXSv1Ly0tRZcuXeDq6ootW7bA09MT169fh5OTk9b7ZGInIiJRkEoeLvqsr6t58+YhKioKkZGRAIBly5Zh9+7dWLVqFSZOnFip/6pVq5CXl4djx46hVq1aAAAfHx/d4tQ9TCIiIvEqKChQW0pKSjT2Ky0txalTpxAWFqZqk0qlCAsLQ0pKisZ1duzYgdDQUIwYMQJubm5o0aIFZs+eDYVCoXV8TOxERCQOkn/K8dVZKq538/LygqOjo2qJj4/XuLvc3FwoFAq4ubmptbu5uSErK0vjOleuXMGWLVugUCiwZ88exMTEYO7cuZg5c6bWh8lSPBERiYKhbil748YNODg4qNqtra31jOwfSqUSrq6uWL58OSwsLBAUFISbN29izpw5iI2N1WobTOxEREQ6cHBwUEvsVXFxcYGFhQWys7PV2rOzs+Hu7q5xnfr166NWrVqwsLBQtTVv3hxZWVkoLS2FlZXVU/fLUjwREYmCxAD/dGFlZYWgoCAkJSWp2pRKJZKSkhAaGqpxnVdeeQUZGRlQKpWqtkuXLqF+/fpaJXWAiZ2IiESiYla8PouuoqOjsWLFCqxZswYXLlzARx99hKKiItUs+SFDhmDSpEmq/h999BHy8vIwevRoXLp0Cbt378bs2bMxYsQIrffJUjwREZGR9OvXD3fu3MG0adOQlZWFwMBA7N27VzWhLjMzE1LpP2NsLy8v7Nu3D2PGjEGrVq3g6emJ0aNHY8KECVrvk4mdiIhEoaYe2zpy5EiMHDlS43vJycmV2kJDQ3H8+PFq7QvQMrHv2LFD6w326NGj2sEQEREZi6Fmxf/baZXYe/XqpdXGJBKJThfRExERkWFpldgfnZ1HRERkisTy2Fa9zrEXFxdDJpMZKhYiIiKjEUspXufL3RQKBWbMmAFPT0/Y2dnhypUrAICYmBh88803Bg+QiIjIEPS5nay+E++eJZ0T+6xZs5CYmIgvvvhC7WL5Fi1aYOXKlQYNjoiIiHSjc2Jfu3Ytli9fjoEDB6rd8i4gIAAXL140aHBERESGUlGK12cxBTqfY7958yaaNm1aqV2pVKKsrMwgQRERERmaWCbP6Txi9/f3x5EjRyq1b9myBS+88IJBgiIiIqLq0XnEPm3aNMjlcty8eRNKpRI//PAD0tPTsXbtWuzatcsYMRIREelNAuj4GJfK65sCnUfsPXv2xM6dO3HgwAHUrl0b06ZNw4ULF7Bz50506dLFGDESERHpTSyz4qt1HXu7du2wf/9+Q8dCREREeqr2DWpOnjyJCxcuAHh43j0oKMhgQRERERladR+9+uj6pkDnxP7nn3+if//++PXXX+Hk5AQAuHfvHtq0aYMNGzagQYMGho6RiIhIbzX1dLdnTedz7MOGDUNZWRkuXLiAvLw85OXl4cKFC1AqlRg2bJgxYiQiIiIt6Txi/+WXX3Ds2DH4+vqq2nx9fbF48WK0a9fOoMEREREZkokMuvWic2L38vLSeCMahUIBDw8PgwRFRERkaCzFV2HOnDn4+OOPcfLkSVXbyZMnMXr0aHz55ZcGDY6IiMhQKibP6bOYAq1G7HXq1FH7plJUVISQkBBYWj5cvby8HJaWlnjvvffQq1cvowRKRERET6dVYl+wYIGRwyAiIjIusZTitUrscrnc2HEQEREZlVhuKVvtG9QAQHFxMUpLS9XaHBwc9AqIiIiIqk/nxF5UVIQJEyZg06ZNuHv3bqX3FQqFQQIjIiIyJD62tQr/93//h4MHD2Lp0qWwtrbGypUrERcXBw8PD6xdu9YYMRIREelNItF/MQU6j9h37tyJtWvXomPHjoiMjES7du3QtGlTeHt7Y/369Rg4cKAx4iQiIiIt6Dxiz8vLQ+PGjQE8PJ+el5cHAGjbti0OHz5s2OiIiIgMRCyPbdU5sTdu3BhXr14FAPj5+WHTpk0AHo7kKx4KQ0RE9G8jllK8zok9MjISZ8+eBQBMnDgRCQkJkMlkGDNmDMaPH2/wAImIiEh7Op9jHzNmjOq/w8LCcPHiRZw6dQpNmzZFq1atDBocERGRoYhlVrxe17EDgLe3N7y9vQ0RCxERkdHoW043kbyuXWJftGiR1hscNWpUtYMhIiIyFt5S9hHz58/XamMSiYSJnYiIqAZpldgrZsH/W2Umf8lb2ZLZqvPSyJoOgchoBEXp0zsZiBTVmDH+2PqmQO9z7ERERKZALKV4U/kCQkRERFrgiJ2IiERBIgGknBVPRERkHqR6JnZ91n2WWIonIiIyI9VK7EeOHMGgQYMQGhqKmzdvAgDWrVuHo0ePGjQ4IiIiQ+FDYKqwdetWhIeHw8bGBqdPn0ZJSQkAID8/H7NnzzZ4gERERIZQUYrXZzEFOif2mTNnYtmyZVixYgVq1aqlan/llVeQlpZm0OCIiIhINzpPnktPT0f79u0rtTs6OuLevXuGiImIiMjgxHKveJ1H7O7u7sjIyKjUfvToUTRu3NggQRERERlaxdPd9FlMgc6JPSoqCqNHj8aJEycgkUhw69YtrF+/HuPGjcNHH31kjBiJiIj0JjXAYgp0LsVPnDgRSqUSr776Kh48eID27dvD2toa48aNw8cff2yMGImIiEhLOid2iUSCKVOmYPz48cjIyEBhYSH8/f1hZ2dnjPiIiIgMQizn2Kt95zkrKyv4+/sbMhYiIiKjkUK/8+RSmEZm1zmxd+rU6YkX6R88eFCvgIiIiKj6dE7sgYGBaq/Lyspw5swZ/Pe//4VcLjdUXERERAbFUnwV5s+fr7F9+vTpKCws1DsgIiIiY+BDYHQ0aNAgrFq1ylCbIyIiomow2GNbU1JSIJPJDLU5IiIig3r4PPbqD7vNthT/9ttvq70WBAG3b9/GyZMnERMTY7DAiIiIDInn2Kvg6Oio9loqlcLX1xeffvopunbtarDAiIiISHc6JXaFQoHIyEi0bNkSderUMVZMREREBsfJcxpYWFiga9eufIobERGZHIkB/pkCnWfFt2jRAleuXDFGLEREREZTMWLXZzEFOif2mTNnYty4cdi1axdu376NgoICtYWIiIhqjtbn2D/99FOMHTsWr7/+OgCgR48eareWFQQBEokECoXC8FESERHpSSzn2LVO7HFxcfjwww9x6NAhY8ZDRERkFBKJ5InPOtFmfVOgdWIXBAEA0KFDB6MFQ0RERPrR6XI3U/m2QkRE9DiW4jVo1qzZU5N7Xl6eXgEREREZA+88p0FcXFylO88RERHRv4dOif3dd9+Fq6ursWIhIiIyGqlEotdDYPRZ91nSOrHz/DoREZkysZxj1/oGNRWz4omIiEh7CQkJ8PHxgUwmQ0hICFJTU7Vab8OGDZBIJOjVq5dO+9M6sSuVSpbhiYjIdEn+mUBXnaU6t4rfuHEjoqOjERsbi7S0NAQEBCA8PBw5OTlPXO/atWsYN24c2rVrp/M+db6lLBERkSmSQqL3oqt58+YhKioKkZGR8Pf3x7Jly2Bra4tVq1ZVuY5CocDAgQMRFxeHxo0bV+M4iYiIRECf0fqjl8o9/oyUkpISjfsrLS3FqVOnEBYWpmqTSqUICwtDSkpKlXF++umncHV1xdChQ6t1nEzsREREOvDy8oKjo6NqiY+P19gvNzcXCoUCbm5uau1ubm7IysrSuM7Ro0fxzTffYMWKFdWOT6fL3YiIiEyVoWbF37hxAw4ODqp2a2trPSN76P79+xg8eDBWrFgBFxeXam+HiZ2IiETBUNexOzg4qCX2qri4uMDCwgLZ2dlq7dnZ2XB3d6/U//Lly7h27Rq6d++ualMqlQAAS0tLpKeno0mTJk+P86k9iIiISGdWVlYICgpCUlKSqk2pVCIpKQmhoaGV+vv5+eH333/HmTNnVEuPHj3QqVMnnDlzBl5eXlrtlyN2IiIShZq4V3x0dDTkcjlat26N4OBgLFiwAEVFRYiMjAQADBkyBJ6enoiPj4dMJkOLFi3U1ndycgKASu1PwsRORESiIIWepfhqXO7Wr18/3LlzB9OmTUNWVhYCAwOxd+9e1YS6zMxMSKWGLZ4zsRMRERnRyJEjMXLkSI3vJScnP3HdxMREnffHxE5ERKLAx7YSERGZESn0mzFuKrPNTSVOIiIi0gJH7EREJAoSiUSvR5CbyuPLmdiJiEgUqvmANrX1TQETOxERiYKh7jz3b8dz7ERERGaEI3YiIhIN0xhz64eJnYiIREEs17GzFE9ERGRGOGInIiJR4OVuREREZoR3niMiIiKTwxE7ERGJAkvxREREZkQsd55jKZ6IiMiMcMRORESiwFI8ERGRGRHLrHgmdiIiEgWxjNhN5QsIERERaYEjdiIiEgWxzIpnYiciIlHgQ2CIiIjI5HDETkREoiCFBFI9Cur6rPssMbETEZEosBRPREREJocjdiIiEgXJ//7ps74pYGInIiJRYCmeiIiITA5H7EREJAoSPWfFsxRPRET0LyKWUjwTOxERiYJYEjvPsRMREZkRjtiJiEgUeLkbERGRGZFKHi76rG8KWIonIiIyIxyxExGRKLAUT0REZEY4K56IiIhMDkfsREQkChLoV043kQE7EzsREYkDZ8UTERGRyWFiF7llXyXAt6kPnOxkaNcmBP9JTX1i/61bNiOghR+c7GRoHdgSe3/ao3qvrKwMUyZNQOvAlqjrWBuNGnpgaMQQ3Lp1y9iHQVSlD/q2x8Xdcfjr+HwcXjsOrZ/3rrKvpaUUk97vhnM7YvHX8fk4sXEiurRprtZHKpVg2vA3cGHXdOSlzMO5HbGYGNXN2IdBBiAxwD9TUKOJ/fDhw+jevTs8PDwgkUiwffv2mgxHdDZv2ogJ46MxZWosUlLT0KpVAHq8EY6cnByN/VOOHYN8UH/II4fi+H9Oo3vPXujbuxfO/fe/AIAHDx7gzOk0TJwSg5TUNGzY9AMuXUrHO2/1eJaHRaTSp+uL+HzsW5j19U8IHfA5frt0Ezu+GoF6dew09p8+vDuG9W6L6C8244XeM7Fyy1FsnBuFAN8Gqj5jI7ogqk87jPlsMwLfnompi35EtDwMw/t3eFaHRdVUMSten8UU1GhiLyoqQkBAABISEmoyDNFatGAeIodGYUhEJJr7+2PxV8tgY2uLNYmrNPZPWLIQXcO7IXrsePg1b47YuBkIfOFFLPtqCQDA0dERu/fuR593+qKZry9CXn4Z8xcuQVraKWRmZj7LQyMCAIwa1BmrfziGdTuO4+KVLHw8awP+Li6FvFeoxv4D3gzGF9/8jH1Hz+PazbtYsfko9v16HqMHd1b1eTmgMXb98hv2Hj2HzNt52HbgDJKOX3xiJYD+HSQGWExBjSb21157DTNnzsRbb71Vk2GIUmlpKU6nnULnV8NUbVKpFJ07hyH1eIrGdU4cT0GnzmFqbV26huNEFf0BoKAgHxKJBE5OTgaJm0hbtSwt8EJzLxw8ka5qEwQBB0+kI7hVI43rWNWyRHFpmVrb38WlaPNCE9Xr42evoFOwL5o2dAUAtGzmidDAxvj51/NGOAoi3ZnUrPiSkhKUlJSoXhcUFNRgNKYtNzcXCoUCrq5uau2ubm5IT7+ocZ3srCy4uj3W39UN2dlZGvsXFxdj6qQJ6NuvPxwcHAwTOJGWXOrYwdLSAjl599Xac+4WwNfHTeM6B1IuYNSgzjialoErN3LRKdgXPTsHwsLin7Hal6v3w8FOhrPbpkKhEGBhIUFswi5s+OmkUY+H9CeFBFI96ulSExmzm1Rij4+PR1xcXE2HQVooKyvDoP59IQgCFiUsrelwiLQybs4WfBXTH2d/iIEgCLjyZy7W7jgOec+XVX36dH0R7772EiImr8H5y7fRytcTc8b1we07+Vi/80QNRk9Po2853TTSuokl9kmTJiE6Olr1uqCgAF5eXjUYkelycXGBhYUFcnKy1dpzsrPh7u6ucR03d3fkZD/WPycbbm7q/cvKyjCwf19kXr+On/Yf5GidakTuX4UoL1fA1dlerd21rgOy7mqu9uX+VYi+0StgbWWJuo61cetOPmaO6omrN++q+sz+pBe+XL0fm/edAgCcy7iFhvWdMT6yCxM7/SuY1OVu1tbWcHBwUFuoeqysrPDCi0E4dDBJ1aZUKnHoUBKCX9Y8sSjk5VAkH0pSa0s6sB8hj/SvSOqXM/7A7n0HULduXeMcANFTlJUrcPrCDXQK8VW1SSQSdApuhtTfrj5x3ZLScty6kw9LSyl6vRqIXcm/qd6zkVlBKSjV+iuUAqRSk/pzKk4imT1nUiN2MqxRn0Qj6j05goJao/VLwViyaAEeFBVhiDwSADA0Ygg8PD0xY1Y8AGDEyNHo+moHLJg/F6+99gY2b9qAtFMnkbB0OYCHSX1Avz44fToNP2zfBYVCgaysh+ffnZ2dYWVlVTMHSqK16NuDWPHpYJw6n4mT/72GkQM6wdbGGmt/PA4AWDljMG7l5GPa4h0AgJdaeMPD1Qln0/+Ep6sTpnzwOqRSCeYlHlBtc8/h3zFhaDhu3P4L5y/fRqBfA4wa1Alrtx+vkWMk7fHpbs9AYWEhMjIyVK+vXr2KM2fOwNnZGQ0bNqzByMThnb79kHvnDj6Nm4bsrCy0CgjEj7v2wu1/E+Ru3MhUG4WEtmmDxHXfIS52KmKnTkbT557Dpq3b8XyLFgCAWzdvYtfOh38gQ1oHqu1r34FDaN+h4zM5LqIKW35Og0sdO0z76A241bXHb+k30XNEgmpCnZe7M5RKQdXf2roWYke8iUaeLih8UIJ9v57D0Ji1yC/8W9Un+vPNiB3+JhZO7od6dexw+04+vtnyK2Yv/+mZHx+RJhJBEISndzOO5ORkdOrUqVK7XC5HYmLiU9cvKCiAo6Mjsu/msyxPZqvOSyNrOgQioxEUpSj5fQXy8433d7wiVySdyYSdffX3UXi/AK8GNjRqrIZQoyP2jh07oga/VxARkYiIZVY8Z3sQERGZEU6eIyIicRDJkJ2JnYiIRIGz4omIiMyIvk9o49PdiIiI6JnjiJ2IiERBJKfYmdiJiEgkRJLZWYonIiIyIxyxExGRKHBWPBERkRnhrHgiIiIyORyxExGRKIhk7hxH7EREJBISAyzVkJCQAB8fH8hkMoSEhCA1NbXKvitWrEC7du1Qp04d1KlTB2FhYU/srwkTOxERkZFs3LgR0dHRiI2NRVpaGgICAhAeHo6cnByN/ZOTk9G/f38cOnQIKSkp8PLyQteuXXHz5k2t98nETkREoiAxwD9dzZs3D1FRUYiMjIS/vz+WLVsGW1tbrFq1SmP/9evXY/jw4QgMDISfnx9WrlwJpVKJpKQkrffJxE5ERKJQMStenwUACgoK1JaSkhKN+ystLcWpU6cQFhamapNKpQgLC0NKSopWMT948ABlZWVwdnbW+jiZ2ImISBQMdYrdy8sLjo6OqiU+Pl7j/nJzc6FQKODm5qbW7ubmhqysLK1injBhAjw8PNS+HDwNZ8UTERHp4MaNG3BwcFC9tra2Nsp+PvvsM2zYsAHJycmQyWRar8fETkRE4mCg690cHBzUEntVXFxcYGFhgezsbLX27OxsuLu7P3HdL7/8Ep999hkOHDiAVq1a6RQmS/FERCQKz3rynJWVFYKCgtQmvlVMhAsNDa1yvS+++AIzZszA3r170bp1a52PkyN2IiIiI4mOjoZcLkfr1q0RHByMBQsWoKioCJGRkQCAIUOGwNPTU3We/vPPP8e0adPw3XffwcfHR3Uu3s7ODnZ2dlrtk4mdiIhEoSbuFd+vXz/cuXMH06ZNQ1ZWFgIDA7F3717VhLrMzExIpf8Uz5cuXYrS0lL06dNHbTuxsbGYPn26VvtkYiciIlGoqVvKjhw5EiNHjtT4XnJystrra9euVXMv/+A5diIiIjPCETsREYmDSJ4Cw8RORESiUN3bwj66vilgKZ6IiMiMcMRORESiUBOz4msCEzsREYmCSE6xM7ETEZFIiCSz8xw7ERGRGeGInYiIREEss+KZ2ImISBz0nDxnInmdpXgiIiJzwhE7ERGJgkjmzjGxExGRSIgks7MUT0REZEY4YiciIlHgrHgiIiIzIpZbyrIUT0REZEY4YiciIlEQydw5JnYiIhIJkWR2JnYiIhIFsUye4zl2IiIiM8IROxERiYIEes6KN1gkxsXETkREoiCSU+wsxRMREZkTjtiJiEgUxHKDGiZ2IiISCXEU41mKJyIiMiMcsRMRkSiwFE9ERGRGxFGIZymeiIjIrHDETkREosBSPBERkRkRy73imdiJiEgcRHKSnefYiYiIzAhH7EREJAoiGbAzsRMRkTiIZfIcS/FERERmhCN2IiISBc6KJyIiMiciOcnOUjwREZEZ4YidiIhEQSQDdiZ2IiISB86KJyIiIpPDETsREYmEfrPiTaUYz8RORESiwFI8ERERmRwmdiIiIjPCUjwREYmCWErxTOxERCQKYrmlLEvxREREZoQjdiIiEgWW4omIiMyIWG4py1I8ERGRGeGInYiIxEEkQ3YmdiIiEgXOiiciIiKTwxE7ERGJAmfFExERmRGRnGJnYiciIpEQSWbnOXYiIiIzwhE7ERGJglhmxTOxExGRKHDynAkQBAEAcL+goIYjITIeQVFa0yEQGU3F57vi77kxFeiZK/Rd/1kx6cR+//59AEDTRl41HAkREenj/v37cHR0NMq2rays4O7ujucMkCvc3d1hZWVlgKiMRyI8i69JRqJUKnHr1i3Y29tDYio1EhNXUFAALy8v3LhxAw4ODjUdDpFB8fP97AmCgPv378PDwwNSqfHmcxcXF6O0VP/ql5WVFWQymQEiMh6THrFLpVI0aNCgpsMQJQcHB/7hI7PFz/ezZayR+qNkMtm/PiEbCi93IyIiMiNM7ERERGaEiZ10Ym1tjdjYWFhbW9d0KEQGx883mQOTnjxHRERE6jhiJyIiMiNM7ERERGaEiZ2IiMiMMLETERGZESZ20lpCQgJ8fHwgk8kQEhKC1NTUmg6JyCAOHz6M7t27w8PDAxKJBNu3b6/pkIiqjYmdtLJx40ZER0cjNjYWaWlpCAgIQHh4OHJycmo6NCK9FRUVISAgAAkJCTUdCpHeeLkbaSUkJAQvvfQSlixZAuDhffq9vLzw8ccfY+LEiTUcHZHhSCQSbNu2Db169arpUIiqhSN2eqrS0lKcOnUKYWFhqjapVIqwsDCkpKTUYGRERPQ4JnZ6qtzcXCgUCri5uam1u7m5ISsrq4aiIiIiTZjYiYiIzAgTOz2Vi4sLLCwskJ2drdaenZ0Nd3f3GoqKiIg0YWKnp7KyskJQUBCSkpJUbUqlEklJSQgNDa3ByIiI6HGWNR0AmYbo6GjI5XK0bt0awcHBWLBgAYqKihAZGVnToRHprbCwEBkZGarXV69exZkzZ+Ds7IyGDRvWYGREuuPlbqS1JUuWYM6cOcjKykJgYCAWLVqEkJCQmg6LSG/Jycno1KlTpXa5XI7ExMRnHxCRHpjYiYiIzAjPsRMREZkRJnYiIiIzwsRORERkRpjYiYiIzAgTOxERkRlhYiciIjIjTOxERERmhImdSE8RERFqz+7u2LEjPvnkk2ceR3JyMiQSCe7du1dlH4lEgu3bt2u9zenTpyMwMFCvuK5duwaJRIIzZ87otR0i0g4TO5mliIgISCQSSCQSWFlZoWnTpvj0009RXl5u9H3/8MMPmDFjhlZ9tUnGRES64L3iyWx169YNq1evRklJCfbs2YMRI0agVq1amDRpUqW+paWlsLKyMsh+nZ2dDbIdIqLq4IidzJa1tTXc3d3h7e2Njz76CGFhYdixYweAf8rns2bNgoeHB3x9fQEAN27cQN++feHk5ARnZ2f07NkT165dU21ToVAgOjoaTk5OqFu3Lv7v//4Pj9+V+fFSfElJCSZMmAAvLy9YW1ujadOm+Oabb3Dt2jXV/cnr1KkDiUSCiIgIAA+fnhcfH49GjRrBxsYGAQEB2LJli9p+9uzZg2bNmsHGxgadOnVSi1NbEyZMQLNmzWBra4vGjRsjJiYGZWVllfp9/fXX8PLygq2tLfr27Yv8/Hy191euXInmzZtDJpPBz88PX331lc6xEJFhMLGTaNjY2KC0tFT1OikpCenp6di/fz927dqFsrIyhIeHw97eHkeOHMGvv/4KOzs7dOvWTbXe3LlzkZiYiFWrVuHo0aPIy8vDtm3bnrjfIUOG4Pvvv8eiRYtw4cIFfP3117Czs4OXlxe2bt0KAEhPT8ft27excOFCAEB8fDzWrl2LZcuW4dy5cxgzZgwGDRqEX375BcDDLyBvv/02unfvjjNnzmDYsGGYOHGizj8Te3t7JCYm4vz581i4cCFWrFiB+fPnq/XJyMjApk2bsHPnTuzduxenT5/G8OHDVe+vX78e06ZNw6xZs3DhwgXMnj0bMTExWLNmjc7xEJEBCERmSC6XCz179hQEQRCUSqWwf/9+wdraWhg3bpzqfTc3N6GkpES1zrp16wRfX19BqVSq2kpKSgQbGxth3759giAIQv369YUvvvhC9X5ZWZnQoEED1b4EQRA6dOggjB49WhAEQUhPTxcACPv379cY56FDhwQAwl9//aVqKy4uFmxtbYVjx46p9R06dKjQv39/QRAEYdKkSYK/v7/a+xMmTKi0rccBELZt21bl+3PmzBGCgoJUr2NjYwULCwvhzz//VLX99NNPglQqFW7fvi0IgiA0adJE+O6779S2M2PGDCE0NFQQBEG4evWqAEA4ffp0lfslIsPhOXYyW7t27YKdnR3KysqgVCoxYMAATJ8+XfV+y5Yt1c6rnz17FhkZGbC3t1fbTnFxMS5fvoz8/Hzcvn1b7VG1lpaWaN26daVyfIUzZ87AwsICHTp00DrujIwMPHjwAF26dFFrLy0txQsvvAAAuHDhQqVH5oaGhmq9jwobN27EokWLcPnyZRQWFqK8vBwODg5qfRo2bAhPT0+1/SiVSqSnp8Pe3h6XL1/G0KFDERUVpepTXl4OR0dHneMhIv0xsZPZ6tSpE5YuXQorKyt4eHjA0lL94167dm2114WFhQgKCsL69esrbatevXrVisHGxkbndQoLCwEAu3fvVkuowMN5A4aSkpKCgQMHIi4uDuHh4XB0dMSGDRswd+5cnWNdsWJFpS8aFhYWBouViLTHxE5mq3bt2mjatKnW/V988UVs3LgRrq6ulUatFerXr48TJ06gffv2AB6OTE+dOoUXX3xRY/+WLVtCqVTil19+QVhYWKX3KyoGCoVC1ebv7w9ra2tkZmZWOdJv3ry5aiJghePHjz/9IB9x7NgxeHt7Y8qUKaq269evV+qXmZmJW7duwcPDQ7UfqVQKX19fuLm5wcPDA1euXMHAgQN12j8RGQcnzxH9z8CBA+Hi4oKePXviyJEjuHr1KpKTkzFq1Cj8+eefAIDRo0fjs88+w/bt23Hx4kUMHz78ideg+/j4QC6X47333sP27dtV29y0aRMAwNvbGxKJBLt27cKdO3dQWFgIe3t7jBs3DmPGjMGaNWtw+fJlpKWlYfHixaoJaR9++CH++OMPjB8/Hunp6fjuu++QmJio0/E+99xzyMzMxIYNG3D58mUsWrRI40RAmUwGuVyOs2fP4siRIxg1ahT69u0Ld3d3AEBcXBzi4+OxaNEiXLp0Cb///jtWr16NefPm6RQPERkGEzvR/9ja2uLw4cNo2LAh3n77bTRv3hxDhw5FcXGxagQ/duxYDB48GHK5HKGhobC3t8dbb731xO0uXboUffr0wfDhw+Hn54eoqCgUFRUBADw9PREXF4eJEyfCzc0NI0eOBADMmDEDMTExiI+PR/PmzdGtWzfs3r0bjRo1AvDwvPfWrVuxfft2BAQEYNmyZZg9e7ZOx9ujRw+MGTMGI0eORGBgII4dO4aYmJhK/Zo2bYq3334br7/+Orp27YpWrVqpXc42bNgwrFy5EqtXr0bLli3RoUMHJCYmqmIlomdLIlQ164eIiIhMDkfsREREZoSJnYiIyIwwsRMREZkRJnYiIiIzwsRORERkRpjYiYiIzAgTOxERkRlhYiciIjIjTOxERERmhImdiIjIjDCxExERmREmdiIiIjPy/zUpEFWpJbyoAAAAAElFTkSuQmCC", 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", 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", 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", 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", + "image/png": 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", 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" ] @@ -254,7 +256,7 @@ }, { "cell_type": "markdown", - "id": "c5cbe0e8", + "id": "2bf79506", "metadata": { "editable": true }, @@ -270,7 +272,7 @@ }, { "cell_type": "markdown", - "id": "19bd77f2", + "id": "a80dc181", "metadata": { "editable": true }, @@ -281,7 +283,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "84f1af2e", + "id": "61ef464c", "metadata": { "collapsed": false, "editable": true @@ -296,7 +298,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "6c01d900", + "id": "ab3892d7", "metadata": { "collapsed": false, "editable": true @@ -325,7 +327,7 @@ }, { "cell_type": "markdown", - "id": "896072f6", + "id": "2d4ac770", "metadata": { "editable": true }, @@ -345,7 +347,7 @@ }, { "cell_type": "markdown", - "id": "babc9c9c", + "id": "20f3e403", "metadata": { "editable": true }, @@ -359,7 +361,7 @@ }, { "cell_type": "markdown", - "id": "2ed28584", + "id": "ea52430d", "metadata": { "editable": true }, @@ -371,7 +373,7 @@ }, { "cell_type": "markdown", - "id": "5b5b9631", + "id": "86cb9f50", "metadata": { "editable": true }, @@ -388,7 +390,7 @@ }, { "cell_type": "markdown", - "id": "7b6c43ec", + "id": "00ca4f95", "metadata": { "editable": true }, @@ -400,7 +402,7 @@ }, { "cell_type": "markdown", - "id": "3962039f", + "id": "ddc56700", "metadata": { "editable": true }, @@ -414,7 +416,7 @@ }, { "cell_type": "markdown", - "id": "ee5f259c", + "id": "eb17dae2", "metadata": { "editable": true }, @@ -426,7 +428,7 @@ }, { "cell_type": "markdown", - "id": "079d8ee3", + "id": "9ae66e77", "metadata": { "editable": true }, @@ -439,7 +441,7 @@ }, { "cell_type": "markdown", - "id": "11f821a2", + "id": "ae777b9c", "metadata": { "editable": true }, @@ -451,7 +453,7 @@ }, { "cell_type": "markdown", - "id": "60990073", + "id": "8b3174c5", "metadata": { "editable": true }, @@ -461,7 +463,7 @@ }, { "cell_type": "markdown", - "id": "4d7195ab", + "id": "b4590742", "metadata": { "editable": true }, @@ -489,7 +491,7 @@ }, { "cell_type": "markdown", - "id": "4bee726a", + "id": "d6f866fd", "metadata": { "editable": true }, @@ -505,7 +507,7 @@ }, { "cell_type": "markdown", - "id": "1190b9a0", + "id": "b190e045", "metadata": { "editable": true }, @@ -517,7 +519,7 @@ }, { "cell_type": "markdown", - "id": "2702211b", + "id": "df876b5f", "metadata": { "editable": true }, @@ -528,7 +530,7 @@ }, { "cell_type": "markdown", - "id": "1b1d9984", + "id": "33756540", "metadata": { "editable": true }, @@ -540,7 +542,7 @@ }, { "cell_type": "markdown", - "id": "cdf7e56a", + "id": "9b588d4a", "metadata": { "editable": true }, @@ -550,7 +552,7 @@ }, { "cell_type": "markdown", - "id": "ac6f3b86", + "id": "5f289c53", "metadata": { "editable": true }, @@ -562,7 +564,7 @@ }, { "cell_type": "markdown", - "id": "faf6fb5f", + "id": "23f5d161", "metadata": { "editable": true }, @@ -572,7 +574,7 @@ }, { "cell_type": "markdown", - "id": "969f86ed", + "id": "fd058392", "metadata": { "editable": true }, @@ -584,7 +586,7 @@ }, { "cell_type": "markdown", - "id": "a98a541c", + "id": "5a8517bd", "metadata": { "editable": true }, @@ -594,7 +596,7 @@ }, { "cell_type": "markdown", - "id": "d8be5438", + "id": "94eec67e", "metadata": { "editable": true }, @@ -606,7 +608,7 @@ }, { "cell_type": "markdown", - "id": "932f43c5", + "id": "d4936ab1", "metadata": { "editable": true }, @@ -620,7 +622,7 @@ }, { "cell_type": "markdown", - "id": "24630eb9", + "id": "7bafaad4", "metadata": { "editable": true }, @@ -636,7 +638,7 @@ }, { "cell_type": "markdown", - "id": "9a7fa4b6", + "id": "b91cc27d", "metadata": { "editable": true }, @@ -648,7 +650,7 @@ }, { "cell_type": "markdown", - "id": "7ead62b2", + "id": "ee926ea4", "metadata": { "editable": true }, @@ -664,7 +666,7 @@ }, { "cell_type": "markdown", - "id": "91639c49", + "id": "88be0c12", "metadata": { "editable": true }, @@ -676,7 +678,7 @@ }, { "cell_type": "markdown", - "id": "e43865c2", + "id": "a16704d1", "metadata": { "editable": true }, @@ -686,7 +688,7 @@ }, { "cell_type": "markdown", - "id": "1884c219", + "id": "1667ae43", "metadata": { "editable": true }, @@ -698,7 +700,7 @@ }, { "cell_type": "markdown", - "id": "bbb1cdbb", + "id": "21368026", "metadata": { "editable": true }, @@ -710,7 +712,7 @@ }, { "cell_type": "markdown", - "id": "7e3590f6", + "id": "3f57f184", "metadata": { "editable": true }, @@ -722,7 +724,7 @@ }, { "cell_type": "markdown", - "id": "2eb8c86b", + "id": "adc38409", "metadata": { "editable": true }, @@ -733,7 +735,7 @@ }, { "cell_type": "markdown", - "id": "e477d15a", + "id": "d4de19b9", "metadata": { "editable": true }, @@ -745,7 +747,7 @@ }, { "cell_type": "markdown", - "id": "b6f01e59", + "id": "9b9b6dcd", "metadata": { "editable": true }, @@ -756,7 +758,7 @@ }, { "cell_type": "markdown", - "id": "71c85471", + "id": "a274021b", "metadata": { "editable": true }, @@ -768,7 +770,7 @@ }, { "cell_type": "markdown", - "id": "2d5be340", + "id": "fe46199f", "metadata": { "editable": true }, @@ -778,7 +780,7 @@ }, { "cell_type": "markdown", - "id": "e426517c", + "id": "913a48d6", "metadata": { "editable": true }, @@ -790,7 +792,7 @@ }, { "cell_type": "markdown", - "id": "c0c7e993", + "id": "e707fd99", "metadata": { "editable": true }, @@ -802,7 +804,7 @@ }, { "cell_type": "markdown", - "id": "7457a096", + "id": "1b749c34", "metadata": { "editable": true }, @@ -814,7 +816,7 @@ }, { "cell_type": "markdown", - "id": "424d5bc9", + "id": "b79b881c", "metadata": { "editable": true }, @@ -826,7 +828,7 @@ }, { "cell_type": "markdown", - "id": "d1fa658b", + "id": "115a36fd", "metadata": { "editable": true }, @@ -836,7 +838,7 @@ }, { "cell_type": "markdown", - "id": "f8fc6b15", + "id": "49086250", "metadata": { "editable": true }, @@ -848,7 +850,7 @@ }, { "cell_type": "markdown", - "id": "9ca82f54", + "id": "a840811a", "metadata": { "editable": true }, @@ -858,7 +860,7 @@ }, { "cell_type": "markdown", - "id": "6c028686", + "id": "d213a918", "metadata": { "editable": true }, @@ -870,7 +872,7 @@ }, { "cell_type": "markdown", - "id": "4007a0ef", + "id": "68a50e61", "metadata": { "editable": true }, @@ -880,7 +882,7 @@ }, { "cell_type": "markdown", - "id": "ba456347", + "id": "b62cf357", "metadata": { "editable": true }, @@ -892,7 +894,7 @@ }, { "cell_type": "markdown", - "id": "c8df4ef3", + "id": "5da2ca5c", "metadata": { "editable": true }, @@ -902,7 +904,7 @@ }, { "cell_type": "markdown", - "id": "da46f9d9", + "id": "803e473e", "metadata": { "editable": true }, @@ -914,7 +916,7 @@ }, { "cell_type": "markdown", - "id": "5c60f30d", + "id": "35e1f9c1", "metadata": { "editable": true }, @@ -924,7 +926,7 @@ }, { "cell_type": "markdown", - "id": "a5bc4dfe", + "id": "3b7c369c", "metadata": { "editable": true }, @@ -936,7 +938,7 @@ }, { "cell_type": "markdown", - "id": "3987cc1e", + "id": "dae26491", "metadata": { "editable": true }, @@ -956,7 +958,7 @@ }, { "cell_type": "markdown", - "id": "b35df09e", + "id": "57668483", "metadata": { "editable": true }, @@ -968,7 +970,7 @@ }, { "cell_type": "markdown", - "id": "5b6a209c", + "id": "5830f7a2", "metadata": { "editable": true }, @@ -978,7 +980,7 @@ }, { "cell_type": "markdown", - "id": "d619a097", + "id": "b933bd20", "metadata": { "editable": true }, @@ -994,7 +996,7 @@ }, { "cell_type": "markdown", - "id": "b7311abb", + "id": "6e8dff87", "metadata": { "editable": true }, @@ -1006,7 +1008,7 @@ }, { "cell_type": "markdown", - "id": "4bfb4209", + "id": "1d6a0fa2", "metadata": { "editable": true }, @@ -1034,7 +1036,7 @@ }, { "cell_type": "markdown", - "id": "4cf4c1f4", + "id": "eacc9804", "metadata": { "editable": true }, @@ -1047,7 +1049,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "453dff54", + "id": "29c9b0a6", "metadata": { "collapsed": false, "editable": true @@ -1103,7 +1105,7 @@ }, { "cell_type": "markdown", - "id": "d6470164", + "id": "f5eaa0fc", "metadata": { "editable": true }, @@ -1114,7 +1116,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "b6e01886", + "id": "1db44432", "metadata": { "collapsed": false, "editable": true @@ -1211,7 +1213,7 @@ }, { "cell_type": "markdown", - "id": "3cede4c9", + "id": "60fbb085", "metadata": { "editable": true }, @@ -1229,7 +1231,7 @@ }, { "cell_type": "markdown", - "id": "94e3b59f", + "id": "cbc6c37e", "metadata": { "editable": true }, @@ -1242,7 +1244,7 @@ }, { "cell_type": "markdown", - "id": "2f000f62", + "id": "675390d3", "metadata": { "editable": true }, @@ -1254,7 +1256,7 @@ }, { "cell_type": "markdown", - "id": "014da5e9", + "id": "ac24e4fb", "metadata": { "editable": true }, @@ -1264,7 +1266,7 @@ }, { "cell_type": "markdown", - "id": "022aee06", + "id": "cfdd6b7f", "metadata": { "editable": true }, @@ -1276,7 +1278,7 @@ }, { "cell_type": "markdown", - "id": "d0d3e174", + "id": "de1524db", "metadata": { "editable": true }, @@ -1286,7 +1288,7 @@ }, { "cell_type": "markdown", - "id": "8af32042", + "id": "506fa7d0", "metadata": { "editable": true }, @@ -1298,7 +1300,7 @@ }, { "cell_type": "markdown", - "id": "4a384d95", + "id": "cc03bbc9", "metadata": { "editable": true }, @@ -1311,7 +1313,7 @@ }, { "cell_type": "markdown", - "id": "bd5b9bb9", + "id": "a1145d71", "metadata": { "editable": true }, @@ -1323,7 +1325,7 @@ }, { "cell_type": "markdown", - "id": "560b3106", + "id": "f9bbe14d", "metadata": { "editable": true }, @@ -1335,7 +1337,7 @@ }, { "cell_type": "markdown", - "id": "4742d327", + "id": "44ca0a6c", "metadata": { "editable": true }, @@ -1347,7 +1349,7 @@ }, { "cell_type": "markdown", - "id": "d160a37f", + "id": "7791881d", "metadata": { "editable": true }, @@ -1357,7 +1359,7 @@ }, { "cell_type": "markdown", - "id": "f3cde07e", + "id": "b3cffde0", "metadata": { "editable": true }, @@ -1369,7 +1371,7 @@ }, { "cell_type": "markdown", - "id": "cf95ad5e", + "id": "842710e4", "metadata": { "editable": true }, @@ -1379,7 +1381,7 @@ }, { "cell_type": "markdown", - "id": "bb614b9d", + "id": "2d0ac22f", "metadata": { "editable": true }, @@ -1395,7 +1397,7 @@ }, { "cell_type": "markdown", - "id": "19e3e8fa", + "id": "8b7ba0b6", "metadata": { "editable": true }, @@ -1407,7 +1409,7 @@ }, { "cell_type": "markdown", - "id": "9330b34b", + "id": "d0ad4149", "metadata": { "editable": true }, @@ -1428,7 +1430,7 @@ }, { "cell_type": "markdown", - "id": "e648539a", + "id": "6efc5e2e", "metadata": { "editable": true }, @@ -1439,7 +1441,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "d12810d3", + "id": "4a6a7ba3", "metadata": { "collapsed": false, "editable": true @@ -1450,30 +1452,30 @@ "output_type": "stream", "text": [ "Max depth: 1\n", - "Error: 0.4203129333425336\n", - "Bias^2: 0.21226966048908316\n", - "Var: 0.20804327285345042\n", - "0.4203129333425336 >= 0.21226966048908316 + 0.20804327285345042 = 0.4203129333425336\n", + "Error: 0.4010613825254484\n", + "Bias^2: 0.2079593417034804\n", + "Var: 0.19310204082196794\n", + "0.4010613825254484 >= 0.2079593417034804 + 0.19310204082196794 = 0.4010613825254483\n", "Max depth: 2\n", - "Error: 0.40767639731018696\n", - "Bias^2: 0.21200998139721822\n", - "Var: 0.19566641591296877\n", - "0.40767639731018696 >= 0.21200998139721822 + 0.19566641591296877 = 0.407676397310187\n", + "Error: 0.4250776117755916\n", + "Bias^2: 0.2080984218270197\n", + "Var: 0.21697918994857185\n", + "0.4250776117755916 >= 0.2080984218270197 + 0.21697918994857185 = 0.42507761177559156\n", "Max depth: 3\n", - "Error: 0.4076774836661818\n", - "Bias^2: 0.2120099429256955\n", - "Var: 0.19566754074048626\n", - "0.4076774836661818 >= 0.2120099429256955 + 0.19566754074048626 = 0.40767748366618173\n", + "Error: 0.4250796355306808\n", + "Bias^2: 0.2080985447081304\n", + "Var: 0.21698109082255032\n", + "0.4250796355306808 >= 0.2080985447081304 + 0.21698109082255032 = 0.42507963553068073\n", "Max depth: 4\n", - "Error: 0.4076774836661818\n", - "Bias^2: 0.2120099429256955\n", - "Var: 0.19566754074048626\n", - "0.4076774836661818 >= 0.2120099429256955 + 0.19566754074048626 = 0.40767748366618173\n", + "Error: 0.4250796355306808\n", + "Bias^2: 0.2080985447081304\n", + "Var: 0.21698109082255038\n", + "0.4250796355306808 >= 0.2080985447081304 + 0.21698109082255038 = 0.4250796355306808\n", "Max depth: 5\n", - "Error: 0.4076774836661816\n", - "Bias^2: 0.2120099429256955\n", - "Var: 0.1956675407404862\n", - "0.4076774836661816 >= 0.2120099429256955 + 0.1956675407404862 = 0.40767748366618173\n" + "Error: 0.42507963553068073\n", + "Bias^2: 0.2080985447081304\n", + "Var: 0.21698109082255032\n", + "0.42507963553068073 >= 0.2080985447081304 + 0.21698109082255032 = 0.42507963553068073\n" ] }, { @@ -1494,7 +1496,7 @@ }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -1548,7 +1550,7 @@ }, { "cell_type": "markdown", - "id": "f3e95eca", + "id": "23c85cdf", "metadata": { "editable": true }, @@ -1559,7 +1561,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "d6426469", + "id": "777b44f6", "metadata": { "collapsed": false, "editable": true @@ -1577,14 +1579,14 @@ "name": "stdout", "output_type": "stream", "text": [ - "[0.93333333 0.93333333 0.93333333 0.92857143 1. 0.92857143\n", + "[0.93333333 0.93333333 0.86666667 1. 1. 0.92857143\n", " 1. 0.92857143 0.85714286 0.92857143]\n", - "Test set accuracy with Gradient boosting and scaled data: 0.99\n" + "Test set accuracy with Gradient boosting and scaled data: 0.97\n" ] }, { "data": { - "image/png": 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", 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", 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", + "image/png": 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", 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" ] @@ -1655,7 +1657,7 @@ }, { "cell_type": "markdown", - "id": "8de56415", + "id": "79c731f6", "metadata": { "editable": true }, @@ -1678,7 +1680,7 @@ }, { "cell_type": "markdown", - "id": "dfc59b18", + "id": "abf51c81", "metadata": { "editable": true }, @@ -1691,7 +1693,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "79e4cc00", + "id": "3243120a", "metadata": { "collapsed": false, "editable": true @@ -1809,7 +1811,7 @@ }, { "cell_type": "markdown", - "id": "c4700b19", + "id": "3415611e", "metadata": { "editable": true }, @@ -1820,7 +1822,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "eb0594c5", + "id": "0a1059eb", "metadata": { "collapsed": false, "editable": true @@ -1915,7 +1917,7 @@ }, { "cell_type": "markdown", - "id": "bbf154c4", + "id": "e88f148c", "metadata": { "editable": true }, @@ -1925,7 +1927,7 @@ }, { "cell_type": "markdown", - "id": "84d512fc", + "id": "05417d95", "metadata": { "editable": true }, @@ -1940,7 +1942,7 @@ }, { "cell_type": "markdown", - "id": "71b8eb59", + "id": "07dabfb9", "metadata": { "editable": true }, @@ -1956,7 +1958,7 @@ }, { "cell_type": "markdown", - "id": "473329b7", + "id": "f4be6096", "metadata": { "editable": true }, @@ -1981,7 +1983,7 @@ }, { "cell_type": "markdown", - "id": "6c94f44a", + "id": "13fbfb78", "metadata": { "editable": true }, @@ -2027,7 +2029,7 @@ }, { "cell_type": "markdown", - "id": "d9942ded", + "id": "6e70a00a", "metadata": { "editable": true }, @@ -2062,7 +2064,7 @@ }, { "cell_type": "markdown", - "id": "7818c700", + "id": "498439cc", "metadata": { "editable": true }, @@ -2085,7 +2087,7 @@ }, { "cell_type": "markdown", - "id": "cc946602", + "id": "5aefcdd5", "metadata": { "editable": true }, @@ -2108,7 +2110,7 @@ }, { "cell_type": "markdown", - "id": "c2b75185", + "id": "e5724f99", "metadata": { "editable": true }, @@ -2128,7 +2130,7 @@ }, { "cell_type": "markdown", - "id": "4600986a", + "id": "4b014afb", "metadata": { "editable": true }, @@ -2144,7 +2146,7 @@ }, { "cell_type": "markdown", - "id": "cfb572d5", + "id": "86d5bf4b", "metadata": { "editable": true }, @@ -2172,7 +2174,7 @@ }, { "cell_type": "markdown", - "id": "3e38f862", + "id": "6fd99cf9", "metadata": { "editable": true }, @@ -2190,7 +2192,7 @@ }, { "cell_type": "markdown", - "id": "76abe122", + "id": "40a95915", "metadata": { "editable": true }, @@ -2215,7 +2217,7 @@ }, { "cell_type": "markdown", - "id": "0b246db8", + "id": "de9b4ef9", "metadata": { "editable": true }, @@ -2233,7 +2235,7 @@ }, { "cell_type": "markdown", - "id": "9a6555fc", + "id": "09e09e8e", "metadata": { "editable": true }, @@ -2263,7 +2265,7 @@ }, { "cell_type": "markdown", - "id": "c2561e72", + "id": "4048b90f", "metadata": { "editable": true }, @@ -2277,7 +2279,7 @@ }, { "cell_type": "markdown", - "id": "796637c8", + "id": "d62bdef5", "metadata": { "editable": true }, @@ -2303,7 +2305,7 @@ }, { "cell_type": "markdown", - "id": "6afdb59e", + "id": "7110e8f8", "metadata": { "editable": true }, @@ -2332,7 +2334,7 @@ }, { "cell_type": "markdown", - "id": "95097424", + "id": "49da7ac1", "metadata": { "editable": true }, @@ -2349,7 +2351,7 @@ }, { "cell_type": "markdown", - "id": "31a33cea", + "id": "b7ddf675", "metadata": { "editable": true }, @@ -2371,7 +2373,7 @@ }, { "cell_type": "markdown", - "id": "d6583875", + "id": "4f93bfd8", "metadata": { "editable": true }, @@ -2389,7 +2391,7 @@ }, { "cell_type": "markdown", - "id": "285bbf5d", + "id": "553b0708", "metadata": { "editable": true }, @@ -2412,7 +2414,7 @@ }, { "cell_type": "markdown", - "id": "5b51dec9", + "id": "c82a9f73", "metadata": { "editable": true }, @@ -2431,7 +2433,7 @@ }, { "cell_type": "markdown", - "id": "4cb37b8f", + "id": "5791b95e", "metadata": { "editable": true }, @@ -2447,7 +2449,7 @@ }, { "cell_type": "markdown", - "id": "45fe33d7", + "id": "2681e655", "metadata": { "editable": true }, @@ -2462,7 +2464,7 @@ }, { "cell_type": "markdown", - "id": "3bbbaa88", + "id": "1764e858", "metadata": { "editable": true }, @@ -2486,7 +2488,7 @@ }, { "cell_type": "markdown", - "id": "d6a9f606", + "id": "435da59f", "metadata": { "editable": true }, @@ -2498,7 +2500,7 @@ }, { "cell_type": "markdown", - "id": "81aaff6e", + "id": "c7959e4f", "metadata": { "editable": true }, @@ -2516,7 +2518,7 @@ }, { "cell_type": "markdown", - "id": "9f856eb3", + "id": "fa48be28", "metadata": { "editable": true }, @@ -2526,7 +2528,7 @@ }, { "cell_type": "markdown", - "id": "f15429d1", + "id": "02027f10", "metadata": { "editable": true }, @@ -2544,7 +2546,7 @@ }, { "cell_type": "markdown", - "id": "e8a8f8e7", + "id": "7247c2cd", "metadata": { "editable": true }, @@ -2554,7 +2556,7 @@ }, { "cell_type": "markdown", - "id": "4fb17989", + "id": "0f51c953", "metadata": { "editable": true }, @@ -2573,7 +2575,7 @@ }, { "cell_type": "markdown", - "id": "0bed5ddd", + "id": "3ed82ee8", "metadata": { "editable": true }, @@ -2585,7 +2587,7 @@ }, { "cell_type": "markdown", - "id": "f8e64229", + "id": "f95ef8c8", "metadata": { "editable": true }, @@ -2599,7 +2601,7 @@ }, { "cell_type": "markdown", - "id": "f8f02179", + "id": "36be46d9", "metadata": { "editable": true }, @@ -2614,7 +2616,7 @@ }, { "cell_type": "markdown", - "id": "19008689", + "id": "374b4069", "metadata": { "editable": true }, @@ -2632,7 +2634,7 @@ }, { "cell_type": "markdown", - "id": "a3d31b3a", + "id": "5676ade3", "metadata": { "editable": true }, @@ -2646,7 +2648,7 @@ }, { "cell_type": "markdown", - "id": "79d29641", + "id": "5dc3dfdc", "metadata": { "editable": true }, @@ -2664,7 +2666,7 @@ }, { "cell_type": "markdown", - "id": "4d19d779", + "id": "7c26c421", "metadata": { "editable": true }, @@ -2688,7 +2690,7 @@ }, { "cell_type": "markdown", - "id": "b8fa06ec", + "id": "73947428", "metadata": { "editable": true }, @@ -2726,7 +2728,7 @@ }, { "cell_type": "markdown", - "id": "262fc510", + "id": "69763d57", "metadata": { "editable": true }, @@ -2749,7 +2751,7 @@ }, { "cell_type": "markdown", - "id": "36608f14", + "id": "3ee70227", "metadata": { "editable": true }, @@ -2785,7 +2787,7 @@ }, { "cell_type": "markdown", - "id": "a2b4652f", + "id": "6c8fe760", "metadata": { "editable": true }, @@ -2806,7 +2808,7 @@ }, { "cell_type": "markdown", - "id": "8cef8c03", + "id": "82d92000", "metadata": { "editable": true }, @@ -2828,7 +2830,7 @@ }, { "cell_type": "markdown", - "id": "df85a3e6", + "id": "81af1340", "metadata": { "editable": true }, @@ -2848,7 +2850,7 @@ }, { "cell_type": "markdown", - "id": "f01aaa3c", + "id": "9ccf4491", "metadata": { "editable": true }, @@ -2863,7 +2865,7 @@ }, { "cell_type": "markdown", - "id": "0005330c", + "id": "d561f7fa", "metadata": { "editable": true }, @@ -2881,7 +2883,7 @@ }, { "cell_type": "markdown", - "id": "efcfaccf", + "id": "2f59b95d", "metadata": { "editable": true }, @@ -2911,7 +2913,7 @@ }, { "cell_type": "markdown", - "id": "f7b109c9", + "id": "89648aa7", "metadata": { "editable": true }, @@ -2940,7 +2942,7 @@ }, { "cell_type": "markdown", - "id": "e9142380", + "id": "1bd0711b", "metadata": { "editable": true }, @@ -2971,7 +2973,7 @@ }, { "cell_type": "markdown", - "id": "cb638c92", + "id": "2e71e339", "metadata": { "editable": true }, @@ -2994,7 +2996,7 @@ }, { "cell_type": "markdown", - "id": "676fd56e", + "id": "3f1e33e9", "metadata": { "editable": true }, @@ -3012,7 +3014,7 @@ }, { "cell_type": "markdown", - "id": "ac1d93d3", + "id": "0b2bc8fa", "metadata": { "editable": true }, @@ -3035,7 +3037,7 @@ }, { "cell_type": "markdown", - "id": "c5047fcb", + "id": "3fad774c", "metadata": { "editable": true }, @@ -3056,7 +3058,7 @@ }, { "cell_type": "markdown", - "id": "bdd66000", + "id": "f418cc25", "metadata": { "editable": true }, @@ -3072,7 +3074,7 @@ }, { "cell_type": "markdown", - "id": "7fb56aeb", + "id": "5b323429", "metadata": { "editable": true }, diff --git a/doc/LectureNotes/week48.ipynb b/doc/LectureNotes/week48.ipynb index c0298d422..34d69ba5d 100644 --- a/doc/LectureNotes/week48.ipynb +++ b/doc/LectureNotes/week48.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "d7c532d5", + "id": "da40c119", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "d27ab2b4", + "id": "82348ef6", "metadata": { "editable": true }, @@ -22,14 +22,14 @@ "# Week 48: Gradient boosting and summary of course\n", "**Morten Hjorth-Jensen**, Department of Physics and Center for Computing in Science Education, University of Oslo, Norway\n", "\n", - "Date: **Nov 24, 2024**\n", + "Date: **Nov 25, 2024**\n", "\n", "Copyright 1999-2024, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license" ] }, { "cell_type": "markdown", - "id": "9f55b05a", + "id": "61b3407d", "metadata": { "editable": true }, @@ -39,7 +39,7 @@ }, { "cell_type": "markdown", - "id": "e72578f8", + "id": "c40156de", "metadata": { "editable": true }, @@ -56,23 +56,25 @@ "a. These lecture notes at \n", "\n", "b. See also lecture notes from week 47 at . The lecture on Monday starts with a repetition on AdaBoost before we move over to gradient boosting with examples\n", - "\n", - "\n", "\n", - "c. Video on Decision trees \n", + "c. Video of lecture at \n", "\n", - "d. Video on boosting methods \n", + "d. Whiteboard notes at \n", "\n", - "e. Video on AdaBoost \n", + "e. Video on Decision trees \n", "\n", - "f. Video on Gradient boost, part 1, parts 2-4 follow thereafter \n", + "f. Video on boosting methods \n", "\n", - "g. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at ." + "g. Video on AdaBoost \n", + "\n", + "h. Video on Gradient boost, part 1, parts 2-4 follow thereafter \n", + "\n", + "i. Decision Trees: Rashcka et al chapter 3 pages 86-98, and chapter 7 on Ensemble methods, Voting and Bagging and Gradient Boosting. See also lecture from STK-IN4300, lecture 7 at ." ] }, { "cell_type": "markdown", - "id": "51ee8fb3", + "id": "53d9e808", "metadata": { "editable": true }, @@ -91,7 +93,7 @@ }, { "cell_type": "markdown", - "id": "5b4ed2be", + "id": "9edfc128", "metadata": { "editable": true }, @@ -118,7 +120,7 @@ }, { "cell_type": "markdown", - "id": "3e76e282", + "id": "9278d8ee", "metadata": { "editable": true }, @@ -129,7 +131,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "acba62b3", + "id": "116422d2", "metadata": { "collapsed": false, "editable": true @@ -203,7 +205,7 @@ }, { "cell_type": "markdown", - "id": "c5cbe0e8", + "id": "2bf79506", "metadata": { "editable": true }, @@ -219,7 +221,7 @@ }, { "cell_type": "markdown", - "id": "19bd77f2", + "id": "a80dc181", "metadata": { "editable": true }, @@ -230,7 +232,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "84f1af2e", + "id": "61ef464c", "metadata": { "collapsed": false, "editable": true @@ -245,7 +247,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "6c01d900", + "id": "ab3892d7", "metadata": { "collapsed": false, "editable": true @@ -263,7 +265,7 @@ }, { "cell_type": "markdown", - "id": "896072f6", + "id": "2d4ac770", "metadata": { "editable": true }, @@ -283,7 +285,7 @@ }, { "cell_type": "markdown", - "id": "babc9c9c", + "id": "20f3e403", "metadata": { "editable": true }, @@ -297,7 +299,7 @@ }, { "cell_type": "markdown", - "id": "2ed28584", + "id": "ea52430d", "metadata": { "editable": true }, @@ -309,7 +311,7 @@ }, { "cell_type": "markdown", - "id": "5b5b9631", + "id": "86cb9f50", "metadata": { "editable": true }, @@ -326,7 +328,7 @@ }, { "cell_type": "markdown", - "id": "7b6c43ec", + "id": "00ca4f95", "metadata": { "editable": true }, @@ -338,7 +340,7 @@ }, { "cell_type": "markdown", - "id": "3962039f", + "id": "ddc56700", "metadata": { "editable": true }, @@ -352,7 +354,7 @@ }, { "cell_type": "markdown", - "id": "ee5f259c", + "id": "eb17dae2", "metadata": { "editable": true }, @@ -364,7 +366,7 @@ }, { "cell_type": "markdown", - "id": "079d8ee3", + "id": "9ae66e77", "metadata": { "editable": true }, @@ -377,7 +379,7 @@ }, { "cell_type": "markdown", - "id": "11f821a2", + "id": "ae777b9c", "metadata": { "editable": true }, @@ -389,7 +391,7 @@ }, { "cell_type": "markdown", - "id": "60990073", + "id": "8b3174c5", "metadata": { "editable": true }, @@ -399,7 +401,7 @@ }, { "cell_type": "markdown", - "id": "4d7195ab", + "id": "b4590742", "metadata": { "editable": true }, @@ -427,7 +429,7 @@ }, { "cell_type": "markdown", - "id": "4bee726a", + "id": "d6f866fd", "metadata": { "editable": true }, @@ -443,7 +445,7 @@ }, { "cell_type": "markdown", - "id": "1190b9a0", + "id": "b190e045", "metadata": { "editable": true }, @@ -455,7 +457,7 @@ }, { "cell_type": "markdown", - "id": "2702211b", + "id": "df876b5f", "metadata": { "editable": true }, @@ -466,7 +468,7 @@ }, { "cell_type": "markdown", - "id": "1b1d9984", + "id": "33756540", "metadata": { "editable": true }, @@ -478,7 +480,7 @@ }, { "cell_type": "markdown", - "id": "cdf7e56a", + "id": "9b588d4a", "metadata": { "editable": true }, @@ -488,7 +490,7 @@ }, { "cell_type": "markdown", - "id": "ac6f3b86", + "id": "5f289c53", "metadata": { "editable": true }, @@ -500,7 +502,7 @@ }, { "cell_type": "markdown", - "id": "faf6fb5f", + "id": "23f5d161", "metadata": { "editable": true }, @@ -510,7 +512,7 @@ }, { "cell_type": "markdown", - "id": "969f86ed", + "id": "fd058392", "metadata": { "editable": true }, @@ -522,7 +524,7 @@ }, { "cell_type": "markdown", - "id": "a98a541c", + "id": "5a8517bd", "metadata": { "editable": true }, @@ -532,7 +534,7 @@ }, { "cell_type": "markdown", - "id": "d8be5438", + "id": "94eec67e", "metadata": { "editable": true }, @@ -544,7 +546,7 @@ }, { "cell_type": "markdown", - "id": "932f43c5", + "id": "d4936ab1", "metadata": { "editable": true }, @@ -558,7 +560,7 @@ }, { "cell_type": "markdown", - "id": "24630eb9", + "id": "7bafaad4", "metadata": { "editable": true }, @@ -574,7 +576,7 @@ }, { "cell_type": "markdown", - "id": "9a7fa4b6", + "id": "b91cc27d", "metadata": { "editable": true }, @@ -586,7 +588,7 @@ }, { "cell_type": "markdown", - "id": "7ead62b2", + "id": "ee926ea4", "metadata": { "editable": true }, @@ -602,7 +604,7 @@ }, { "cell_type": "markdown", - "id": "91639c49", + "id": "88be0c12", "metadata": { "editable": true }, @@ -614,7 +616,7 @@ }, { "cell_type": "markdown", - "id": "e43865c2", + "id": "a16704d1", "metadata": { "editable": true }, @@ -624,7 +626,7 @@ }, { "cell_type": "markdown", - "id": "1884c219", + "id": "1667ae43", "metadata": { "editable": true }, @@ -636,7 +638,7 @@ }, { "cell_type": "markdown", - "id": "bbb1cdbb", + "id": "21368026", "metadata": { "editable": true }, @@ -648,7 +650,7 @@ }, { "cell_type": "markdown", - "id": "7e3590f6", + "id": "3f57f184", "metadata": { "editable": true }, @@ -660,7 +662,7 @@ }, { "cell_type": "markdown", - "id": "2eb8c86b", + "id": "adc38409", "metadata": { "editable": true }, @@ -671,7 +673,7 @@ }, { "cell_type": "markdown", - "id": "e477d15a", + "id": "d4de19b9", "metadata": { "editable": true }, @@ -683,7 +685,7 @@ }, { "cell_type": "markdown", - "id": "b6f01e59", + "id": "9b9b6dcd", "metadata": { "editable": true }, @@ -694,7 +696,7 @@ }, { "cell_type": "markdown", - "id": "71c85471", + "id": "a274021b", "metadata": { "editable": true }, @@ -706,7 +708,7 @@ }, { "cell_type": "markdown", - "id": "2d5be340", + "id": "fe46199f", "metadata": { "editable": true }, @@ -716,7 +718,7 @@ }, { "cell_type": "markdown", - "id": "e426517c", + "id": "913a48d6", "metadata": { "editable": true }, @@ -728,7 +730,7 @@ }, { "cell_type": "markdown", - "id": "c0c7e993", + "id": "e707fd99", "metadata": { "editable": true }, @@ -740,7 +742,7 @@ }, { "cell_type": "markdown", - "id": "7457a096", + "id": "1b749c34", "metadata": { "editable": true }, @@ -752,7 +754,7 @@ }, { "cell_type": "markdown", - "id": "424d5bc9", + "id": "b79b881c", "metadata": { "editable": true }, @@ -764,7 +766,7 @@ }, { "cell_type": "markdown", - "id": "d1fa658b", + "id": "115a36fd", "metadata": { "editable": true }, @@ -774,7 +776,7 @@ }, { "cell_type": "markdown", - "id": "f8fc6b15", + "id": "49086250", "metadata": { "editable": true }, @@ -786,7 +788,7 @@ }, { "cell_type": "markdown", - "id": "9ca82f54", + "id": "a840811a", "metadata": { "editable": true }, @@ -796,7 +798,7 @@ }, { "cell_type": "markdown", - "id": "6c028686", + "id": "d213a918", "metadata": { "editable": true }, @@ -808,7 +810,7 @@ }, { "cell_type": "markdown", - "id": "4007a0ef", + "id": "68a50e61", "metadata": { "editable": true }, @@ -818,7 +820,7 @@ }, { "cell_type": "markdown", - "id": "ba456347", + "id": "b62cf357", "metadata": { "editable": true }, @@ -830,7 +832,7 @@ }, { "cell_type": "markdown", - "id": "c8df4ef3", + "id": "5da2ca5c", "metadata": { "editable": true }, @@ -840,7 +842,7 @@ }, { "cell_type": "markdown", - "id": "da46f9d9", + "id": "803e473e", "metadata": { "editable": true }, @@ -852,7 +854,7 @@ }, { "cell_type": "markdown", - "id": "5c60f30d", + "id": "35e1f9c1", "metadata": { "editable": true }, @@ -862,7 +864,7 @@ }, { "cell_type": "markdown", - "id": "a5bc4dfe", + "id": "3b7c369c", "metadata": { "editable": true }, @@ -874,7 +876,7 @@ }, { "cell_type": "markdown", - "id": "3987cc1e", + "id": "dae26491", "metadata": { "editable": true }, @@ -894,7 +896,7 @@ }, { "cell_type": "markdown", - "id": "b35df09e", + "id": "57668483", "metadata": { "editable": true }, @@ -906,7 +908,7 @@ }, { "cell_type": "markdown", - "id": "5b6a209c", + "id": "5830f7a2", "metadata": { "editable": true }, @@ -916,7 +918,7 @@ }, { "cell_type": "markdown", - "id": "d619a097", + "id": "b933bd20", "metadata": { "editable": true }, @@ -932,7 +934,7 @@ }, { "cell_type": "markdown", - "id": "b7311abb", + "id": "6e8dff87", "metadata": { "editable": true }, @@ -944,7 +946,7 @@ }, { "cell_type": "markdown", - "id": "4bfb4209", + "id": "1d6a0fa2", "metadata": { "editable": true }, @@ -972,7 +974,7 @@ }, { "cell_type": "markdown", - "id": "4cf4c1f4", + "id": "eacc9804", "metadata": { "editable": true }, @@ -985,7 +987,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "453dff54", + "id": "29c9b0a6", "metadata": { "collapsed": false, "editable": true @@ -1010,7 +1012,7 @@ }, { "cell_type": "markdown", - "id": "d6470164", + "id": "f5eaa0fc", "metadata": { "editable": true }, @@ -1021,7 +1023,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "b6e01886", + "id": "1db44432", "metadata": { "collapsed": false, "editable": true @@ -1110,7 +1112,7 @@ }, { "cell_type": "markdown", - "id": "3cede4c9", + "id": "60fbb085", "metadata": { "editable": true }, @@ -1128,7 +1130,7 @@ }, { "cell_type": "markdown", - "id": "94e3b59f", + "id": "cbc6c37e", "metadata": { "editable": true }, @@ -1141,7 +1143,7 @@ }, { "cell_type": "markdown", - "id": "2f000f62", + "id": "675390d3", "metadata": { "editable": true }, @@ -1153,7 +1155,7 @@ }, { "cell_type": "markdown", - "id": "014da5e9", + "id": "ac24e4fb", "metadata": { "editable": true }, @@ -1163,7 +1165,7 @@ }, { "cell_type": "markdown", - "id": "022aee06", + "id": "cfdd6b7f", "metadata": { "editable": true }, @@ -1175,7 +1177,7 @@ }, { "cell_type": "markdown", - "id": "d0d3e174", + "id": "de1524db", "metadata": { "editable": true }, @@ -1185,7 +1187,7 @@ }, { "cell_type": "markdown", - "id": "8af32042", + "id": "506fa7d0", "metadata": { "editable": true }, @@ -1197,7 +1199,7 @@ }, { "cell_type": "markdown", - "id": "4a384d95", + "id": "cc03bbc9", "metadata": { "editable": true }, @@ -1210,7 +1212,7 @@ }, { "cell_type": "markdown", - "id": "bd5b9bb9", + "id": "a1145d71", "metadata": { "editable": true }, @@ -1222,7 +1224,7 @@ }, { "cell_type": "markdown", - "id": "560b3106", + "id": "f9bbe14d", "metadata": { "editable": true }, @@ -1234,7 +1236,7 @@ }, { "cell_type": "markdown", - "id": "4742d327", + "id": "44ca0a6c", "metadata": { "editable": true }, @@ -1246,7 +1248,7 @@ }, { "cell_type": "markdown", - "id": "d160a37f", + "id": "7791881d", "metadata": { "editable": true }, @@ -1256,7 +1258,7 @@ }, { "cell_type": "markdown", - "id": "f3cde07e", + "id": "b3cffde0", "metadata": { "editable": true }, @@ -1268,7 +1270,7 @@ }, { "cell_type": "markdown", - "id": "cf95ad5e", + "id": "842710e4", "metadata": { "editable": true }, @@ -1278,7 +1280,7 @@ }, { "cell_type": "markdown", - "id": "bb614b9d", + "id": "2d0ac22f", "metadata": { "editable": true }, @@ -1294,7 +1296,7 @@ }, { "cell_type": "markdown", - "id": "19e3e8fa", + "id": "8b7ba0b6", "metadata": { "editable": true }, @@ -1306,7 +1308,7 @@ }, { "cell_type": "markdown", - "id": "9330b34b", + "id": "d0ad4149", "metadata": { "editable": true }, @@ -1327,7 +1329,7 @@ }, { "cell_type": "markdown", - "id": "e648539a", + "id": "6efc5e2e", "metadata": { "editable": true }, @@ -1338,7 +1340,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "d12810d3", + "id": "4a6a7ba3", "metadata": { "collapsed": false, "editable": true @@ -1389,7 +1391,7 @@ }, { "cell_type": "markdown", - "id": "f3e95eca", + "id": "23c85cdf", "metadata": { "editable": true }, @@ -1400,7 +1402,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "d6426469", + "id": "777b44f6", "metadata": { "collapsed": false, "editable": true @@ -1448,7 +1450,7 @@ }, { "cell_type": "markdown", - "id": "8de56415", + "id": "79c731f6", "metadata": { "editable": true }, @@ -1471,7 +1473,7 @@ }, { "cell_type": "markdown", - "id": "dfc59b18", + "id": "abf51c81", "metadata": { "editable": true }, @@ -1484,7 +1486,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "79e4cc00", + "id": "3243120a", "metadata": { "collapsed": false, "editable": true @@ -1542,7 +1544,7 @@ }, { "cell_type": "markdown", - "id": "c4700b19", + "id": "3415611e", "metadata": { "editable": true }, @@ -1553,7 +1555,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "eb0594c5", + "id": "0a1059eb", "metadata": { "collapsed": false, "editable": true @@ -1640,7 +1642,7 @@ }, { "cell_type": "markdown", - "id": "bbf154c4", + "id": "e88f148c", "metadata": { "editable": true }, @@ -1650,7 +1652,7 @@ }, { "cell_type": "markdown", - "id": "84d512fc", + "id": "05417d95", "metadata": { "editable": true }, @@ -1665,7 +1667,7 @@ }, { "cell_type": "markdown", - "id": "71b8eb59", + "id": "07dabfb9", "metadata": { "editable": true }, @@ -1681,7 +1683,7 @@ }, { "cell_type": "markdown", - "id": "473329b7", + "id": "f4be6096", "metadata": { "editable": true }, @@ -1706,7 +1708,7 @@ }, { "cell_type": "markdown", - "id": "6c94f44a", + "id": "13fbfb78", "metadata": { "editable": true }, @@ -1752,7 +1754,7 @@ }, { "cell_type": "markdown", - "id": "d9942ded", + "id": "6e70a00a", "metadata": { "editable": true }, @@ -1787,7 +1789,7 @@ }, { "cell_type": "markdown", - "id": "7818c700", + "id": "498439cc", "metadata": { "editable": true }, @@ -1810,7 +1812,7 @@ }, { "cell_type": "markdown", - "id": "cc946602", + "id": "5aefcdd5", "metadata": { "editable": true }, @@ -1833,7 +1835,7 @@ }, { "cell_type": "markdown", - "id": "c2b75185", + "id": "e5724f99", "metadata": { "editable": true }, @@ -1853,7 +1855,7 @@ }, { "cell_type": "markdown", - "id": "4600986a", + "id": "4b014afb", "metadata": { "editable": true }, @@ -1869,7 +1871,7 @@ }, { "cell_type": "markdown", - "id": "cfb572d5", + "id": "86d5bf4b", "metadata": { "editable": true }, @@ -1897,7 +1899,7 @@ }, { "cell_type": "markdown", - "id": "3e38f862", + "id": "6fd99cf9", "metadata": { "editable": true }, @@ -1915,7 +1917,7 @@ }, { "cell_type": "markdown", - "id": "76abe122", + "id": "40a95915", "metadata": { "editable": true }, @@ -1940,7 +1942,7 @@ }, { "cell_type": "markdown", - "id": "0b246db8", + "id": "de9b4ef9", "metadata": { "editable": true }, @@ -1958,7 +1960,7 @@ }, { "cell_type": "markdown", - "id": "9a6555fc", + "id": "09e09e8e", "metadata": { "editable": true }, @@ -1988,7 +1990,7 @@ }, { "cell_type": "markdown", - "id": "c2561e72", + "id": "4048b90f", "metadata": { "editable": true }, @@ -2002,7 +2004,7 @@ }, { "cell_type": "markdown", - "id": "796637c8", + "id": "d62bdef5", "metadata": { "editable": true }, @@ -2028,7 +2030,7 @@ }, { "cell_type": "markdown", - "id": "6afdb59e", + "id": "7110e8f8", "metadata": { "editable": true }, @@ -2057,7 +2059,7 @@ }, { "cell_type": "markdown", - "id": "95097424", + "id": "49da7ac1", "metadata": { "editable": true }, @@ -2074,7 +2076,7 @@ }, { "cell_type": "markdown", - "id": "31a33cea", + "id": "b7ddf675", "metadata": { "editable": true }, @@ -2096,7 +2098,7 @@ }, { "cell_type": "markdown", - "id": "d6583875", + "id": "4f93bfd8", "metadata": { "editable": true }, @@ -2114,7 +2116,7 @@ }, { "cell_type": "markdown", - "id": "285bbf5d", + "id": "553b0708", "metadata": { "editable": true }, @@ -2137,7 +2139,7 @@ }, { "cell_type": "markdown", - "id": "5b51dec9", + "id": "c82a9f73", "metadata": { "editable": true }, @@ -2156,7 +2158,7 @@ }, { "cell_type": "markdown", - "id": "4cb37b8f", + "id": "5791b95e", "metadata": { "editable": true }, @@ -2172,7 +2174,7 @@ }, { "cell_type": "markdown", - "id": "45fe33d7", + "id": "2681e655", "metadata": { "editable": true }, @@ -2187,7 +2189,7 @@ }, { "cell_type": "markdown", - "id": "3bbbaa88", + "id": "1764e858", "metadata": { "editable": true }, @@ -2211,7 +2213,7 @@ }, { "cell_type": "markdown", - "id": "d6a9f606", + "id": "435da59f", "metadata": { "editable": true }, @@ -2223,7 +2225,7 @@ }, { "cell_type": "markdown", - "id": "81aaff6e", + "id": "c7959e4f", "metadata": { "editable": true }, @@ -2241,7 +2243,7 @@ }, { "cell_type": "markdown", - "id": "9f856eb3", + "id": "fa48be28", "metadata": { "editable": true }, @@ -2251,7 +2253,7 @@ }, { "cell_type": "markdown", - "id": "f15429d1", + "id": "02027f10", "metadata": { "editable": true }, @@ -2269,7 +2271,7 @@ }, { "cell_type": "markdown", - "id": "e8a8f8e7", + "id": "7247c2cd", "metadata": { "editable": true }, @@ -2279,7 +2281,7 @@ }, { "cell_type": "markdown", - "id": "4fb17989", + "id": "0f51c953", "metadata": { "editable": true }, @@ -2298,7 +2300,7 @@ }, { "cell_type": "markdown", - "id": "0bed5ddd", + "id": "3ed82ee8", "metadata": { "editable": true }, @@ -2310,7 +2312,7 @@ }, { "cell_type": "markdown", - "id": "f8e64229", + "id": "f95ef8c8", "metadata": { "editable": true }, @@ -2324,7 +2326,7 @@ }, { "cell_type": "markdown", - "id": "f8f02179", + "id": "36be46d9", "metadata": { "editable": true }, @@ -2339,7 +2341,7 @@ }, { "cell_type": "markdown", - "id": "19008689", + "id": "374b4069", "metadata": { "editable": true }, @@ -2357,7 +2359,7 @@ }, { "cell_type": "markdown", - "id": "a3d31b3a", + "id": "5676ade3", "metadata": { "editable": true }, @@ -2371,7 +2373,7 @@ }, { "cell_type": "markdown", - "id": "79d29641", + "id": "5dc3dfdc", "metadata": { "editable": true }, @@ -2389,7 +2391,7 @@ }, { "cell_type": "markdown", - "id": "4d19d779", + "id": "7c26c421", "metadata": { "editable": true }, @@ -2413,7 +2415,7 @@ }, { "cell_type": "markdown", - "id": "b8fa06ec", + "id": "73947428", "metadata": { "editable": true }, @@ -2451,7 +2453,7 @@ }, { "cell_type": "markdown", - "id": "262fc510", + "id": "69763d57", "metadata": { "editable": true }, @@ -2474,7 +2476,7 @@ }, { "cell_type": "markdown", - "id": "36608f14", + "id": "3ee70227", "metadata": { "editable": true }, @@ -2510,7 +2512,7 @@ }, { "cell_type": "markdown", - "id": "a2b4652f", + "id": "6c8fe760", "metadata": { "editable": true }, @@ -2531,7 +2533,7 @@ }, { "cell_type": "markdown", - "id": "8cef8c03", + "id": "82d92000", "metadata": { "editable": true }, @@ -2553,7 +2555,7 @@ }, { "cell_type": "markdown", - "id": "df85a3e6", + "id": "81af1340", "metadata": { "editable": true }, @@ -2573,7 +2575,7 @@ }, { "cell_type": "markdown", - "id": "f01aaa3c", + "id": "9ccf4491", "metadata": { "editable": true }, @@ -2588,7 +2590,7 @@ }, { "cell_type": "markdown", - "id": "0005330c", + "id": "d561f7fa", "metadata": { "editable": true }, @@ -2606,7 +2608,7 @@ }, { "cell_type": "markdown", - "id": "efcfaccf", + "id": "2f59b95d", "metadata": { "editable": true }, @@ -2636,7 +2638,7 @@ }, { "cell_type": "markdown", - "id": "f7b109c9", + "id": "89648aa7", "metadata": { "editable": true }, @@ -2665,7 +2667,7 @@ }, { "cell_type": "markdown", - "id": "e9142380", + "id": "1bd0711b", "metadata": { "editable": true }, @@ -2696,7 +2698,7 @@ }, { "cell_type": "markdown", - "id": "cb638c92", + "id": "2e71e339", "metadata": { "editable": true }, @@ -2719,7 +2721,7 @@ }, { "cell_type": "markdown", - "id": "676fd56e", + "id": "3f1e33e9", "metadata": { "editable": true }, @@ -2737,7 +2739,7 @@ }, { "cell_type": "markdown", - "id": "ac1d93d3", + "id": "0b2bc8fa", "metadata": { "editable": true }, @@ -2760,7 +2762,7 @@ }, { "cell_type": "markdown", - "id": "c5047fcb", + "id": "3fad774c", "metadata": { "editable": true }, @@ -2781,7 +2783,7 @@ }, { "cell_type": "markdown", - "id": "bdd66000", + "id": "f418cc25", "metadata": { "editable": true }, @@ -2797,7 +2799,7 @@ }, { "cell_type": "markdown", - "id": "7fb56aeb", + "id": "5b323429", "metadata": { "editable": true },