From 932bef9f2567f925cda6d2106bcfa1e93e2b676b Mon Sep 17 00:00:00 2001 From: Morten Hjorth-Jensen Date: Mon, 29 Sep 2025 16:23:16 +0200 Subject: [PATCH] updating notes with links --- .../_build/.doctrees/environment.pickle | Bin 374477 -> 374477 bytes .../_build/.doctrees/week40.doctree | Bin 187118 -> 187685 bytes .../_build/html/_sources/week40.ipynb | 270 ++++---- doc/LectureNotes/_build/html/searchindex.js | 2 +- doc/LectureNotes/_build/html/week40.html | 5 +- .../_build/jupyter_execute/week40.ipynb | 270 ++++---- doc/LectureNotes/week40.ipynb | 270 ++++---- doc/pub/week40/html/._week40-bs001.html | 6 +- doc/pub/week40/html/week40-reveal.html | 6 +- doc/pub/week40/html/week40-solarized.html | 6 +- doc/pub/week40/html/week40.html | 6 +- doc/pub/week40/ipynb/ipynb-week40-src.tar.gz | Bin 34323 -> 34323 bytes doc/pub/week40/ipynb/week40.ipynb | 617 +++++------------- doc/src/week40/week40.do.txt | 4 +- 14 files changed, 586 insertions(+), 876 deletions(-) diff --git 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"fb2c3381", + "id": "75c3b33e", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "a2dfe61e", + "id": "4ba50982", "metadata": { "editable": true }, @@ -37,13 +37,15 @@ "\n", "\n", "2. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model\n", - "\n", - "" + "\n", + "3. Video of lecture at \n", + "\n", + "4. Whiteboard notes at " ] }, { "cell_type": "markdown", - "id": "e342e71b", + "id": "1d527020", "metadata": { "editable": true }, @@ -65,7 +67,7 @@ }, { "cell_type": "markdown", - "id": "97a574c5", + "id": "63a4d497", "metadata": { "editable": true }, @@ -84,7 +86,7 @@ }, { "cell_type": "markdown", - "id": "22f40b12", + "id": "73621d6b", "metadata": { "editable": true }, @@ -106,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "729f5aba", + "id": "fc1df17b", "metadata": { "editable": true }, @@ -132,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "e2782fb0", + "id": "a3d311e6", "metadata": { "editable": true }, @@ -158,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "7968ba20", + "id": "4120d6f9", "metadata": { "editable": true }, @@ -183,7 +185,7 @@ }, { "cell_type": "markdown", - "id": "8b89d296", + "id": "9e85d1e4", "metadata": { "editable": true }, @@ -195,7 +197,7 @@ }, { "cell_type": "markdown", - "id": "4380d27a", + "id": "a0d8c838", "metadata": { "editable": true }, @@ -207,7 +209,7 @@ }, { "cell_type": "markdown", - "id": "930a5781", + "id": "7cea7945", "metadata": { "editable": true }, @@ -222,7 +224,7 @@ }, { "cell_type": "markdown", - "id": "5c7f6446", + "id": "6adc5106", "metadata": { "editable": true }, @@ -234,7 +236,7 @@ }, { "cell_type": "markdown", - "id": "964ad807", + "id": "f976068e", "metadata": { "editable": true }, @@ -246,7 +248,7 @@ }, { "cell_type": "markdown", - "id": "3214fcef", + "id": "dedf9f0e", "metadata": { "editable": true }, @@ -263,7 +265,7 @@ }, { "cell_type": "markdown", - "id": "db0ae7e7", + "id": "bd8b54ab", "metadata": { "editable": true }, @@ -277,7 +279,7 @@ }, { "cell_type": "markdown", - "id": "81b0f404", + "id": "57bfb17f", "metadata": { "editable": true }, @@ -287,7 +289,7 @@ }, { "cell_type": "markdown", - "id": "a1b9b4b7", + "id": "00aee268", "metadata": { "editable": true }, @@ -299,7 +301,7 @@ }, { "cell_type": "markdown", - "id": "9ee3938f", + "id": "e12940f3", "metadata": { "editable": true }, @@ -311,7 +313,7 @@ }, { "cell_type": "markdown", - "id": "3a4c630e", + "id": "e5b2b29e", "metadata": { "editable": true }, @@ -323,7 +325,7 @@ }, { "cell_type": "markdown", - "id": "38250af6", + "id": "c6c0ba4c", "metadata": { "editable": true }, @@ -334,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "122e8f3d", + "id": "46ee2ea8", "metadata": { "editable": true }, @@ -346,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "b41b63c5", + "id": "9a05709b", "metadata": { "editable": true }, @@ -357,7 +359,7 @@ }, { "cell_type": "markdown", - "id": "80534aba", + "id": "ae1362c9", "metadata": { "editable": true }, @@ -373,7 +375,7 @@ }, { "cell_type": "markdown", - "id": "eb1b9888", + "id": "57f4670b", "metadata": { "editable": true }, @@ -385,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "ab198853", + "id": "1dc19f59", "metadata": { "editable": true }, @@ -395,7 +397,7 @@ }, { "cell_type": "markdown", - "id": "f5b74ed9", + "id": "4e96dc87", "metadata": { "editable": true }, @@ -407,7 +409,7 @@ }, { "cell_type": "markdown", - "id": "7c684bba", + "id": "fa77bec9", "metadata": { "editable": true }, @@ -422,7 +424,7 @@ }, { "cell_type": "markdown", - "id": "7c8b3019", + "id": "1b013fd2", "metadata": { "editable": true }, @@ -434,7 +436,7 @@ }, { "cell_type": "markdown", - "id": "8e203747", + "id": "910f36dd", "metadata": { "editable": true }, @@ -445,7 +447,7 @@ }, { "cell_type": "markdown", - "id": "fbf05462", + "id": "8212d0ed", "metadata": { "editable": true }, @@ -457,7 +459,7 @@ }, { "cell_type": "markdown", - "id": "00138c6f", + "id": "7ae7078b", "metadata": { "editable": true }, @@ -469,7 +471,7 @@ }, { "cell_type": "markdown", - "id": "33e45216", + "id": "59e57d7c", "metadata": { "editable": true }, @@ -481,7 +483,7 @@ }, { "cell_type": "markdown", - "id": "6e353bf4", + "id": "6ffe0955", "metadata": { "editable": true }, @@ -491,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "004a41f8", + "id": "56e9bd82", "metadata": { "editable": true }, @@ -503,7 +505,7 @@ }, { "cell_type": "markdown", - "id": "a232ef44", + "id": "86b12946", "metadata": { "editable": true }, @@ -517,7 +519,7 @@ }, { "cell_type": "markdown", - "id": "d090847e", + "id": "d55394df", "metadata": { "editable": true }, @@ -529,7 +531,7 @@ }, { "cell_type": "markdown", - "id": "46a9546e", + "id": "ee01378a", "metadata": { "editable": true }, @@ -539,7 +541,7 @@ }, { "cell_type": "markdown", - "id": "f85b4c49", + "id": "c7fadfbb", "metadata": { "editable": true }, @@ -551,7 +553,7 @@ }, { "cell_type": "markdown", - "id": "4ba76229", + "id": "e8310f63", "metadata": { "editable": true }, @@ -561,7 +563,7 @@ }, { "cell_type": "markdown", - "id": "9f40376d", + "id": "be651647", "metadata": { "editable": true }, @@ -573,7 +575,7 @@ }, { "cell_type": "markdown", - "id": "e344416d", + "id": "e277c601", "metadata": { "editable": true }, @@ -584,7 +586,7 @@ }, { "cell_type": "markdown", - "id": "f9107f2a", + "id": "aea3a410", "metadata": { "editable": true }, @@ -607,7 +609,7 @@ }, { "cell_type": "markdown", - "id": "36ce9b10", + "id": "bfa7221f", "metadata": { "editable": true }, @@ -619,7 +621,7 @@ }, { "cell_type": "markdown", - "id": "2cd2dfd0", + "id": "3d749c39", "metadata": { "editable": true }, @@ -629,7 +631,7 @@ }, { "cell_type": "markdown", - "id": "ce302774", + "id": "dc061a39", "metadata": { "editable": true }, @@ -641,7 +643,7 @@ }, { "cell_type": "markdown", - "id": "d443be10", + "id": "8ea10488", "metadata": { "editable": true }, @@ -658,7 +660,7 @@ }, { "cell_type": "markdown", - "id": "6755463b", + "id": "9cb3baf8", "metadata": { "editable": true }, @@ -677,7 +679,7 @@ }, { "cell_type": "markdown", - "id": "84f1d6cb", + "id": "387393d7", "metadata": { "editable": true }, @@ -694,7 +696,7 @@ }, { "cell_type": "markdown", - "id": "d1d136eb", + "id": "30f64659", "metadata": { "editable": true }, @@ -709,7 +711,7 @@ }, { "cell_type": "markdown", - "id": "34bda880", + "id": "3ba65422", "metadata": { "editable": true }, @@ -719,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "853aa76a", + "id": "005f46d7", "metadata": { "editable": true }, @@ -735,7 +737,7 @@ }, { "cell_type": "markdown", - "id": "b3fb4491", + "id": "61a638bc", "metadata": { "editable": true }, @@ -747,7 +749,7 @@ }, { "cell_type": "markdown", - "id": "453c22a3", + "id": "469c0042", "metadata": { "editable": true }, @@ -758,7 +760,7 @@ }, { "cell_type": "markdown", - "id": "611c9632", + "id": "0af5449a", "metadata": { "editable": true }, @@ -770,7 +772,7 @@ }, { "cell_type": "markdown", - "id": "c45aca41", + "id": "f4c16b4f", "metadata": { "editable": true }, @@ -780,7 +782,7 @@ }, { "cell_type": "markdown", - "id": "35751022", + "id": "ddbe7f50", "metadata": { "editable": true }, @@ -794,7 +796,7 @@ }, { "cell_type": "markdown", - "id": "781c6916", + "id": "52830f96", "metadata": { "editable": true }, @@ -806,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "446ec153", + "id": "1b8a1c14", "metadata": { "editable": true }, @@ -816,7 +818,7 @@ }, { "cell_type": "markdown", - "id": "fa87caf0", + "id": "8ad73cea", "metadata": { "editable": true }, @@ -828,7 +830,7 @@ }, { "cell_type": "markdown", - "id": "6a5dfd86", + "id": "6d47dd0b", "metadata": { "editable": true }, @@ -840,7 +842,7 @@ }, { "cell_type": "markdown", - "id": "a0e56fe0", + "id": "f399c2f4", "metadata": { "editable": true }, @@ -853,7 +855,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "ea0068e6", + "id": "79f6b6fc", "metadata": { "collapsed": false, "editable": true @@ -996,7 +998,7 @@ }, { "cell_type": "markdown", - "id": "9fe762a6", + "id": "24e84b29", "metadata": { "editable": true }, @@ -1015,7 +1017,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "b6f32217", + "id": "7a73eca4", "metadata": { "collapsed": false, "editable": true @@ -1058,7 +1060,7 @@ }, { "cell_type": "markdown", - "id": "9f278530", + "id": "40d4b30f", "metadata": { "editable": true }, @@ -1072,7 +1074,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "88e13c71", + "id": "ac0089bf", "metadata": { "collapsed": false, "editable": true @@ -1161,7 +1163,7 @@ }, { "cell_type": "markdown", - "id": "938925ff", + "id": "1e9acef3", "metadata": { "editable": true }, @@ -1178,7 +1180,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "c01e1379", + "id": "9153234a", "metadata": { "collapsed": false, "editable": true @@ -1207,7 +1209,7 @@ }, { "cell_type": "markdown", - "id": "a2b8d6a4", + "id": "908d547b", "metadata": { "editable": true }, @@ -1221,7 +1223,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "6ec30a76", + "id": "8a46f4f3", "metadata": { "collapsed": false, "editable": true @@ -1266,7 +1268,7 @@ }, { "cell_type": "markdown", - "id": "1c00160e", + "id": "ba0275a7", "metadata": { "editable": true }, @@ -1291,7 +1293,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "1468b093", + "id": "1af34f8e", "metadata": { "collapsed": false, "editable": true @@ -1303,7 +1305,7 @@ }, { "cell_type": "markdown", - "id": "509b083d", + "id": "1eac30d3", "metadata": { "editable": true }, @@ -1314,7 +1316,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "c58d0fa7", + "id": "a0cdd9c9", "metadata": { "collapsed": false, "editable": true @@ -1326,7 +1328,7 @@ }, { "cell_type": "markdown", - "id": "a0445ed4", + "id": "013777ad", "metadata": { "editable": true }, @@ -1339,7 +1341,7 @@ }, { "cell_type": "markdown", - "id": "93e60a48", + "id": "410f90ac", "metadata": { "editable": true }, @@ -1350,7 +1352,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "c0c0be2f", + "id": "fa16a459", "metadata": { "collapsed": false, "editable": true @@ -1393,7 +1395,7 @@ }, { "cell_type": "markdown", - "id": "c4e95738", + "id": "a721de53", "metadata": { "editable": true }, @@ -1411,7 +1413,7 @@ }, { "cell_type": "markdown", - "id": "455b9abe", + "id": "68de5052", "metadata": { "editable": true }, @@ -1435,7 +1437,7 @@ }, { "cell_type": "markdown", - "id": "fafe38d0", + "id": "7685af02", "metadata": { "editable": true }, @@ -1453,7 +1455,7 @@ }, { "cell_type": "markdown", - "id": "a2b62ddd", + "id": "3dfcfcb0", "metadata": { "editable": true }, @@ -1493,7 +1495,7 @@ }, { "cell_type": "markdown", - "id": "86b9d89a", + "id": "0d037ca7", "metadata": { "editable": true }, @@ -1522,7 +1524,7 @@ }, { "cell_type": "markdown", - "id": "b252c4a7", + "id": "7bcf7188", "metadata": { "editable": true }, @@ -1543,7 +1545,7 @@ }, { "cell_type": "markdown", - "id": "91b833ad", + "id": "cd094e20", "metadata": { "editable": true }, @@ -1572,7 +1574,7 @@ }, { "cell_type": "markdown", - "id": "e4346288", + "id": "ea99157e", "metadata": { "editable": true }, @@ -1593,7 +1595,7 @@ }, { "cell_type": "markdown", - "id": "341895b3", + "id": "b73754c2", "metadata": { "editable": true }, @@ -1614,7 +1616,7 @@ }, { "cell_type": "markdown", - "id": "37369181", + "id": "aa97c83d", "metadata": { "editable": true }, @@ -1631,7 +1633,7 @@ }, { "cell_type": "markdown", - "id": "130c62a2", + "id": "abe84919", "metadata": { "editable": true }, @@ -1652,7 +1654,7 @@ }, { "cell_type": "markdown", - "id": "8be7d076", + "id": "d3ff207b", "metadata": { "editable": true }, @@ -1668,7 +1670,7 @@ }, { "cell_type": "markdown", - "id": "27f3849a", + "id": "f982c11f", "metadata": { "editable": true }, @@ -1685,7 +1687,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "6485b942", + "id": "04a3e090", "metadata": { "collapsed": false, "editable": true @@ -1726,7 +1728,7 @@ }, { "cell_type": "markdown", - "id": "33f6d6bb", + "id": "95b1f5a5", "metadata": { "editable": true }, @@ -1736,7 +1738,7 @@ }, { "cell_type": "markdown", - "id": "28ab766e", + "id": "0d200eff", "metadata": { "editable": true }, @@ -1747,7 +1749,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "ec9476bf", + "id": "040a69d0", "metadata": { "collapsed": false, "editable": true @@ -1807,7 +1809,7 @@ }, { "cell_type": "markdown", - "id": "77fa7a86", + "id": "49f17f65", "metadata": { "editable": true }, @@ -1817,7 +1819,7 @@ }, { "cell_type": "markdown", - "id": "2222f781", + "id": "714e0891", "metadata": { "editable": true }, @@ -1828,7 +1830,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "4ddc9629", + "id": "28bde670", "metadata": { "collapsed": false, "editable": true @@ -1848,7 +1850,7 @@ }, { "cell_type": "markdown", - "id": "78af4075", + "id": "4440856f", "metadata": { "editable": true }, @@ -1860,7 +1862,7 @@ }, { "cell_type": "markdown", - "id": "16109aee", + "id": "6199da92", "metadata": { "editable": true }, @@ -1872,7 +1874,7 @@ }, { "cell_type": "markdown", - "id": "e0ba2d42", + "id": "62c964e3", "metadata": { "editable": true }, @@ -1887,7 +1889,7 @@ }, { "cell_type": "markdown", - "id": "59a5a47a", + "id": "64ba4c70", "metadata": { "editable": true }, @@ -1899,7 +1901,7 @@ }, { "cell_type": "markdown", - "id": "204ea252", + "id": "66c11135", "metadata": { "editable": true }, @@ -1916,7 +1918,7 @@ }, { "cell_type": "markdown", - "id": "cf69a239", + "id": "0f47b20a", "metadata": { "editable": true }, @@ -1931,7 +1933,7 @@ }, { "cell_type": "markdown", - "id": "634b25c8", + "id": "bda56156", "metadata": { "editable": true }, @@ -1949,7 +1951,7 @@ }, { "cell_type": "markdown", - "id": "c3a9df64", + "id": "1330fab9", "metadata": { "editable": true }, @@ -1961,7 +1963,7 @@ }, { "cell_type": "markdown", - "id": "419f4ca1", + "id": "ae474dfb", "metadata": { "editable": true }, @@ -1979,7 +1981,7 @@ }, { "cell_type": "markdown", - "id": "af3386ac", + "id": "b6cb6fed", "metadata": { "editable": true }, @@ -1992,7 +1994,7 @@ }, { "cell_type": "markdown", - "id": "e254da43", + "id": "2f8f9b4e", "metadata": { "editable": true }, @@ -2004,7 +2006,7 @@ }, { "cell_type": "markdown", - "id": "96ebff08", + "id": "18e74238", "metadata": { "editable": true }, @@ -2022,7 +2024,7 @@ }, { "cell_type": "markdown", - "id": "e482ce9c", + "id": "d10df3e7", "metadata": { "editable": true }, @@ -2040,7 +2042,7 @@ }, { "cell_type": "markdown", - "id": "a5002fb2", + "id": "da21a316", "metadata": { "editable": true }, @@ -2050,7 +2052,7 @@ }, { "cell_type": "markdown", - "id": "95c66242", + "id": "76938a28", "metadata": { "editable": true }, @@ -2068,7 +2070,7 @@ }, { "cell_type": "markdown", - "id": "9590e411", + "id": "65434967", "metadata": { "editable": true }, @@ -2087,7 +2089,7 @@ }, { "cell_type": "markdown", - "id": "8c4ff6f9", + "id": "31d4f5aa", "metadata": { "editable": true }, @@ -2100,7 +2102,7 @@ }, { "cell_type": "markdown", - "id": "4b6ffa92", + "id": "114030e5", "metadata": { "editable": true }, @@ -2118,7 +2120,7 @@ }, { "cell_type": "markdown", - "id": "eb180d8b", + "id": "a93aec4e", "metadata": { "editable": true }, @@ -2129,7 +2131,7 @@ }, { "cell_type": "markdown", - "id": "f1d0bda0", + "id": "7c85562d", "metadata": { "editable": true }, @@ -2148,7 +2150,7 @@ }, { "cell_type": "markdown", - "id": "99837971", + "id": "1152ea5e", "metadata": { "editable": true }, @@ -2166,7 +2168,7 @@ }, { "cell_type": "markdown", - "id": "16029266", + "id": "4f3d4b33", "metadata": { "editable": true }, @@ -2179,7 +2181,7 @@ }, { "cell_type": "markdown", - "id": "cc602c6c", + "id": "4c1ac54e", "metadata": { "editable": true }, @@ -2199,7 +2201,7 @@ }, { "cell_type": "markdown", - "id": "2b825937", + "id": "5c4a861f", "metadata": { "editable": true }, @@ -2232,7 +2234,7 @@ }, { "cell_type": "markdown", - "id": "8ef00888", + "id": "276b271b", "metadata": { "editable": true }, @@ -2244,7 +2246,7 @@ }, { "cell_type": "markdown", - "id": "2617b084", + "id": "63a5b8f1", "metadata": { "editable": true }, @@ -2263,7 +2265,7 @@ }, { "cell_type": "markdown", - "id": "c42c607f", + "id": "316b8c32", "metadata": { "editable": true }, @@ -2277,7 +2279,7 @@ }, { "cell_type": "markdown", - "id": "ca6286ac", + "id": "34ba90c8", "metadata": { "editable": true }, @@ -2300,7 +2302,7 @@ }, { "cell_type": "markdown", - "id": "cb2c654a", + "id": "3019fcaf", "metadata": { "editable": true }, @@ -2319,7 +2321,7 @@ }, { "cell_type": "markdown", - "id": "01494130", + "id": "389ff36b", "metadata": { "editable": true }, @@ -2331,7 +2333,7 @@ }, { "cell_type": "markdown", - "id": "8d4f8bee", + "id": "ee9b399a", "metadata": { "editable": true }, @@ -2341,7 +2343,7 @@ }, { "cell_type": "markdown", - "id": "4d9c8595", + "id": "36f98b26", "metadata": { "editable": true }, @@ -2353,7 +2355,7 @@ }, { "cell_type": "markdown", - "id": "6a5663c8", + "id": "cb7b8839", "metadata": { "editable": true }, @@ -2370,7 +2372,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "2f4f91b8", + "id": "db8d28b5", "metadata": { "collapsed": false, "editable": true diff --git a/doc/LectureNotes/_build/html/searchindex.js b/doc/LectureNotes/_build/html/searchindex.js index 51060b2ae..cfe7a3ffd 100644 --- a/doc/LectureNotes/_build/html/searchindex.js +++ b/doc/LectureNotes/_build/html/searchindex.js @@ -1 +1 @@ -Search.setIndex({"alltitles": {"1a)": [[18, "a"]], "3a)": [[18, "id1"]], "3b)": [[18, "b"]], "4a)": [[18, "id2"]], "4b)": [[18, "id3"]], "A Classification Tree": [[9, "a-classification-tree"]], "A Frequentist approach to data analysis": [[0, "a-frequentist-approach-to-data-analysis"], [28, "a-frequentist-approach-to-data-analysis"]], "A better approach": [[8, "a-better-approach"]], "A first summary": [[28, "a-first-summary"]], "A more compact expression": [[33, "a-more-compact-expression"], [34, "a-more-compact-expression"]], "A new Cost Function": [[32, "a-new-cost-function"]], "A quick Reminder on Lagrangian Multipliers": [[8, "a-quick-reminder-on-lagrangian-multipliers"]], "A simple example": [[4, "a-simple-example"]], "A soft classifier": [[8, "a-soft-classifier"]], "A top-down perspective on Neural networks": [[1, "a-top-down-perspective-on-neural-networks"]], "A way to Read the Bias-Variance Tradeoff": [[32, "a-way-to-read-the-bias-variance-tradeoff"], [33, "a-way-to-read-the-bias-variance-tradeoff"]], "ADAM algorithm, taken from Goodfellow et al": [[31, "adam-algorithm-taken-from-goodfellow-et-al"]], "ADAM optimizer": [[13, "adam-optimizer"], [31, "id2"]], "Accuracy": [[31, "accuracy"]], "Activation functions": [[12, "activation-functions"], [34, "activation-functions"]], "Activation functions, Logistic and Hyperbolic ones": [[34, "activation-functions-logistic-and-hyperbolic-ones"]], "AdaGrad Properties": [[31, "adagrad-properties"]], "AdaGrad Update Rule Derivation": [[31, "adagrad-update-rule-derivation"]], "AdaGrad algorithm, taken from Goodfellow et al": [[31, "adagrad-algorithm-taken-from-goodfellow-et-al"]], "Adam Optimizer": [[31, "adam-optimizer"]], "Adam vs. AdaGrad and RMSProp": [[31, "adam-vs-adagrad-and-rmsprop"]], "Adam: Bias Correction": [[31, "adam-bias-correction"]], "Adam: Exponential Moving Averages (Moments)": [[31, "adam-exponential-moving-averages-moments"]], "Adam: Update Rule Derivation": [[31, "adam-update-rule-derivation"]], "Adaptive boosting: AdaBoost, Basic Algorithm": [[10, "adaptive-boosting-adaboost-basic-algorithm"]], "Adaptivity Across Dimensions": [[31, "adaptivity-across-dimensions"]], "Adding Neural Networks": [[34, "adding-neural-networks"]], "Adding error analysis and training set up": [[28, "adding-error-analysis-and-training-set-up"], [29, "adding-error-analysis-and-training-set-up"]], "Adjust hyperparameters": [[1, "adjust-hyperparameters"]], "Algorithms and codes for Adagrad, RMSprop and Adam": [[31, "algorithms-and-codes-for-adagrad-rmsprop-and-adam"]], "Algorithms for Setting up Decision Trees": [[9, "algorithms-for-setting-up-decision-trees"]], "An Overview of Ensemble Methods": [[10, "an-overview-of-ensemble-methods"]], "An extrapolation example": [[4, "an-extrapolation-example"]], "An optimization/minimization problem": [[28, "an-optimization-minimization-problem"]], "And finally \\boldsymbol{X}\\boldsymbol{X}^T": [[29, "and-finally-boldsymbol-x-boldsymbol-x-t"]], "And finally ADAM": [[31, "and-finally-adam"]], "And what about using neural networks?": [[28, "and-what-about-using-neural-networks"]], "Another Example from Scikit-Learn\u2019s Repository": [[32, "another-example-from-scikit-learn-s-repository"], [33, "another-example-from-scikit-learn-s-repository"]], "Another Example, now with a polynomial fit": [[30, "another-example-now-with-a-polynomial-fit"]], "Another example, the moons again": [[9, "another-example-the-moons-again"]], "Applied Data Analysis and Machine Learning": [[21, null]], "Artificial neurons": [[34, "artificial-neurons"]], "Assumptions made": [[32, "assumptions-made"]], "Autocorrelation function": [[25, "autocorrelation-function"]], "Automatic differentiation": [[13, "automatic-differentiation"]], "Back to Ridge and LASSO Regression": [[29, "back-to-ridge-and-lasso-regression"], [30, "back-to-ridge-and-lasso-regression"]], "Back to the Cancer Data": [[11, "back-to-the-cancer-data"]], "Background literature": [[23, "background-literature"]], "Bagging": [[10, "bagging"]], "Bagging Examples": [[10, "bagging-examples"]], "Basic Matrix Features": [[22, "basic-matrix-features"]], "Basic ideas of the Principal 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Newton\u2019s method": [[31, "but-none-of-these-can-compete-with-newton-s-method"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[3, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Cancer Data again now with Decision Trees and other Methods": [[9, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Challenge: Choosing a Fixed Learning Rate": [[31, "challenge-choosing-a-fixed-learning-rate"]], "Choose cost function and optimizer": [[1, "choose-cost-function-and-optimizer"]], "Classical PCA Theorem": [[11, "classical-pca-theorem"]], "Classification problems": [[33, "classification-problems"], [34, "classification-problems"]], "Clustering and Unsupervised Learning": [[14, null]], "Code Example for Cross-validation and k-fold Cross-validation": [[32, "code-example-for-cross-validation-and-k-fold-cross-validation"], [33, "code-example-for-cross-validation-and-k-fold-cross-validation"]], "Code example for the Bootstrap method": [[32, "code-example-for-the-bootstrap-method"]], "Code for SVD and Inversion of Matrices": [[5, "code-for-svd-and-inversion-of-matrices"]], "Code with a Number of Minibatches which varies": [[31, "code-with-a-number-of-minibatches-which-varies"]], "Codes and Approaches": [[14, "codes-and-approaches"]], "Codes for the SVD": [[5, "codes-for-the-svd"], [29, "codes-for-the-svd"], [30, "codes-for-the-svd"]], "Coding Setup and Linear Regression": [[15, "coding-setup-and-linear-regression"]], "Collect and pre-process data": [[1, "collect-and-pre-process-data"]], "Communication channels": [[28, "communication-channels"]], "Compare Bagging on Trees with Random Forests": [[10, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[2, "comparing-with-a-numerical-scheme"]], "Comparison with OLS": [[30, "comparison-with-ols"]], "Computation of gradients": [[31, "computation-of-gradients"]], "Computing the Gini index": [[9, "computing-the-gini-index"]], "Conditions on convex functions": [[30, "conditions-on-convex-functions"]], "Confidence Intervals": [[32, "confidence-intervals"]], "Conjugate gradient method": [[13, "conjugate-gradient-method"]], "Convergence rates": [[31, "convergence-rates"]], "Convex function": [[30, "convex-function"]], "Convex functions": [[13, "convex-functions"], [30, "convex-functions"]], "Convolution Examples: Polynomial multiplication": [[3, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[3, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolutional Neural Network": [[12, "convolutional-neural-network"], [34, "convolutional-neural-network"]], "Convolutional Neural Networks": [[3, null]], "Correlation Function and Design/Feature Matrix": [[29, "correlation-function-and-design-feature-matrix"]], "Correlation Matrix": [[11, "correlation-matrix"], [29, "correlation-matrix"]], "Correlation Matrix with Pandas": [[29, "correlation-matrix-with-pandas"]], "Course Format": [[28, "course-format"]], "Course setting": [[24, null]], "Covariance Matrix Examples": [[29, "covariance-matrix-examples"]], "Covariance and Correlation Matrix": [[29, "covariance-and-correlation-matrix"]], "Cross-validation": [[6, "cross-validation"]], "Cross-validation in brief": [[32, "cross-validation-in-brief"], [33, "cross-validation-in-brief"]], "Deadlines for projects (tentative)": [[28, "deadlines-for-projects-tentative"]], "Decision trees, overarching aims": [[9, null]], "Deep Neural Networks": [[31, "deep-neural-networks"]], "Deep learning methods": [[28, "deep-learning-methods"]], "Define model and architecture": [[1, "define-model-and-architecture"]], "Defining the cost function": [[1, "defining-the-cost-function"]], "Definitions": [[19, "definitions"]], "Deliverables": [[15, "deliverables"], [16, "deliverables"], [19, "deliverables"], [20, "deliverables"], [23, "deliverables"]], "Derivation of the AdaGrad Algorithm": [[31, "derivation-of-the-adagrad-algorithm"]], "Derivatives and the chain rule": [[12, "derivatives-and-the-chain-rule"]], "Derivatives, example 1": [[29, "derivatives-example-1"]], "Deriving OLS from a probability distribution": [[5, "deriving-ols-from-a-probability-distribution"], [32, "deriving-ols-from-a-probability-distribution"]], "Deriving and Implementing Ordinary Least Squares": [[16, "deriving-and-implementing-ordinary-least-squares"]], "Deriving and Implementing Ridge Regression": [[17, "deriving-and-implementing-ridge-regression"]], "Deriving the Lasso Regression Equations": [[29, "deriving-the-lasso-regression-equations"], [30, "deriving-the-lasso-regression-equations"], [30, "id6"]], "Deriving the Ridge Regression Equations": [[29, "deriving-the-ridge-regression-equations"], [30, "deriving-the-ridge-regression-equations"], [30, "id3"]], "Deriving the back propagation code for a multilayer perceptron model": [[12, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Developing a code for doing neural networks with back propagation": [[1, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[11, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[8, "different-kernels-and-mercer-s-theorem"]], "Disadvantages": [[9, "disadvantages"]], "Discriminative Modeling": [[28, "discriminative-modeling"]], "Discussing the correlation data": [[34, "discussing-the-correlation-data"]], "Does Logistic Regression do a better Job?": [[34, "does-logistic-regression-do-a-better-job"]], "Domains and probabilities": [[25, "domains-and-probabilities"]], "Dropout": [[1, "dropout"]], "Economy-size SVD": [[29, "economy-size-svd"], [30, "economy-size-svd"]], "Elements of Probability Theory and Statistical Data Analysis": [[25, null]], "Empirical Evidence: Convergence Time and Memory in Practice": [[31, "empirical-evidence-convergence-time-and-memory-in-practice"]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[10, null]], "Entropy and the ID3 algorithm": [[9, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[28, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[1, "evaluate-model-performance-on-test-data"]], "Example 2": [[29, "example-2"]], "Example 3": [[29, "example-3"]], "Example 4": [[29, "example-4"]], "Example Matrix": [[29, "example-matrix"], [30, "example-matrix"]], "Example code for Bias-Variance tradeoff": [[32, "example-code-for-bias-variance-tradeoff"]], "Example code for Logistic Regression": [[33, "example-code-for-logistic-regression"], [34, "example-code-for-logistic-regression"]], "Example of discriminative modeling, taken from Generative Deep Learning by David Foster": [[28, "example-of-discriminative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example of generative modeling, taken from Generative Deep Learning by David Foster": [[28, "example-of-generative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example of own Standard scaling": [[29, "example-of-own-standard-scaling"]], "Example relevant for the exercises": [[29, "example-relevant-for-the-exercises"]], "Example: Exponential decay": [[2, "example-exponential-decay"]], "Example: Population growth": [[2, "example-population-growth"]], "Example: The diffusion equation": [[2, "example-the-diffusion-equation"]], "Example: binary classification problem": [[1, "example-binary-classification-problem"]], "Examples": [[28, "examples"]], "Examples of XOR, OR and AND gates": [[34, "examples-of-xor-or-and-and-gates"]], "Examples of likelihood functions used in logistic regression and neural networks": [[7, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Examples of likelihood functions used in logistic regression and nueral networks": [[33, "examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks"]], "Exercise 1 - Choice of model and degrees of freedom": [[17, "exercise-1-choice-of-model-and-degrees-of-freedom"]], "Exercise 1 - Finding the derivative of Matrix-Vector expressions": [[16, "exercise-1-finding-the-derivative-of-matrix-vector-expressions"]], "Exercise 1 - Github Setup": [[15, "exercise-1-github-setup"]], "Exercise 1, scale your data": [[18, "exercise-1-scale-your-data"]], "Exercise 1: Creating the report document": [[20, "exercise-1-creating-the-report-document"]], "Exercise 1: Expectation values for ordinary least squares expressions": [[19, "exercise-1-expectation-values-for-ordinary-least-squares-expressions"]], "Exercise 1: Setting up various Python environments": [[0, "exercise-1-setting-up-various-python-environments"]], "Exercise 2 - Deriving the expression for OLS": [[16, "exercise-2-deriving-the-expression-for-ols"]], "Exercise 2 - Deriving the expression for Ridge Regression": [[17, "exercise-2-deriving-the-expression-for-ridge-regression"]], "Exercise 2 - Setting up a Github repository": [[15, "exercise-2-setting-up-a-github-repository"]], "Exercise 2, calculate the gradients": [[18, "exercise-2-calculate-the-gradients"]], "Exercise 2: Adding good figures": [[20, "exercise-2-adding-good-figures"]], "Exercise 2: Expectation values for Ridge regression": [[19, "exercise-2-expectation-values-for-ridge-regression"]], "Exercise 2: making your own data and exploring scikit-learn": [[0, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3 - Creating feature matrix and implementing OLS using the analytical expression": [[16, "exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression"]], "Exercise 3 - Fitting an OLS model to data": [[15, "exercise-3-fitting-an-ols-model-to-data"]], "Exercise 3 - Scaling data": [[17, "exercise-3-scaling-data"]], "Exercise 3 - Setting up a Python virtual environment": [[15, "exercise-3-setting-up-a-python-virtual-environment"]], "Exercise 3, using the analytical formulae for OLS and Ridge regression to find the optimal paramters \\boldsymbol{\\theta}": [[18, "exercise-3-using-the-analytical-formulae-for-ols-and-ridge-regression-to-find-the-optimal-paramters-boldsymbol-theta"]], "Exercise 3: Deriving the expression for the Bias-Variance Trade-off": [[19, "exercise-3-deriving-the-expression-for-the-bias-variance-trade-off"]], "Exercise 3: Normalizing our data": [[0, "exercise-3-normalizing-our-data"]], "Exercise 3: Writing an abstract and introduction": [[20, "exercise-3-writing-an-abstract-and-introduction"]], "Exercise 4 - Fitting a polynomial": [[16, "exercise-4-fitting-a-polynomial"]], "Exercise 4 - Implementing Ridge Regression": [[17, "exercise-4-implementing-ridge-regression"]], "Exercise 4 - Testing multiple hyperparameters": [[17, "exercise-4-testing-multiple-hyperparameters"]], "Exercise 4 - The train-test split": [[15, "exercise-4-the-train-test-split"]], "Exercise 4, Implementing the simplest form for gradient descent": [[18, "exercise-4-implementing-the-simplest-form-for-gradient-descent"]], "Exercise 4: Adding Ridge Regression": [[0, "exercise-4-adding-ridge-regression"]], "Exercise 4: Computing the Bias and Variance": [[19, "exercise-4-computing-the-bias-and-variance"]], "Exercise 4: Making the code available and presentable": [[20, "exercise-4-making-the-code-available-and-presentable"]], "Exercise 5 - Comparing your code with sklearn": [[16, "exercise-5-comparing-your-code-with-sklearn"]], "Exercise 5, Ridge regression and a new Synthetic Dataset": [[18, "exercise-5-ridge-regression-and-a-new-synthetic-dataset"]], "Exercise 5: Analytical exercises": [[0, "exercise-5-analytical-exercises"]], "Exercise 5: Interpretation of scaling and metrics": [[19, "exercise-5-interpretation-of-scaling-and-metrics"]], "Exercise 5: Referencing": [[20, "exercise-5-referencing"]], "Exercise: Cross-validation as resampling techniques, adding more complexity": [[6, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[6, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[6, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function with resampling": [[6, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[6, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function with resampling": [[6, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[0, "exercises"]], "Exercises and Projects": [[6, "exercises-and-projects"]], "Exercises week 34": [[15, null]], "Exercises week 35": [[16, null]], "Exercises week 36": [[17, null]], "Exercises week 37": [[18, null]], "Exercises week 38": [[19, null]], "Exercises week 39": [[20, null]], "Expectation value and variance": [[32, "expectation-value-and-variance"]], "Expectation value and variance for \\boldsymbol{\\theta}": [[32, "expectation-value-and-variance-for-boldsymbol-theta"]], "Expectation values": [[25, "expectation-values"]], "Extending to more predictors": [[33, "extending-to-more-predictors"], [34, "extending-to-more-predictors"]], "Extending to more than one variable": [[30, "extending-to-more-than-one-variable"]], "Extremely useful tools, strongly recommended": [[28, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[12, "feed-forward-neural-networks"], [34, "feed-forward-neural-networks"]], "Feed-forward pass": [[1, "feed-forward-pass"]], "Final back propagating equation": [[12, "final-back-propagating-equation"]], "Finding the Limit": [[32, "finding-the-limit"]], "Fine-tuning neural network hyperparameters": [[1, "fine-tuning-neural-network-hyperparameters"]], "Fitting an Equation of State for Dense Nuclear Matter": [[0, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "Fixing the singularity": [[29, "fixing-the-singularity"], [30, "fixing-the-singularity"]], "Format for electronic delivery of report and programs": [[23, "format-for-electronic-delivery-of-report-and-programs"]], "Frequently used scaling functions": [[29, "frequently-used-scaling-functions"], [31, "frequently-used-scaling-functions"]], "From OLS to Ridge and Lasso": [[30, "from-ols-to-ridge-and-lasso"]], "From one to many layers, the universal approximation theorem": [[12, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Functionality in Scikit-Learn": [[29, "functionality-in-scikit-learn"], [31, "functionality-in-scikit-learn"]], "Further Dimensionality Remarks": [[3, "further-dimensionality-remarks"]], "Further properties (important for our analyses later)": [[5, "further-properties-important-for-our-analyses-later"], [29, "further-properties-important-for-our-analyses-later"], [30, "further-properties-important-for-our-analyses-later"]], "Gaussian Elimination": [[22, "gaussian-elimination"]], "General Features": [[9, "general-features"]], "General linear models and linear algebra": [[28, "general-linear-models-and-linear-algebra"]], "Generalizing the fitting procedure as a linear algebra problem": [[28, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [28, "id1"]], "Generative Adversarial Networks": [[4, "generative-adversarial-networks"]], "Generative Models": [[4, "generative-models"]], "Generative Versus Discriminative Modeling": [[28, "generative-versus-discriminative-modeling"]], "Geometric Interpretation and link with Singular Value Decomposition": [[11, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Getting started with project 1": [[20, "getting-started-with-project-1"]], "Gradient Boosting, Classification Example": [[10, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[10, "gradient-boosting-examples-of-regression"]], "Gradient Clipping": [[1, "gradient-clipping"]], "Gradient Descent Example": [[30, "id1"], [31, "id1"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[10, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[2, "gradient-descent"]], "Gradient descent and Ridge": [[30, "gradient-descent-and-ridge"], [31, "gradient-descent-and-ridge"]], "Gradient descent and revisiting Ordinary Least Squares from last week": [[31, "gradient-descent-and-revisiting-ordinary-least-squares-from-last-week"]], "Gradient descent example": [[30, "gradient-descent-example"], [31, "gradient-descent-example"]], "Grading": [[26, "grading"], [26, "id2"], [28, "grading"]], "How to take derivatives of Matrix-Vector expressions": [[16, "how-to-take-derivatives-of-matrix-vector-expressions"]], "Hyperplanes and all that": [[8, "hyperplanes-and-all-that"]], "Identifying Terms": [[32, "identifying-terms"]], "Illustration of a single perceptron model and a multi-perceptron model": [[34, "illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model"]], "Important Matrix and vector handling packages": [[22, "important-matrix-and-vector-handling-packages"]], "Important technicalities: More on Rescaling data": [[29, "important-technicalities-more-on-rescaling-data"]], "Improving gradient descent with momentum": [[31, "improving-gradient-descent-with-momentum"]], "Improving performance": [[1, "improving-performance"]], "In summary": [[26, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[13, "including-stochastic-gradient-descent-with-autograd"], [31, "including-stochastic-gradient-descent-with-autograd"]], "Including more classes": [[33, "including-more-classes"], [34, "including-more-classes"]], "Incremental PCA": [[11, "incremental-pca"]], "Independent and Identically Distributed (iid)": [[32, "independent-and-identically-distributed-iid"]], "Installing R, C++, cython or Julia": [[28, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[28, "installing-r-c-cython-numba-etc"]], "Instructor information": [[26, "instructor-information"]], "Interpretations and optimizing our parameters": [[28, "interpretations-and-optimizing-our-parameters"], [28, "id2"], [28, "id3"], [29, "interpretations-and-optimizing-our-parameters"], [29, "id1"], [29, "id2"]], "Interpreting the Ridge results": [[29, "interpreting-the-ridge-results"], [30, "interpreting-the-ridge-results"], [30, "id4"]], "Introducing JAX": [[13, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[11, "introducing-the-covariance-and-correlation-functions"], [29, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[0, "introduction"], [6, "introduction"], [21, "introduction"], [22, "introduction"]], "Introduction to Neural networks": [[34, "introduction-to-neural-networks"]], "Introduction to numerical projects": [[23, "introduction-to-numerical-projects"]], "Iterative Fitting, Classification and AdaBoost": [[10, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[10, "iterative-fitting-regression-and-squared-error-cost-function"]], "Kernel PCA": [[11, "kernel-pca"]], "Kernels and non-linearity": [[8, "kernels-and-non-linearity"]], "LU Decomposition, the inverse of a matrix": [[22, "lu-decomposition-the-inverse-of-a-matrix"]], "Lab sessions Tuesday and Wednesday": [[34, "lab-sessions-tuesday-and-wednesday"]], "Lab sessions week 39": [[33, "lab-sessions-week-39"]], "Lasso Regression": [[30, "lasso-regression"]], "Lasso case": [[30, "lasso-case"]], "Layers": [[1, "layers"]], "Layers used to build CNNs": [[3, "layers-used-to-build-cnns"]], "Learning goals": [[15, "learning-goals"], [16, "learning-goals"], [17, "learning-goals"], [18, "learning-goals"], [19, "learning-goals"], [20, "learning-goals"]], "Learning outcomes": [[21, "learning-outcomes"], [28, "learning-outcomes"]], "Lecture Monday September 29, 2025": [[34, "lecture-monday-september-29-2025"]], "Lecture material": [[33, "lecture-material"]], "Lectures and ComputerLab": [[28, "lectures-and-computerlab"]], "Limitations of supervised learning with deep networks": [[1, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[22, null]], "Linear Regression": [[0, null]], "Linear Regression Problems": [[29, "linear-regression-problems"], [30, "linear-regression-problems"]], "Linear Regression and the SVD": [[30, "linear-regression-and-the-svd"]], "Linear Regression, basic elements": [[0, "linear-regression-basic-elements"]], "Linear classifier": [[33, "linear-classifier"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[5, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[5, "linking-the-regression-analysis-with-a-statistical-interpretation"], [32, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with the SVD": [[5, "linking-with-the-svd"], [29, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[27, "links-to-relevant-courses-at-the-university-of-oslo"]], "Logistic Regression": [[7, null], [7, "id1"], [33, "logistic-regression"]], "Logistic Regression, from last week": [[34, "logistic-regression-from-last-week"]], "MNIST and GANs": [[4, "mnist-and-gans"]], "Machine Learning": [[28, "machine-learning"]], "Machine learning": [[21, "machine-learning"]], "Main textbooks": [[28, "main-textbooks"]], "Making a tree": [[9, "making-a-tree"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[10, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Making your own test-train splitting": [[29, "making-your-own-test-train-splitting"]], "Material for exercises week 35": [[29, "material-for-exercises-week-35"]], "Material for lab sessions sessions Tuesday and Wednesday": [[30, "material-for-lab-sessions-sessions-tuesday-and-wednesday"]], "Material for lecture Monday September 2": [[30, "material-for-lecture-monday-september-2"]], "Material for lecture Monday September 8": [[31, "material-for-lecture-monday-september-8"]], "Material for the lab sessions": [[31, "material-for-the-lab-sessions"], [32, "material-for-the-lab-sessions"]], "Mathematical Interpretation of Ordinary Least Squares": [[5, "mathematical-interpretation-of-ordinary-least-squares"], [29, "mathematical-interpretation-of-ordinary-least-squares"], [30, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical model": [[34, "mathematical-model"], [34, "id1"], [34, "id2"], [34, "id3"], [34, "id4"]], "Mathematical optimization of convex functions": [[8, "mathematical-optimization-of-convex-functions"]], "Mathematics of CNNs": [[3, "mathematics-of-cnns"]], "Mathematics of the SVD and implications": [[5, "mathematics-of-the-svd-and-implications"], [29, "mathematics-of-the-svd-and-implications"], [30, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[28, "matrices-in-python"]], "Matrix multiplication": [[1, "matrix-multiplication"]], "Matrix-vector notation": [[34, "matrix-vector-notation"]], "Matrix-vector notation and activation": [[12, "matrix-vector-notation-and-activation"], [34, "matrix-vector-notation-and-activation"]], "Maximum Likelihood Estimation (MLE)": [[32, "maximum-likelihood-estimation-mle"]], "Maximum likelihood": [[33, "maximum-likelihood"], [34, "maximum-likelihood"]], "Meet the covariance!": [[25, "meet-the-covariance"]], "Meet the Covariance Matrix": [[5, "meet-the-covariance-matrix"], [29, "meet-the-covariance-matrix"]], "Meet the Hessian Matrix": [[29, "meet-the-hessian-matrix"]], "Meet the Pandas": [[28, "meet-the-pandas"]], "Memory Usage and Scalability": [[31, "memory-usage-and-scalability"]], "Memory constraints": [[31, "memory-constraints"]], "Min-Max Scaling": [[29, "min-max-scaling"]], "Minimizing the cross entropy": [[33, "minimizing-the-cross-entropy"], [34, "minimizing-the-cross-entropy"]], "Momentum based GD": [[13, "momentum-based-gd"], [31, "momentum-based-gd"]], "More classes": [[33, "more-classes"], [34, "more-classes"]], "More complicated Example: The Ising model": [[6, "more-complicated-example-the-ising-model"]], "More examples on bootstrap and cross-validation and errors": [[32, "more-examples-on-bootstrap-and-cross-validation-and-errors"], [33, "more-examples-on-bootstrap-and-cross-validation-and-errors"]], "More interpretations": [[29, "more-interpretations"], [30, "more-interpretations"], [30, "id5"]], "More on Dimensionalities": [[3, "more-on-dimensionalities"]], "More on Rescaling data": [[6, "more-on-rescaling-data"]], "More on Steepest descent": [[30, "more-on-steepest-descent"]], "More on convex functions": [[30, "more-on-convex-functions"]], "More preprocessing": [[29, "more-preprocessing"], [31, "more-preprocessing"]], "Motivation for Adaptive Step Sizes": [[31, "motivation-for-adaptive-step-sizes"]], "Multilayer perceptrons": [[12, "multilayer-perceptrons"], [34, "multilayer-perceptrons"]], "Network requirements": [[2, "network-requirements"]], "Neural Networks vs CNNs": [[3, "neural-networks-vs-cnns"]], "Neural network types": [[34, "neural-network-types"]], "Neural networks": [[12, null]], "Non-Convex Problems": [[31, "non-convex-problems"]], "Note about SVD Calculations": [[29, "note-about-svd-calculations"], [30, "note-about-svd-calculations"]], "Note on Scikit-Learn": [[30, "note-on-scikit-learn"]], "Numerical experiments and the covariance, central limit theorem": [[25, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[22, "numpy-and-arrays"], [28, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[28, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization and Deep learning": [[33, "optimization-and-deep-learning"], [34, "optimization-and-deep-learning"]], "Optimization and gradient descent, the central part of any Machine Learning algortithm": [[30, "optimization-and-gradient-descent-the-central-part-of-any-machine-learning-algortithm"]], "Optimization, the central part of any Machine Learning algortithm": [[13, null], [33, "optimization-the-central-part-of-any-machine-learning-algortithm"], [34, "optimization-the-central-part-of-any-machine-learning-algortithm"]], "Optimizing our parameters": [[28, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[28, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[1, "optimizing-the-cost-function"]], "Organizing our data": [[0, "organizing-our-data"], [28, "organizing-our-data"]], "Other Matrix and Vector Operations": [[22, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[4, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning at UiO": [[28, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning at UiO, contn": [[28, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other measures in classification studies": [[34, "other-measures-in-classification-studies"]], "Other popular texts": [[28, "other-popular-texts"]], "Other techniques": [[11, "other-techniques"]], "Other types of networks": [[12, "other-types-of-networks"], [34, "other-types-of-networks"]], "Other ways of visualizing the trees": [[9, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[28, "our-model-for-the-nuclear-binding-energies"]], "Overview of first week": [[28, "overview-of-first-week"]], "Overview video on Stochastic Gradient Descent (SGD)": [[31, "overview-video-on-stochastic-gradient-descent-sgd"]], "Own code for Ordinary Least Squares": [[28, "own-code-for-ordinary-least-squares"], [29, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[11, "pca-and-scikit-learn"]], "Pandas AI": [[28, "pandas-ai"]], "Part a : Ordinary Least Square (OLS) for the Runge function": [[23, "part-a-ordinary-least-square-ols-for-the-runge-function"]], "Part b: Adding Ridge regression for the Runge function": [[23, "part-b-adding-ridge-regression-for-the-runge-function"]], "Part c: Writing your own gradient descent code": [[23, "part-c-writing-your-own-gradient-descent-code"]], "Part d: Including momentum and more advanced ways to update the learning the rate": [[23, "part-d-including-momentum-and-more-advanced-ways-to-update-the-learning-the-rate"]], "Part e: Writing our own code for Lasso regression": [[23, "part-e-writing-our-own-code-for-lasso-regression"]], "Part f: Stochastic gradient descent": [[23, "part-f-stochastic-gradient-descent"]], "Part g: Bias-variance trade-off and resampling techniques": [[23, "part-g-bias-variance-trade-off-and-resampling-techniques"]], "Part h): Cross-validation as resampling techniques, adding more complexity": [[23, "part-h-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Partial Differential Equations": [[2, "partial-differential-equations"]], "Plan for week 39, September 22-26, 2025": [[33, "plan-for-week-39-september-22-26-2025"]], "Plans for week 35": [[29, "plans-for-week-35"]], "Plans for week 36": [[30, "plans-for-week-36"]], "Plans for week 37, lecture Monday": [[31, "plans-for-week-37-lecture-monday"]], "Plans for week 38, lecture Monday September 15": [[32, "plans-for-week-38-lecture-monday-september-15"]], "Plotting the Histogram": [[32, "plotting-the-histogram"]], "Plotting the mean value for each group": [[33, "plotting-the-mean-value-for-each-group"]], "Practical tips": [[13, "practical-tips"], [31, "practical-tips"]], "Practicalities": [[26, "practicalities"], [26, "id1"]], "Preamble: Note on writing reports, using reference material, AI and other tools": [[23, "preamble-note-on-writing-reports-using-reference-material-ai-and-other-tools"]], "Predicting New Points With A Trained Recurrent Neural Network": [[4, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Preprocessing our data": [[29, "preprocessing-our-data"]], "Prerequisites": [[28, "prerequisites"]], "Prerequisites and background": [[21, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[3, "prerequisites-collect-and-pre-process-data"]], "Probability Distribution Functions": [[25, "probability-distribution-functions"]], "Program example for gradient descent with Ridge Regression": [[30, "program-example-for-gradient-descent-with-ridge-regression"], [31, "program-example-for-gradient-descent-with-ridge-regression"]], "Program for stochastic gradient": [[13, "program-for-stochastic-gradient"]], "Project 1 on Machine Learning, deadline October 6 (midnight), 2025": [[23, null]], "Properties of PDFs": [[25, "properties-of-pdfs"]], "Pros and cons": [[31, "pros-and-cons"]], "Pros and cons of trees, pros": [[9, "pros-and-cons-of-trees-pros"]], "Python installers": [[21, "python-installers"], [28, "python-installers"]], "RMS prop": [[13, "rms-prop"]], "RMSProp algorithm, taken from Goodfellow et al": [[31, "rmsprop-algorithm-taken-from-goodfellow-et-al"]], "RMSProp: Adaptive Learning Rates": [[31, "rmsprop-adaptive-learning-rates"]], "RMSprop for adaptive learning rate with Stochastic Gradient Descent": [[31, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"]], "Random Numbers": [[25, "random-numbers"]], "Random forests": [[10, "random-forests"]], "Randomized PCA": [[11, "randomized-pca"]], "Reading material": [[28, "reading-material"]], "Reading recommendations:": [[29, "reading-recommendations"]], "Reading suggestions week 34": [[28, "reading-suggestions-week-34"]], "Readings and Videos": [[32, "readings-and-videos"]], "Readings and Videos, logistic regression": [[33, "readings-and-videos-logistic-regression"]], "Readings and Videos, resampling methods": [[33, "readings-and-videos-resampling-methods"]], "Readings and Videos:": [[31, "readings-and-videos"]], "Recurrent neural networks": [[12, "recurrent-neural-networks"], [34, "recurrent-neural-networks"]], "Recurrent neural networks: Overarching view": [[4, null]], "Reducing the number of degrees of freedom, overarching view": [[0, "reducing-the-number-of-degrees-of-freedom-overarching-view"], [29, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reformulating the problem": [[2, "reformulating-the-problem"]], "Regression Case": [[10, "regression-case"]], "Regression analysis and resampling methods": [[23, "regression-analysis-and-resampling-methods"]], "Regression analysis, overarching aims": [[28, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[28, "regression-analysis-overarching-aims-ii"]], "Regularization": [[1, "regularization"]], "Relevance": [[34, "relevance"]], "Reminder from last week": [[29, "reminder-from-last-week"]], "Reminder on Newton-Raphson\u2019s method": [[30, "reminder-on-newton-raphson-s-method"]], "Reminder on Statistics": [[6, "reminder-on-statistics"]], "Reminder on different scaling methods": [[31, "reminder-on-different-scaling-methods"]], "Replace or not": [[13, "replace-or-not"], [31, "replace-or-not"]], "Required Technologies": [[21, "required-technologies"]], "Resampling Methods": [[6, null]], "Resampling and the Bias-Variance Trade-off": [[19, "resampling-and-the-bias-variance-trade-off"]], "Resampling approaches can be computationally expensive": [[32, "resampling-approaches-can-be-computationally-expensive"], [33, "resampling-approaches-can-be-computationally-expensive"]], "Resampling methods": [[6, "id1"], [32, "resampling-methods"], [32, "id2"], [33, "resampling-methods"], [33, "id1"]], "Resampling methods: Bootstrap": [[32, "resampling-methods-bootstrap"], [33, "resampling-methods-bootstrap"]], "Resampling methods: Bootstrap approach": [[32, "resampling-methods-bootstrap-approach"]], "Resampling methods: Bootstrap background": [[32, "resampling-methods-bootstrap-background"]], "Resampling methods: Bootstrap steps": [[32, "resampling-methods-bootstrap-steps"]], "Resampling methods: More Bootstrap background": [[32, "resampling-methods-more-bootstrap-background"]], "Residual Error": [[29, "residual-error"], [30, "residual-error"]], "Resources on differential equations and deep learning": [[2, "resources-on-differential-equations-and-deep-learning"]], "Revisiting Ordinary Least Squares": [[30, "revisiting-ordinary-least-squares"]], "Revisiting our Linear Regression Solvers": [[13, "revisiting-our-linear-regression-solvers"]], "Revisiting our Logistic Regression case": [[33, "revisiting-our-logistic-regression-case"], [34, "revisiting-our-logistic-regression-case"]], "Rewriting the Covariance and/or Correlation Matrix": [[29, "rewriting-the-covariance-and-or-correlation-matrix"]], "Rewriting the \\delta-function": [[32, "rewriting-the-delta-function"]], "Rewriting the fitting procedure as a linear algebra problem": [[28, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[28, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge Regression": [[30, "ridge-regression"]], "Ridge and LASSO Regression": [[29, "ridge-and-lasso-regression"], [30, "ridge-and-lasso-regression"], [30, "id2"]], "Ridge and Lasso Regression": [[5, null], [5, "id1"]], "SGD example": [[31, "sgd-example"]], "SGD vs Full-Batch GD: Convergence Speed and Memory Comparison": [[31, "sgd-vs-full-batch-gd-convergence-speed-and-memory-comparison"]], "SVD analysis": [[30, "svd-analysis"]], "Same code but now with momentum gradient descent": [[13, "same-code-but-now-with-momentum-gradient-descent"], [31, "same-code-but-now-with-momentum-gradient-descent"], [31, "id3"], [31, "id4"]], "Schedule first week": [[28, "schedule-first-week"]], "Schematic Regression Procedure": [[9, "schematic-regression-procedure"]], "Second moment of the gradient": [[31, "second-moment-of-the-gradient"]], "September 15-19": [[19, "september-15-19"]], "Setting up the Back propagation algorithm": [[12, "setting-up-the-back-propagation-algorithm"]], "Setting up the Matrix to be inverted": [[29, "setting-up-the-matrix-to-be-inverted"], [30, "setting-up-the-matrix-to-be-inverted"]], "Setting up the network using Autograd; The full program": [[2, "setting-up-the-network-using-autograd-the-full-program"]], "Similar (second order function now) problem but now with AdaGrad": [[13, "similar-second-order-function-now-problem-but-now-with-adagrad"], [31, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[9, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple case": [[29, "simple-case"], [30, "simple-case"]], "Simple code for solving the above problem": [[30, "simple-code-for-solving-the-above-problem"]], "Simple example": [[33, "simple-example"]], "Simple example code": [[31, "simple-example-code"]], "Simple example to illustrate Ordinary Least Squares, Ridge and Lasso Regression": [[30, "simple-example-to-illustrate-ordinary-least-squares-ridge-and-lasso-regression"]], "Simple geometric interpretation": [[30, "simple-geometric-interpretation"]], "Simple linear regression model using scikit-learn": [[0, "simple-linear-regression-model-using-scikit-learn"], [28, "simple-linear-regression-model-using-scikit-learn"]], "Simple one-dimensional second-order polynomial": [[18, "simple-one-dimensional-second-order-polynomial"]], "Simple program": [[30, "simple-program"], [31, "simple-program"]], "Slightly different approach": [[31, "slightly-different-approach"]], "Sneaking in automatic differentiation using Autograd": [[31, "sneaking-in-automatic-differentiation-using-autograd"]], "Software and needed installations": [[23, "software-and-needed-installations"], [28, "software-and-needed-installations"]], "Solving Differential Equations with Deep Learning": [[2, null]], "Solving the one dimensional Poisson equation": [[2, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation with Neural Networks": [[2, "solving-the-wave-equation-with-neural-networks"]], "Solving using Newton-Raphson\u2019s method": [[33, "solving-using-newton-raphson-s-method"], [34, "solving-using-newton-raphson-s-method"]], "Some famous Matrices": [[22, "some-famous-matrices"]], "Some selected properties": [[33, "some-selected-properties"]], "Some simple problems": [[13, "some-simple-problems"], [30, "some-simple-problems"]], "Some useful matrix and vector expressions": [[29, "some-useful-matrix-and-vector-expressions"]], "Splitting our Data in Training and Test data": [[0, "splitting-our-data-in-training-and-test-data"], [29, "splitting-our-data-in-training-and-test-data"]], "Standard Approach based on the Normal Distribution": [[32, "standard-approach-based-on-the-normal-distribution"]], "Standard steepest descent": [[13, "standard-steepest-descent"]], "Statistical analysis": [[32, "statistical-analysis"], [33, "statistical-analysis"]], "Statistical analysis and optimization of data": [[21, "statistical-analysis-and-optimization-of-data"], [28, "statistical-analysis-and-optimization-of-data"]], "Steepest descent": [[13, "steepest-descent"], [30, "steepest-descent"]], "Stochastic Gradient Descent": [[31, "stochastic-gradient-descent"]], "Stochastic Gradient Descent (SGD)": [[13, "stochastic-gradient-descent-sgd"], [31, "stochastic-gradient-descent-sgd"]], "Stochastic variables and the main concepts, the discrete case": [[25, "stochastic-variables-and-the-main-concepts-the-discrete-case"]], "Strongly Convex Case": [[31, "strongly-convex-case"]], "Suggested readings and videos": [[34, "suggested-readings-and-videos"]], "Summing up": [[32, "summing-up"], [33, "summing-up"]], "Support Vector Machines, overarching aims": [[8, null]], "Synthetic data generation": [[33, "synthetic-data-generation"], [34, "synthetic-data-generation"]], "Systematic reduction": [[3, "systematic-reduction"]], "Teachers": [[28, "teachers"]], "Teachers and Grading": [[26, null]], "Teaching Assistants Fall semester 2023": [[26, "teaching-assistants-fall-semester-2023"]], "Tentative deadllines for projects": [[26, "tentative-deadllines-for-projects"]], "Testing the Means Squared Error as function of Complexity": [[0, "testing-the-means-squared-error-as-function-of-complexity"], [29, "testing-the-means-squared-error-as-function-of-complexity"]], "Textbooks": [[27, null]], "The Algorithm before theorem": [[11, "the-algorithm-before-theorem"]], "The Breast Cancer Data, now with Keras": [[1, "the-breast-cancer-data-now-with-keras"]], "The CART algorithm for Classification": [[9, "the-cart-algorithm-for-classification"]], "The CART algorithm for Regression": [[9, "the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[3, "the-cifar01-data-set"]], "The Central Limit Theorem": [[32, "the-central-limit-theorem"]], "The Hessian matrix": [[30, "the-hessian-matrix"], [31, "the-hessian-matrix"]], "The Hessian matrix for Ridge Regression": [[30, "the-hessian-matrix-for-ridge-regression"], [31, "the-hessian-matrix-for-ridge-regression"]], "The Jacobian": [[29, "the-jacobian"]], "The MNIST dataset again": [[3, "the-mnist-dataset-again"]], "The OLS case": [[30, "the-ols-case"]], "The RELU function family": [[1, "the-relu-function-family"]], "The Ridge case": [[30, "the-ridge-case"]], "The SVD, a Fantastic Algorithm": [[29, "the-svd-a-fantastic-algorithm"], [30, "the-svd-a-fantastic-algorithm"]], "The Softmax function": [[1, "the-softmax-function"]], "The \\chi^2 function": [[0, "the-chi-2-function"], [28, "the-chi-2-function"], [28, "id4"], [28, "id5"], [28, "id6"], [28, "id7"], [28, "id8"]], "The bias-variance tradeoff": [[6, "the-bias-variance-tradeoff"], [32, "the-bias-variance-tradeoff"], [33, "the-bias-variance-tradeoff"]], "The code for solving the ODE": [[2, "the-code-for-solving-the-ode"]], "The complete code with a simple data set": [[29, "the-complete-code-with-a-simple-data-set"]], "The cost function rewritten": [[33, "the-cost-function-rewritten"], [34, "the-cost-function-rewritten"]], "The cost/loss function": [[29, "the-cost-loss-function"]], "The course has two central parts": [[21, "the-course-has-two-central-parts"]], "The derivative of the cost/loss function": [[30, "the-derivative-of-the-cost-loss-function"], [31, "the-derivative-of-the-cost-loss-function"]], "The equations": [[30, "the-equations"]], "The equations for ordinary least squares": [[29, "the-equations-for-ordinary-least-squares"]], "The equations to solve": [[33, "the-equations-to-solve"], [34, "the-equations-to-solve"]], "The first Case": [[30, "the-first-case"]], "The gradient step": [[31, "the-gradient-step"]], "The ideal": [[30, "the-ideal"]], "The logistic function": [[7, "the-logistic-function"], [33, "the-logistic-function"]], "The mean squared error and its derivative": [[29, "the-mean-squared-error-and-its-derivative"]], "The moons example": [[8, "the-moons-example"]], "The multilayer perceptron (MLP)": [[12, "the-multilayer-perceptron-mlp"]], "The network with one input layer, specified number of hidden layers, and one output layer": [[2, "the-network-with-one-input-layer-specified-number-of-hidden-layers-and-one-output-layer"]], "The plethora of machine learning algorithms/methods": [[28, "the-plethora-of-machine-learning-algorithms-methods"]], "The same example but now with cross-validation": [[32, "the-same-example-but-now-with-cross-validation"], [33, "the-same-example-but-now-with-cross-validation"]], "The sensitiveness of the gradient descent": [[30, "the-sensitiveness-of-the-gradient-descent"]], "The singular value decomposition": [[5, "the-singular-value-decomposition"], [29, "the-singular-value-decomposition"], [30, "the-singular-value-decomposition"]], "The two-dimensional case": [[8, "the-two-dimensional-case"]], "Theoretical Convergence Speed and convex optimization": [[31, "theoretical-convergence-speed-and-convex-optimization"]], "Time decay rate": [[31, "time-decay-rate"]], 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Brief reminder on masses and binding energies": [[28, "to-our-real-data-nuclear-binding-energies-brief-reminder-on-masses-and-binding-energies"]], "Topics covered in this course: Statistical analysis and optimization of data": [[28, "topics-covered-in-this-course-statistical-analysis-and-optimization-of-data"]], "Towards the PCA theorem": [[11, "towards-the-pca-theorem"]], "Train and test datasets": [[1, "train-and-test-datasets"]], "Two parameters": [[33, "two-parameters"], [34, "two-parameters"]], "Two-dimensional Objects": [[3, "two-dimensional-objects"]], "Type of problem": [[2, "type-of-problem"]], "Types of Machine Learning": [[28, "types-of-machine-learning"]], "Understanding what happens": [[32, "understanding-what-happens"], [33, "understanding-what-happens"]], "Use the books!": [[19, "use-the-books"]], "Useful Python libraries": [[21, "useful-python-libraries"], [28, "useful-python-libraries"]], "Using Autograd": [[13, "using-autograd"]], "Using Scikit-learn": [[34, "using-scikit-learn"]], "Using forward Euler to solve the ODE": [[2, "using-forward-euler-to-solve-the-ode"]], "Using gradient descent methods, limitations": [[13, "using-gradient-descent-methods-limitations"], [30, "using-gradient-descent-methods-limitations"], [31, "using-gradient-descent-methods-limitations"]], "Using the correlation matrix": [[34, "using-the-correlation-matrix"]], "Various steps in cross-validation": [[32, "various-steps-in-cross-validation"], [33, "various-steps-in-cross-validation"]], "Visualization": [[1, "visualization"], [1, "id1"]], "Visualizing the Tree, Classification": [[9, "visualizing-the-tree-classification"]], "Week 34: Introduction to the course, Logistics and Practicalities": [[28, null]], "Week 35: From Ordinary Linear Regression to Ridge and Lasso Regression": [[29, null]], "Week 36: Linear Regression and Gradient descent": [[30, null]], "Week 37: Gradient descent methods": [[31, null]], "Week 38: Statistical analysis, bias-variance tradeoff and resampling methods": [[32, null]], "Week 39: Resampling methods and logistic regression": [[33, null]], "Week 40: Gradient descent methods (continued) and start Neural networks": [[34, null]], "What Is Generative Modeling?": [[28, "what-is-generative-modeling"]], "What does it mean?": [[29, "what-does-it-mean"], [30, "what-does-it-mean"]], "What is Machine Learning?": [[0, "what-is-machine-learning"]], "What is a good model?": [[0, "what-is-a-good-model"], [28, "what-is-a-good-model"]], "What is a good model? 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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", "Applied Data Analysis and Machine Learning", "2. Linear Algebra, Handling of Arrays and more Python Features", "Project 1 on Machine Learning, deadline October 6 (midnight), 2025", "Course setting", "1. 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"an-overview-of-ensemble-methods"]], "An extrapolation example": [[4, "an-extrapolation-example"]], "An optimization/minimization problem": [[28, "an-optimization-minimization-problem"]], "And finally \\boldsymbol{X}\\boldsymbol{X}^T": [[29, "and-finally-boldsymbol-x-boldsymbol-x-t"]], "And finally ADAM": [[31, "and-finally-adam"]], "And what about using neural networks?": [[28, "and-what-about-using-neural-networks"]], "Another Example from Scikit-Learn\u2019s Repository": [[32, "another-example-from-scikit-learn-s-repository"], [33, "another-example-from-scikit-learn-s-repository"]], "Another Example, now with a polynomial fit": [[30, "another-example-now-with-a-polynomial-fit"]], "Another example, the moons again": [[9, "another-example-the-moons-again"]], "Applied Data Analysis and Machine Learning": [[21, null]], "Artificial neurons": [[34, "artificial-neurons"]], "Assumptions made": [[32, "assumptions-made"]], "Autocorrelation function": [[25, "autocorrelation-function"]], "Automatic differentiation": [[13, "automatic-differentiation"]], "Back to Ridge and LASSO Regression": [[29, "back-to-ridge-and-lasso-regression"], [30, "back-to-ridge-and-lasso-regression"]], "Back to the Cancer Data": [[11, "back-to-the-cancer-data"]], "Background literature": [[23, "background-literature"]], "Bagging": [[10, "bagging"]], "Bagging Examples": [[10, "bagging-examples"]], "Basic Matrix Features": [[22, "basic-matrix-features"]], "Basic ideas of the Principal Component Analysis (PCA)": [[11, null]], "Basic math of the SVD": [[5, "basic-math-of-the-svd"], [29, "basic-math-of-the-svd"], [30, "basic-math-of-the-svd"]], "Basics": [[7, "basics"], [33, "basics"], [34, "basics"]], "Basics of a tree": [[9, "basics-of-a-tree"]], "Batch Normalization": [[1, "batch-normalization"]], "Batches and mini-batches": [[31, "batches-and-mini-batches"]], "Bayes\u2019 Theorem and Ridge and Lasso Regression": [[5, "bayes-theorem-and-ridge-and-lasso-regression"]], "Boosting, a Bird\u2019s Eye View": [[10, "boosting-a-bird-s-eye-view"]], "Bootstrap": [[6, "bootstrap"]], "Bringing it together, first back propagation equation": [[12, "bringing-it-together-first-back-propagation-equation"]], "Building a Feed Forward Neural Network": [[1, null]], "Building a tree, regression": [[9, "building-a-tree-regression"]], "Building neural networks in Tensorflow and Keras": [[1, "building-neural-networks-in-tensorflow-and-keras"]], "But none of these can compete with Newton\u2019s method": [[31, "but-none-of-these-can-compete-with-newton-s-method"]], "CNNs in more detail, building convolutional neural networks in Tensorflow and Keras": [[3, "cnns-in-more-detail-building-convolutional-neural-networks-in-tensorflow-and-keras"]], "Cancer Data again now with Decision Trees and other Methods": [[9, "cancer-data-again-now-with-decision-trees-and-other-methods"]], "Challenge: Choosing a Fixed Learning Rate": [[31, "challenge-choosing-a-fixed-learning-rate"]], "Choose cost function and optimizer": [[1, "choose-cost-function-and-optimizer"]], "Classical PCA Theorem": [[11, "classical-pca-theorem"]], "Classification problems": [[33, "classification-problems"], [34, "classification-problems"]], "Clustering and Unsupervised Learning": [[14, null]], "Code Example for Cross-validation and k-fold Cross-validation": [[32, "code-example-for-cross-validation-and-k-fold-cross-validation"], [33, "code-example-for-cross-validation-and-k-fold-cross-validation"]], "Code example for the Bootstrap method": [[32, "code-example-for-the-bootstrap-method"]], "Code for SVD and Inversion of Matrices": [[5, "code-for-svd-and-inversion-of-matrices"]], "Code with a Number of Minibatches which varies": [[31, "code-with-a-number-of-minibatches-which-varies"]], "Codes and Approaches": [[14, "codes-and-approaches"]], "Codes for the SVD": [[5, "codes-for-the-svd"], [29, "codes-for-the-svd"], [30, "codes-for-the-svd"]], "Coding Setup and Linear Regression": [[15, "coding-setup-and-linear-regression"]], "Collect and pre-process data": [[1, "collect-and-pre-process-data"]], "Communication channels": [[28, "communication-channels"]], "Compare Bagging on Trees with Random Forests": [[10, "compare-bagging-on-trees-with-random-forests"]], "Comparing with a numerical scheme": [[2, "comparing-with-a-numerical-scheme"]], "Comparison with OLS": [[30, "comparison-with-ols"]], "Computation of gradients": [[31, "computation-of-gradients"]], "Computing the Gini index": [[9, "computing-the-gini-index"]], "Conditions on convex functions": [[30, "conditions-on-convex-functions"]], "Confidence Intervals": [[32, "confidence-intervals"]], "Conjugate gradient method": [[13, "conjugate-gradient-method"]], "Convergence rates": [[31, "convergence-rates"]], "Convex function": [[30, "convex-function"]], "Convex functions": [[13, "convex-functions"], [30, "convex-functions"]], "Convolution Examples: Polynomial multiplication": [[3, "convolution-examples-polynomial-multiplication"]], "Convolution Examples: Principle of Superposition and Periodic Forces (Fourier Transforms)": [[3, "convolution-examples-principle-of-superposition-and-periodic-forces-fourier-transforms"]], "Convolutional Neural Network": [[12, "convolutional-neural-network"], [34, "convolutional-neural-network"]], "Convolutional Neural Networks": [[3, null]], "Correlation Function and Design/Feature Matrix": [[29, "correlation-function-and-design-feature-matrix"]], "Correlation Matrix": [[11, "correlation-matrix"], [29, "correlation-matrix"]], "Correlation Matrix with Pandas": [[29, "correlation-matrix-with-pandas"]], "Course Format": [[28, "course-format"]], "Course setting": [[24, null]], "Covariance Matrix Examples": [[29, "covariance-matrix-examples"]], "Covariance and Correlation Matrix": [[29, "covariance-and-correlation-matrix"]], "Cross-validation": [[6, "cross-validation"]], "Cross-validation in brief": [[32, "cross-validation-in-brief"], [33, "cross-validation-in-brief"]], "Deadlines for projects (tentative)": [[28, "deadlines-for-projects-tentative"]], "Decision trees, overarching aims": [[9, null]], "Deep Neural Networks": [[31, "deep-neural-networks"]], "Deep learning methods": [[28, "deep-learning-methods"]], "Define model and architecture": [[1, "define-model-and-architecture"]], "Defining the cost function": [[1, "defining-the-cost-function"]], "Definitions": [[19, "definitions"]], "Deliverables": [[15, "deliverables"], [16, "deliverables"], [19, "deliverables"], [20, "deliverables"], [23, "deliverables"]], "Derivation of the AdaGrad Algorithm": [[31, "derivation-of-the-adagrad-algorithm"]], "Derivatives and the chain rule": [[12, "derivatives-and-the-chain-rule"]], "Derivatives, example 1": [[29, "derivatives-example-1"]], "Deriving OLS from a probability distribution": [[5, "deriving-ols-from-a-probability-distribution"], [32, "deriving-ols-from-a-probability-distribution"]], "Deriving and Implementing Ordinary Least Squares": [[16, "deriving-and-implementing-ordinary-least-squares"]], "Deriving and Implementing Ridge Regression": [[17, "deriving-and-implementing-ridge-regression"]], "Deriving the Lasso Regression Equations": [[29, "deriving-the-lasso-regression-equations"], [30, "deriving-the-lasso-regression-equations"], [30, "id6"]], "Deriving the Ridge Regression Equations": [[29, "deriving-the-ridge-regression-equations"], [30, "deriving-the-ridge-regression-equations"], [30, "id3"]], "Deriving the back propagation code for a multilayer perceptron model": [[12, "deriving-the-back-propagation-code-for-a-multilayer-perceptron-model"]], "Developing a code for doing neural networks with back propagation": [[1, "developing-a-code-for-doing-neural-networks-with-back-propagation"]], "Diagonalize the sample covariance matrix to obtain the principal components": [[11, "diagonalize-the-sample-covariance-matrix-to-obtain-the-principal-components"]], "Different kernels and Mercer\u2019s theorem": [[8, "different-kernels-and-mercer-s-theorem"]], "Disadvantages": [[9, "disadvantages"]], "Discriminative Modeling": [[28, "discriminative-modeling"]], "Discussing the correlation data": [[34, "discussing-the-correlation-data"]], "Does Logistic Regression do a better Job?": [[34, "does-logistic-regression-do-a-better-job"]], "Domains and probabilities": [[25, "domains-and-probabilities"]], "Dropout": [[1, "dropout"]], "Economy-size SVD": [[29, "economy-size-svd"], [30, "economy-size-svd"]], "Elements of Probability Theory and Statistical Data Analysis": [[25, null]], "Empirical Evidence: Convergence Time and Memory in Practice": [[31, "empirical-evidence-convergence-time-and-memory-in-practice"]], "Ensemble Methods: From a Single Tree to Many Trees and Extreme Boosting, Meet the Jungle of Methods": [[10, null]], "Entropy and the ID3 algorithm": [[9, "entropy-and-the-id3-algorithm"]], "Essential elements of ML": [[28, "essential-elements-of-ml"]], "Evaluate model performance on test data": [[1, "evaluate-model-performance-on-test-data"]], "Example 2": [[29, "example-2"]], "Example 3": [[29, "example-3"]], "Example 4": [[29, "example-4"]], "Example Matrix": [[29, "example-matrix"], [30, "example-matrix"]], "Example code for Bias-Variance tradeoff": [[32, "example-code-for-bias-variance-tradeoff"]], "Example code for Logistic Regression": [[33, "example-code-for-logistic-regression"], [34, "example-code-for-logistic-regression"]], "Example of discriminative modeling, taken from Generative Deep Learning by David Foster": [[28, "example-of-discriminative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example of generative modeling, taken from Generative Deep Learning by David Foster": [[28, "example-of-generative-modeling-taken-from-generative-deep-learning-by-david-foster"]], "Example of own Standard scaling": [[29, "example-of-own-standard-scaling"]], "Example relevant for the exercises": [[29, "example-relevant-for-the-exercises"]], "Example: Exponential decay": [[2, "example-exponential-decay"]], "Example: Population growth": [[2, "example-population-growth"]], "Example: The diffusion equation": [[2, "example-the-diffusion-equation"]], "Example: binary classification problem": [[1, "example-binary-classification-problem"]], "Examples": [[28, "examples"]], "Examples of XOR, OR and AND gates": [[34, "examples-of-xor-or-and-and-gates"]], "Examples of likelihood functions used in logistic regression and neural networks": [[7, "examples-of-likelihood-functions-used-in-logistic-regression-and-neural-networks"]], "Examples of likelihood functions used in logistic regression and nueral networks": [[33, "examples-of-likelihood-functions-used-in-logistic-regression-and-nueral-networks"]], "Exercise 1 - Choice of model and degrees of freedom": [[17, "exercise-1-choice-of-model-and-degrees-of-freedom"]], "Exercise 1 - Finding the derivative of Matrix-Vector expressions": [[16, "exercise-1-finding-the-derivative-of-matrix-vector-expressions"]], "Exercise 1 - Github Setup": [[15, "exercise-1-github-setup"]], "Exercise 1, scale your data": [[18, "exercise-1-scale-your-data"]], "Exercise 1: Creating the report document": [[20, "exercise-1-creating-the-report-document"]], "Exercise 1: Expectation values for ordinary least squares expressions": [[19, "exercise-1-expectation-values-for-ordinary-least-squares-expressions"]], "Exercise 1: Setting up various Python environments": [[0, "exercise-1-setting-up-various-python-environments"]], "Exercise 2 - Deriving the expression for OLS": [[16, "exercise-2-deriving-the-expression-for-ols"]], "Exercise 2 - Deriving the expression for Ridge Regression": [[17, "exercise-2-deriving-the-expression-for-ridge-regression"]], "Exercise 2 - Setting up a Github repository": [[15, "exercise-2-setting-up-a-github-repository"]], "Exercise 2, calculate the gradients": [[18, "exercise-2-calculate-the-gradients"]], "Exercise 2: Adding good figures": [[20, "exercise-2-adding-good-figures"]], "Exercise 2: Expectation values for Ridge regression": [[19, "exercise-2-expectation-values-for-ridge-regression"]], "Exercise 2: making your own data and exploring scikit-learn": [[0, "exercise-2-making-your-own-data-and-exploring-scikit-learn"]], "Exercise 3 - Creating feature matrix and implementing OLS using the analytical expression": [[16, "exercise-3-creating-feature-matrix-and-implementing-ols-using-the-analytical-expression"]], "Exercise 3 - Fitting an OLS model to data": [[15, "exercise-3-fitting-an-ols-model-to-data"]], "Exercise 3 - Scaling data": [[17, "exercise-3-scaling-data"]], "Exercise 3 - Setting up a Python virtual environment": [[15, "exercise-3-setting-up-a-python-virtual-environment"]], "Exercise 3, using the analytical formulae for OLS and Ridge regression to find the optimal paramters \\boldsymbol{\\theta}": [[18, "exercise-3-using-the-analytical-formulae-for-ols-and-ridge-regression-to-find-the-optimal-paramters-boldsymbol-theta"]], "Exercise 3: Deriving the expression for the Bias-Variance Trade-off": [[19, "exercise-3-deriving-the-expression-for-the-bias-variance-trade-off"]], "Exercise 3: Normalizing our data": [[0, "exercise-3-normalizing-our-data"]], "Exercise 3: Writing an abstract and introduction": [[20, "exercise-3-writing-an-abstract-and-introduction"]], "Exercise 4 - Fitting a polynomial": [[16, "exercise-4-fitting-a-polynomial"]], "Exercise 4 - Implementing Ridge Regression": [[17, "exercise-4-implementing-ridge-regression"]], "Exercise 4 - Testing multiple hyperparameters": [[17, "exercise-4-testing-multiple-hyperparameters"]], "Exercise 4 - The train-test split": [[15, "exercise-4-the-train-test-split"]], "Exercise 4, Implementing the simplest form for gradient descent": [[18, "exercise-4-implementing-the-simplest-form-for-gradient-descent"]], "Exercise 4: Adding Ridge Regression": [[0, "exercise-4-adding-ridge-regression"]], "Exercise 4: Computing the Bias and Variance": [[19, "exercise-4-computing-the-bias-and-variance"]], "Exercise 4: Making the code available and presentable": [[20, "exercise-4-making-the-code-available-and-presentable"]], "Exercise 5 - Comparing your code with sklearn": [[16, "exercise-5-comparing-your-code-with-sklearn"]], "Exercise 5, Ridge regression and a new Synthetic Dataset": [[18, "exercise-5-ridge-regression-and-a-new-synthetic-dataset"]], "Exercise 5: Analytical exercises": [[0, "exercise-5-analytical-exercises"]], "Exercise 5: Interpretation of scaling and metrics": [[19, "exercise-5-interpretation-of-scaling-and-metrics"]], "Exercise 5: Referencing": [[20, "exercise-5-referencing"]], "Exercise: Cross-validation as resampling techniques, adding more complexity": [[6, "exercise-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Exercise: Analysis of real data": [[6, "exercise-analysis-of-real-data"]], "Exercise: Bias-variance trade-off and resampling techniques": [[6, "exercise-bias-variance-trade-off-and-resampling-techniques"]], "Exercise: Lasso Regression on the Franke function with resampling": [[6, "exercise-lasso-regression-on-the-franke-function-with-resampling"]], "Exercise: Ordinary Least Square (OLS) on the Franke function": [[6, "exercise-ordinary-least-square-ols-on-the-franke-function"]], "Exercise: Ridge Regression on the Franke function with resampling": [[6, "exercise-ridge-regression-on-the-franke-function-with-resampling"]], "Exercises": [[0, "exercises"]], "Exercises and Projects": [[6, "exercises-and-projects"]], "Exercises week 34": [[15, null]], "Exercises week 35": [[16, null]], "Exercises week 36": [[17, null]], "Exercises week 37": [[18, null]], "Exercises week 38": [[19, null]], "Exercises week 39": [[20, null]], "Expectation value and variance": [[32, "expectation-value-and-variance"]], "Expectation value and variance for \\boldsymbol{\\theta}": [[32, "expectation-value-and-variance-for-boldsymbol-theta"]], "Expectation values": [[25, "expectation-values"]], "Extending to more predictors": [[33, "extending-to-more-predictors"], [34, "extending-to-more-predictors"]], "Extending to more than one variable": [[30, "extending-to-more-than-one-variable"]], "Extremely useful tools, strongly recommended": [[28, "extremely-useful-tools-strongly-recommended"]], "Feed-forward neural networks": [[12, "feed-forward-neural-networks"], [34, "feed-forward-neural-networks"]], "Feed-forward pass": [[1, "feed-forward-pass"]], "Final back propagating equation": [[12, "final-back-propagating-equation"]], "Finding the Limit": [[32, "finding-the-limit"]], "Fine-tuning neural network hyperparameters": [[1, "fine-tuning-neural-network-hyperparameters"]], "Fitting an Equation of State for Dense Nuclear Matter": [[0, "fitting-an-equation-of-state-for-dense-nuclear-matter"]], "Fixing the singularity": [[29, "fixing-the-singularity"], [30, "fixing-the-singularity"]], "Format for electronic delivery of report and programs": [[23, "format-for-electronic-delivery-of-report-and-programs"]], "Frequently used scaling functions": [[29, "frequently-used-scaling-functions"], [31, "frequently-used-scaling-functions"]], "From OLS to Ridge and Lasso": [[30, "from-ols-to-ridge-and-lasso"]], "From one to many layers, the universal approximation theorem": [[12, "from-one-to-many-layers-the-universal-approximation-theorem"]], "Functionality in Scikit-Learn": [[29, "functionality-in-scikit-learn"], [31, "functionality-in-scikit-learn"]], "Further Dimensionality Remarks": [[3, "further-dimensionality-remarks"]], "Further properties (important for our analyses later)": [[5, "further-properties-important-for-our-analyses-later"], [29, "further-properties-important-for-our-analyses-later"], [30, "further-properties-important-for-our-analyses-later"]], "Gaussian Elimination": [[22, "gaussian-elimination"]], "General Features": [[9, "general-features"]], "General linear models and linear algebra": [[28, "general-linear-models-and-linear-algebra"]], "Generalizing the fitting procedure as a linear algebra problem": [[28, "generalizing-the-fitting-procedure-as-a-linear-algebra-problem"], [28, "id1"]], "Generative Adversarial Networks": [[4, "generative-adversarial-networks"]], "Generative Models": [[4, "generative-models"]], "Generative Versus Discriminative Modeling": [[28, "generative-versus-discriminative-modeling"]], "Geometric Interpretation and link with Singular Value Decomposition": [[11, "geometric-interpretation-and-link-with-singular-value-decomposition"]], "Getting started with project 1": [[20, "getting-started-with-project-1"]], "Gradient Boosting, Classification Example": [[10, "gradient-boosting-classification-example"]], "Gradient Boosting, Examples of Regression": [[10, "gradient-boosting-examples-of-regression"]], "Gradient Clipping": [[1, "gradient-clipping"]], "Gradient Descent Example": [[30, "id1"], [31, "id1"]], "Gradient boosting: Basics with Steepest Descent/Functional Gradient Descent": [[10, "gradient-boosting-basics-with-steepest-descent-functional-gradient-descent"]], "Gradient descent": [[2, "gradient-descent"]], "Gradient descent and Ridge": [[30, "gradient-descent-and-ridge"], [31, "gradient-descent-and-ridge"]], "Gradient descent and revisiting Ordinary Least Squares from last week": [[31, "gradient-descent-and-revisiting-ordinary-least-squares-from-last-week"]], "Gradient descent example": [[30, "gradient-descent-example"], [31, "gradient-descent-example"]], "Grading": [[26, "grading"], [26, "id2"], [28, "grading"]], "How to take derivatives of Matrix-Vector expressions": [[16, "how-to-take-derivatives-of-matrix-vector-expressions"]], "Hyperplanes and all that": [[8, "hyperplanes-and-all-that"]], "Identifying Terms": [[32, "identifying-terms"]], "Illustration of a single perceptron model and a multi-perceptron model": [[34, "illustration-of-a-single-perceptron-model-and-a-multi-perceptron-model"]], "Important Matrix and vector handling packages": [[22, "important-matrix-and-vector-handling-packages"]], "Important technicalities: More on Rescaling data": [[29, "important-technicalities-more-on-rescaling-data"]], "Improving gradient descent with momentum": [[31, "improving-gradient-descent-with-momentum"]], "Improving performance": [[1, "improving-performance"]], "In summary": [[26, "in-summary"]], "Including Stochastic Gradient Descent with Autograd": [[13, "including-stochastic-gradient-descent-with-autograd"], [31, "including-stochastic-gradient-descent-with-autograd"]], "Including more classes": [[33, "including-more-classes"], [34, "including-more-classes"]], "Incremental PCA": [[11, "incremental-pca"]], "Independent and Identically Distributed (iid)": [[32, "independent-and-identically-distributed-iid"]], "Installing R, C++, cython or Julia": [[28, "installing-r-c-cython-or-julia"]], "Installing R, C++, cython, Numba etc": [[28, "installing-r-c-cython-numba-etc"]], "Instructor information": [[26, "instructor-information"]], "Interpretations and optimizing our parameters": [[28, "interpretations-and-optimizing-our-parameters"], [28, "id2"], [28, "id3"], [29, "interpretations-and-optimizing-our-parameters"], [29, "id1"], [29, "id2"]], "Interpreting the Ridge results": [[29, "interpreting-the-ridge-results"], [30, "interpreting-the-ridge-results"], [30, "id4"]], "Introducing JAX": [[13, "introducing-jax"]], "Introducing the Covariance and Correlation functions": [[11, "introducing-the-covariance-and-correlation-functions"], [29, "introducing-the-covariance-and-correlation-functions"]], "Introduction": [[0, "introduction"], [6, "introduction"], [21, "introduction"], [22, "introduction"]], "Introduction to Neural networks": [[34, "introduction-to-neural-networks"]], "Introduction to numerical projects": [[23, "introduction-to-numerical-projects"]], "Iterative Fitting, Classification and AdaBoost": [[10, "iterative-fitting-classification-and-adaboost"]], "Iterative Fitting, Regression and Squared-error Cost Function": [[10, "iterative-fitting-regression-and-squared-error-cost-function"]], "Kernel PCA": [[11, "kernel-pca"]], "Kernels and non-linearity": [[8, "kernels-and-non-linearity"]], "LU Decomposition, the inverse of a matrix": [[22, "lu-decomposition-the-inverse-of-a-matrix"]], "Lab sessions Tuesday and Wednesday": [[34, "lab-sessions-tuesday-and-wednesday"]], "Lab sessions week 39": [[33, "lab-sessions-week-39"]], "Lasso Regression": [[30, "lasso-regression"]], "Lasso case": [[30, "lasso-case"]], "Layers": [[1, "layers"]], "Layers used to build CNNs": [[3, "layers-used-to-build-cnns"]], "Learning goals": [[15, "learning-goals"], [16, "learning-goals"], [17, "learning-goals"], [18, "learning-goals"], [19, "learning-goals"], [20, "learning-goals"]], "Learning outcomes": [[21, "learning-outcomes"], [28, "learning-outcomes"]], "Lecture Monday September 29, 2025": [[34, "lecture-monday-september-29-2025"]], "Lecture material": [[33, "lecture-material"]], "Lectures and ComputerLab": [[28, "lectures-and-computerlab"]], "Limitations of supervised learning with deep networks": [[1, "limitations-of-supervised-learning-with-deep-networks"]], "Linear Algebra, Handling of Arrays and more Python Features": [[22, null]], "Linear Regression": [[0, null]], "Linear Regression Problems": [[29, "linear-regression-problems"], [30, "linear-regression-problems"]], "Linear Regression and the SVD": [[30, "linear-regression-and-the-svd"]], "Linear Regression, basic elements": [[0, "linear-regression-basic-elements"]], "Linear classifier": [[33, "linear-classifier"]], "Linking Bayes\u2019 Theorem with Ridge and Lasso Regression": [[5, "linking-bayes-theorem-with-ridge-and-lasso-regression"]], "Linking the regression analysis with a statistical interpretation": [[5, "linking-the-regression-analysis-with-a-statistical-interpretation"], [32, "linking-the-regression-analysis-with-a-statistical-interpretation"]], "Linking with the SVD": [[5, "linking-with-the-svd"], [29, "linking-with-the-svd"]], "Links to relevant courses at the University of Oslo": [[27, "links-to-relevant-courses-at-the-university-of-oslo"]], "Logistic Regression": [[7, null], [7, "id1"], [33, "logistic-regression"]], "Logistic Regression, from last week": [[34, "logistic-regression-from-last-week"]], "MNIST and GANs": [[4, "mnist-and-gans"]], "Machine Learning": [[28, "machine-learning"]], "Machine learning": [[21, "machine-learning"]], "Main textbooks": [[28, "main-textbooks"]], "Making a tree": [[9, "making-a-tree"]], "Making your own Bootstrap: Changing the Level of the Decision Tree": [[10, "making-your-own-bootstrap-changing-the-level-of-the-decision-tree"]], "Making your own test-train splitting": [[29, "making-your-own-test-train-splitting"]], "Material for exercises week 35": [[29, "material-for-exercises-week-35"]], "Material for lab sessions sessions Tuesday and Wednesday": [[30, "material-for-lab-sessions-sessions-tuesday-and-wednesday"]], "Material for lecture Monday September 2": [[30, "material-for-lecture-monday-september-2"]], "Material for lecture Monday September 8": [[31, "material-for-lecture-monday-september-8"]], "Material for the lab sessions": [[31, "material-for-the-lab-sessions"], [32, "material-for-the-lab-sessions"]], "Mathematical Interpretation of Ordinary Least Squares": [[5, "mathematical-interpretation-of-ordinary-least-squares"], [29, "mathematical-interpretation-of-ordinary-least-squares"], [30, "mathematical-interpretation-of-ordinary-least-squares"]], "Mathematical model": [[34, "mathematical-model"], [34, "id1"], [34, "id2"], [34, "id3"], [34, "id4"]], "Mathematical optimization of convex functions": [[8, "mathematical-optimization-of-convex-functions"]], "Mathematics of CNNs": [[3, "mathematics-of-cnns"]], "Mathematics of the SVD and implications": [[5, "mathematics-of-the-svd-and-implications"], [29, "mathematics-of-the-svd-and-implications"], [30, "mathematics-of-the-svd-and-implications"]], "Matrices in Python": [[28, "matrices-in-python"]], "Matrix multiplication": [[1, "matrix-multiplication"]], "Matrix-vector notation": [[34, "matrix-vector-notation"]], "Matrix-vector notation and activation": [[12, "matrix-vector-notation-and-activation"], [34, "matrix-vector-notation-and-activation"]], "Maximum Likelihood Estimation (MLE)": [[32, "maximum-likelihood-estimation-mle"]], "Maximum likelihood": [[33, "maximum-likelihood"], [34, "maximum-likelihood"]], "Meet the covariance!": [[25, "meet-the-covariance"]], "Meet the Covariance Matrix": [[5, "meet-the-covariance-matrix"], [29, "meet-the-covariance-matrix"]], "Meet the Hessian Matrix": [[29, "meet-the-hessian-matrix"]], "Meet the Pandas": [[28, "meet-the-pandas"]], "Memory Usage and Scalability": [[31, "memory-usage-and-scalability"]], "Memory constraints": [[31, "memory-constraints"]], "Min-Max Scaling": [[29, "min-max-scaling"]], "Minimizing the cross entropy": [[33, "minimizing-the-cross-entropy"], [34, "minimizing-the-cross-entropy"]], "Momentum based GD": [[13, "momentum-based-gd"], [31, "momentum-based-gd"]], "More classes": [[33, "more-classes"], [34, "more-classes"]], "More complicated Example: The Ising model": [[6, "more-complicated-example-the-ising-model"]], "More examples on bootstrap and cross-validation and errors": [[32, "more-examples-on-bootstrap-and-cross-validation-and-errors"], [33, "more-examples-on-bootstrap-and-cross-validation-and-errors"]], "More interpretations": [[29, "more-interpretations"], [30, "more-interpretations"], [30, "id5"]], "More on Dimensionalities": [[3, "more-on-dimensionalities"]], "More on Rescaling data": [[6, "more-on-rescaling-data"]], "More on Steepest descent": [[30, "more-on-steepest-descent"]], "More on convex functions": [[30, "more-on-convex-functions"]], "More preprocessing": [[29, "more-preprocessing"], [31, "more-preprocessing"]], "Motivation for Adaptive Step Sizes": [[31, "motivation-for-adaptive-step-sizes"]], "Multilayer perceptrons": [[12, "multilayer-perceptrons"], [34, "multilayer-perceptrons"]], "Network requirements": [[2, "network-requirements"]], "Neural Networks vs CNNs": [[3, "neural-networks-vs-cnns"]], "Neural network types": [[34, "neural-network-types"]], "Neural networks": [[12, null]], "Non-Convex Problems": [[31, "non-convex-problems"]], "Note about SVD Calculations": [[29, "note-about-svd-calculations"], [30, "note-about-svd-calculations"]], "Note on Scikit-Learn": [[30, "note-on-scikit-learn"]], "Numerical experiments and the covariance, central limit theorem": [[25, "numerical-experiments-and-the-covariance-central-limit-theorem"]], "Numpy and arrays": [[22, "numpy-and-arrays"], [28, "numpy-and-arrays"]], "Numpy examples and Important Matrix and vector handling packages": [[28, "numpy-examples-and-important-matrix-and-vector-handling-packages"]], "Optimization and Deep learning": [[33, "optimization-and-deep-learning"], [34, "optimization-and-deep-learning"]], "Optimization and gradient descent, the central part of any Machine Learning algortithm": [[30, "optimization-and-gradient-descent-the-central-part-of-any-machine-learning-algortithm"]], "Optimization, the central part of any Machine Learning algortithm": [[13, null], [33, "optimization-the-central-part-of-any-machine-learning-algortithm"], [34, "optimization-the-central-part-of-any-machine-learning-algortithm"]], "Optimizing our parameters": [[28, "optimizing-our-parameters"]], "Optimizing our parameters, more details": [[28, "optimizing-our-parameters-more-details"]], "Optimizing the cost function": [[1, "optimizing-the-cost-function"]], "Organizing our data": [[0, "organizing-our-data"], [28, "organizing-our-data"]], "Other Matrix and Vector Operations": [[22, "other-matrix-and-vector-operations"]], "Other Types of Recurrent Neural Networks": [[4, "other-types-of-recurrent-neural-networks"]], "Other courses on Data science and Machine Learning at UiO": [[28, "other-courses-on-data-science-and-machine-learning-at-uio"]], "Other courses on Data science and Machine Learning at UiO, contn": [[28, "other-courses-on-data-science-and-machine-learning-at-uio-contn"]], "Other measures in classification studies": [[34, "other-measures-in-classification-studies"]], "Other popular texts": [[28, "other-popular-texts"]], "Other techniques": [[11, "other-techniques"]], "Other types of networks": [[12, "other-types-of-networks"], [34, "other-types-of-networks"]], "Other ways of visualizing the trees": [[9, "other-ways-of-visualizing-the-trees"]], "Our model for the nuclear binding energies": [[28, "our-model-for-the-nuclear-binding-energies"]], "Overview of first week": [[28, "overview-of-first-week"]], "Overview video on Stochastic Gradient Descent (SGD)": [[31, "overview-video-on-stochastic-gradient-descent-sgd"]], "Own code for Ordinary Least Squares": [[28, "own-code-for-ordinary-least-squares"], [29, "own-code-for-ordinary-least-squares"]], "PCA and scikit-learn": [[11, "pca-and-scikit-learn"]], "Pandas AI": [[28, "pandas-ai"]], "Part a : Ordinary Least Square (OLS) for the Runge function": [[23, "part-a-ordinary-least-square-ols-for-the-runge-function"]], "Part b: Adding Ridge regression for the Runge function": [[23, "part-b-adding-ridge-regression-for-the-runge-function"]], "Part c: Writing your own gradient descent code": [[23, "part-c-writing-your-own-gradient-descent-code"]], "Part d: Including momentum and more advanced ways to update the learning the rate": [[23, "part-d-including-momentum-and-more-advanced-ways-to-update-the-learning-the-rate"]], "Part e: Writing our own code for Lasso regression": [[23, "part-e-writing-our-own-code-for-lasso-regression"]], "Part f: Stochastic gradient descent": [[23, "part-f-stochastic-gradient-descent"]], "Part g: Bias-variance trade-off and resampling techniques": [[23, "part-g-bias-variance-trade-off-and-resampling-techniques"]], "Part h): Cross-validation as resampling techniques, adding more complexity": [[23, "part-h-cross-validation-as-resampling-techniques-adding-more-complexity"]], "Partial Differential Equations": [[2, "partial-differential-equations"]], "Plan for week 39, September 22-26, 2025": [[33, "plan-for-week-39-september-22-26-2025"]], "Plans for week 35": [[29, "plans-for-week-35"]], "Plans for week 36": [[30, "plans-for-week-36"]], "Plans for week 37, lecture Monday": [[31, "plans-for-week-37-lecture-monday"]], "Plans for week 38, lecture Monday September 15": [[32, "plans-for-week-38-lecture-monday-september-15"]], "Plotting the Histogram": [[32, "plotting-the-histogram"]], "Plotting the mean value for each group": [[33, "plotting-the-mean-value-for-each-group"]], "Practical tips": [[13, "practical-tips"], [31, "practical-tips"]], "Practicalities": [[26, "practicalities"], [26, "id1"]], "Preamble: Note on writing reports, using reference material, AI and other tools": [[23, "preamble-note-on-writing-reports-using-reference-material-ai-and-other-tools"]], "Predicting New Points With A Trained Recurrent Neural Network": [[4, "predicting-new-points-with-a-trained-recurrent-neural-network"]], "Preprocessing our data": [[29, "preprocessing-our-data"]], "Prerequisites": [[28, "prerequisites"]], "Prerequisites and background": [[21, "prerequisites-and-background"]], "Prerequisites: Collect and pre-process data": [[3, "prerequisites-collect-and-pre-process-data"]], "Probability Distribution Functions": [[25, "probability-distribution-functions"]], "Program example for gradient descent with Ridge Regression": [[30, "program-example-for-gradient-descent-with-ridge-regression"], [31, "program-example-for-gradient-descent-with-ridge-regression"]], "Program for stochastic gradient": [[13, "program-for-stochastic-gradient"]], "Project 1 on Machine Learning, deadline October 6 (midnight), 2025": [[23, null]], "Properties of PDFs": [[25, "properties-of-pdfs"]], "Pros and cons": [[31, "pros-and-cons"]], "Pros and cons of trees, pros": [[9, "pros-and-cons-of-trees-pros"]], "Python installers": [[21, "python-installers"], [28, "python-installers"]], "RMS prop": [[13, "rms-prop"]], "RMSProp algorithm, taken from Goodfellow et al": [[31, "rmsprop-algorithm-taken-from-goodfellow-et-al"]], "RMSProp: Adaptive Learning Rates": [[31, "rmsprop-adaptive-learning-rates"]], "RMSprop for adaptive learning rate with Stochastic Gradient Descent": [[31, "rmsprop-for-adaptive-learning-rate-with-stochastic-gradient-descent"]], "Random Numbers": [[25, "random-numbers"]], "Random forests": [[10, "random-forests"]], "Randomized PCA": [[11, "randomized-pca"]], "Reading material": [[28, "reading-material"]], "Reading recommendations:": [[29, "reading-recommendations"]], "Reading suggestions week 34": [[28, "reading-suggestions-week-34"]], "Readings and Videos": [[32, "readings-and-videos"]], "Readings and Videos, logistic regression": [[33, "readings-and-videos-logistic-regression"]], "Readings and Videos, resampling methods": [[33, "readings-and-videos-resampling-methods"]], "Readings and Videos:": [[31, "readings-and-videos"]], "Recurrent neural networks": [[12, "recurrent-neural-networks"], [34, "recurrent-neural-networks"]], "Recurrent neural networks: Overarching view": [[4, null]], "Reducing the number of degrees of freedom, overarching view": [[0, "reducing-the-number-of-degrees-of-freedom-overarching-view"], [29, "reducing-the-number-of-degrees-of-freedom-overarching-view"]], "Reformulating the problem": [[2, "reformulating-the-problem"]], "Regression Case": [[10, "regression-case"]], "Regression analysis and resampling methods": [[23, "regression-analysis-and-resampling-methods"]], "Regression analysis, overarching aims": [[28, "regression-analysis-overarching-aims"]], "Regression analysis, overarching aims II": [[28, "regression-analysis-overarching-aims-ii"]], "Regularization": [[1, "regularization"]], "Relevance": [[34, "relevance"]], "Reminder from last week": [[29, "reminder-from-last-week"]], "Reminder on Newton-Raphson\u2019s method": [[30, "reminder-on-newton-raphson-s-method"]], "Reminder on Statistics": [[6, "reminder-on-statistics"]], "Reminder on different scaling methods": [[31, "reminder-on-different-scaling-methods"]], "Replace or not": [[13, "replace-or-not"], [31, "replace-or-not"]], "Required Technologies": [[21, "required-technologies"]], "Resampling Methods": [[6, null]], "Resampling and the Bias-Variance Trade-off": [[19, "resampling-and-the-bias-variance-trade-off"]], "Resampling approaches can be computationally expensive": [[32, "resampling-approaches-can-be-computationally-expensive"], [33, "resampling-approaches-can-be-computationally-expensive"]], "Resampling methods": [[6, "id1"], [32, "resampling-methods"], [32, "id2"], [33, "resampling-methods"], [33, "id1"]], "Resampling methods: Bootstrap": [[32, "resampling-methods-bootstrap"], [33, "resampling-methods-bootstrap"]], "Resampling methods: Bootstrap approach": [[32, "resampling-methods-bootstrap-approach"]], "Resampling methods: Bootstrap background": [[32, "resampling-methods-bootstrap-background"]], "Resampling methods: Bootstrap steps": [[32, "resampling-methods-bootstrap-steps"]], "Resampling methods: More Bootstrap background": [[32, "resampling-methods-more-bootstrap-background"]], "Residual Error": [[29, "residual-error"], [30, "residual-error"]], "Resources on differential equations and deep learning": [[2, "resources-on-differential-equations-and-deep-learning"]], "Revisiting Ordinary Least Squares": [[30, "revisiting-ordinary-least-squares"]], "Revisiting our Linear Regression Solvers": [[13, "revisiting-our-linear-regression-solvers"]], "Revisiting our Logistic Regression case": [[33, "revisiting-our-logistic-regression-case"], [34, "revisiting-our-logistic-regression-case"]], "Rewriting the Covariance and/or Correlation Matrix": [[29, "rewriting-the-covariance-and-or-correlation-matrix"]], "Rewriting the \\delta-function": [[32, "rewriting-the-delta-function"]], "Rewriting the fitting procedure as a linear algebra problem": [[28, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem"]], "Rewriting the fitting procedure as a linear algebra problem, more details": [[28, "rewriting-the-fitting-procedure-as-a-linear-algebra-problem-more-details"]], "Ridge Regression": [[30, "ridge-regression"]], "Ridge and LASSO Regression": [[29, "ridge-and-lasso-regression"], [30, "ridge-and-lasso-regression"], [30, "id2"]], "Ridge and Lasso Regression": [[5, null], [5, "id1"]], "SGD example": [[31, "sgd-example"]], "SGD vs Full-Batch GD: Convergence Speed and Memory Comparison": [[31, "sgd-vs-full-batch-gd-convergence-speed-and-memory-comparison"]], "SVD analysis": [[30, "svd-analysis"]], "Same code but now with momentum gradient descent": [[13, "same-code-but-now-with-momentum-gradient-descent"], [31, "same-code-but-now-with-momentum-gradient-descent"], [31, "id3"], [31, "id4"]], "Schedule first week": [[28, "schedule-first-week"]], "Schematic Regression Procedure": [[9, "schematic-regression-procedure"]], "Second moment of the gradient": [[31, "second-moment-of-the-gradient"]], "September 15-19": [[19, "september-15-19"]], "Setting up the Back propagation algorithm": [[12, "setting-up-the-back-propagation-algorithm"]], "Setting up the Matrix to be inverted": [[29, "setting-up-the-matrix-to-be-inverted"], [30, "setting-up-the-matrix-to-be-inverted"]], "Setting up the network using Autograd; The full program": [[2, "setting-up-the-network-using-autograd-the-full-program"]], "Similar (second order function now) problem but now with AdaGrad": [[13, "similar-second-order-function-now-problem-but-now-with-adagrad"], [31, "similar-second-order-function-now-problem-but-now-with-adagrad"]], "Simple Python Code to read in Data and perform Classification": [[9, "simple-python-code-to-read-in-data-and-perform-classification"]], "Simple case": [[29, "simple-case"], [30, "simple-case"]], "Simple code for solving the above problem": [[30, "simple-code-for-solving-the-above-problem"]], "Simple example": [[33, "simple-example"]], "Simple example code": [[31, "simple-example-code"]], "Simple example to illustrate Ordinary Least Squares, Ridge and Lasso Regression": [[30, "simple-example-to-illustrate-ordinary-least-squares-ridge-and-lasso-regression"]], "Simple geometric interpretation": [[30, "simple-geometric-interpretation"]], "Simple linear regression model using scikit-learn": [[0, "simple-linear-regression-model-using-scikit-learn"], [28, "simple-linear-regression-model-using-scikit-learn"]], "Simple one-dimensional second-order polynomial": [[18, "simple-one-dimensional-second-order-polynomial"]], "Simple program": [[30, "simple-program"], [31, "simple-program"]], "Slightly different approach": [[31, "slightly-different-approach"]], "Sneaking in automatic differentiation using Autograd": [[31, "sneaking-in-automatic-differentiation-using-autograd"]], "Software and needed installations": [[23, "software-and-needed-installations"], [28, "software-and-needed-installations"]], "Solving Differential Equations with Deep Learning": [[2, null]], "Solving the one dimensional Poisson equation": [[2, "solving-the-one-dimensional-poisson-equation"]], "Solving the wave equation with Neural Networks": [[2, "solving-the-wave-equation-with-neural-networks"]], "Solving using Newton-Raphson\u2019s method": [[33, "solving-using-newton-raphson-s-method"], [34, "solving-using-newton-raphson-s-method"]], "Some famous Matrices": [[22, "some-famous-matrices"]], "Some selected properties": [[33, "some-selected-properties"]], "Some simple problems": [[13, "some-simple-problems"], [30, "some-simple-problems"]], "Some useful matrix and vector expressions": [[29, "some-useful-matrix-and-vector-expressions"]], "Splitting our Data in Training and Test data": [[0, "splitting-our-data-in-training-and-test-data"], [29, "splitting-our-data-in-training-and-test-data"]], "Standard Approach based on the Normal Distribution": [[32, "standard-approach-based-on-the-normal-distribution"]], "Standard steepest descent": [[13, "standard-steepest-descent"]], "Statistical analysis": [[32, "statistical-analysis"], [33, "statistical-analysis"]], "Statistical analysis and optimization of data": [[21, "statistical-analysis-and-optimization-of-data"], [28, "statistical-analysis-and-optimization-of-data"]], "Steepest descent": [[13, "steepest-descent"], [30, "steepest-descent"]], "Stochastic Gradient Descent": [[31, "stochastic-gradient-descent"]], "Stochastic Gradient Descent (SGD)": [[13, "stochastic-gradient-descent-sgd"], [31, "stochastic-gradient-descent-sgd"]], "Stochastic variables and the main concepts, the discrete case": [[25, "stochastic-variables-and-the-main-concepts-the-discrete-case"]], "Strongly Convex Case": [[31, "strongly-convex-case"]], "Suggested readings and videos": [[34, "suggested-readings-and-videos"]], "Summing up": [[32, "summing-up"], [33, "summing-up"]], "Support Vector Machines, overarching aims": [[8, null]], "Synthetic data generation": [[33, "synthetic-data-generation"], [34, "synthetic-data-generation"]], "Systematic reduction": [[3, "systematic-reduction"]], "Teachers": [[28, "teachers"]], "Teachers and Grading": [[26, null]], "Teaching Assistants Fall semester 2023": [[26, "teaching-assistants-fall-semester-2023"]], "Tentative deadllines for projects": [[26, "tentative-deadllines-for-projects"]], "Testing the Means Squared Error as function of Complexity": [[0, "testing-the-means-squared-error-as-function-of-complexity"], [29, "testing-the-means-squared-error-as-function-of-complexity"]], "Textbooks": [[27, null]], "The Algorithm before theorem": [[11, "the-algorithm-before-theorem"]], "The Breast Cancer Data, now with Keras": [[1, "the-breast-cancer-data-now-with-keras"]], "The CART algorithm for Classification": [[9, "the-cart-algorithm-for-classification"]], "The CART algorithm for Regression": [[9, "the-cart-algorithm-for-regression"]], "The CIFAR01 data set": [[3, "the-cifar01-data-set"]], "The Central Limit Theorem": [[32, "the-central-limit-theorem"]], "The Hessian matrix": [[30, "the-hessian-matrix"], [31, "the-hessian-matrix"]], "The Hessian matrix for Ridge Regression": [[30, "the-hessian-matrix-for-ridge-regression"], [31, "the-hessian-matrix-for-ridge-regression"]], "The Jacobian": [[29, "the-jacobian"]], "The MNIST dataset again": [[3, "the-mnist-dataset-again"]], "The OLS case": [[30, "the-ols-case"]], "The RELU function family": [[1, "the-relu-function-family"]], "The Ridge case": [[30, "the-ridge-case"]], "The SVD, a Fantastic Algorithm": [[29, "the-svd-a-fantastic-algorithm"], [30, "the-svd-a-fantastic-algorithm"]], "The Softmax function": [[1, "the-softmax-function"]], "The \\chi^2 function": [[0, "the-chi-2-function"], [28, "the-chi-2-function"], [28, "id4"], [28, "id5"], [28, "id6"], [28, "id7"], [28, "id8"]], "The bias-variance tradeoff": [[6, "the-bias-variance-tradeoff"], [32, "the-bias-variance-tradeoff"], [33, "the-bias-variance-tradeoff"]], "The code for solving the ODE": [[2, "the-code-for-solving-the-ode"]], "The complete code with a simple data set": [[29, "the-complete-code-with-a-simple-data-set"]], "The cost function rewritten": [[33, "the-cost-function-rewritten"], [34, "the-cost-function-rewritten"]], "The cost/loss function": [[29, "the-cost-loss-function"]], "The course has two central parts": [[21, "the-course-has-two-central-parts"]], "The derivative of the cost/loss function": [[30, "the-derivative-of-the-cost-loss-function"], [31, "the-derivative-of-the-cost-loss-function"]], "The equations": [[30, "the-equations"]], "The equations for ordinary least squares": [[29, 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[0, 28, 29, 30, 32, 33], "when": 31, "which": [1, 31], "why": [28, 31, 32, 33, 34], "wisconsin": 7, "workflow": [], "wrap": 32, "write": [4, 11, 20, 23, 30], "x": 29, "xgboost": 10, "xor": 34, "yaml": [], "yet": 30, "your": [0, 10, 16, 18, 23, 29]}}) \ No newline at end of file diff --git a/doc/LectureNotes/_build/html/week40.html b/doc/LectureNotes/_build/html/week40.html index 2a3566603..0a0bc2d29 100644 --- a/doc/LectureNotes/_build/html/week40.html +++ b/doc/LectureNotes/_build/html/week40.html @@ -465,9 +465,10 @@ doconce format html week40.do.txt --no_mako -->

  1. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model

    2. +

  2. Video of lecture at https://youtu.be/MS3Tv8FVArs

    3. +

  3. Whiteboard notes at CompPhysics/MachineLearning

- - +

Suggested readings and videos#

Readings and Videos:

diff --git a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb index 351978e55..5475b7668 100644 --- a/doc/LectureNotes/_build/jupyter_execute/week40.ipynb +++ b/doc/LectureNotes/_build/jupyter_execute/week40.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "a8c8eb55", + "id": "2303c986", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "fb2c3381", + "id": "75c3b33e", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "a2dfe61e", + "id": "4ba50982", "metadata": { "editable": true }, @@ -37,13 +37,15 @@ "\n", "\n", "2. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model\n", - "\n", - "" + "\n", + "3. Video of lecture at \n", + "\n", + "4. Whiteboard notes at " ] }, { "cell_type": "markdown", - "id": "e342e71b", + "id": "1d527020", "metadata": { "editable": true }, @@ -65,7 +67,7 @@ }, { "cell_type": "markdown", - "id": "97a574c5", + "id": "63a4d497", "metadata": { "editable": true }, @@ -84,7 +86,7 @@ }, { "cell_type": "markdown", - "id": "22f40b12", + "id": "73621d6b", "metadata": { "editable": true }, @@ -106,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "729f5aba", + "id": "fc1df17b", "metadata": { "editable": true }, @@ -132,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "e2782fb0", + "id": "a3d311e6", "metadata": { "editable": true }, @@ -158,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "7968ba20", + "id": "4120d6f9", "metadata": { "editable": true }, @@ -183,7 +185,7 @@ }, { "cell_type": "markdown", - "id": "8b89d296", + "id": "9e85d1e4", "metadata": { "editable": true }, @@ -195,7 +197,7 @@ }, { "cell_type": "markdown", - "id": "4380d27a", + "id": "a0d8c838", "metadata": { "editable": true }, @@ -207,7 +209,7 @@ }, { "cell_type": "markdown", - "id": "930a5781", + "id": "7cea7945", "metadata": { "editable": true }, @@ -222,7 +224,7 @@ }, { "cell_type": "markdown", - "id": "5c7f6446", + "id": "6adc5106", "metadata": { "editable": true }, @@ -234,7 +236,7 @@ }, { "cell_type": "markdown", - "id": "964ad807", + "id": "f976068e", "metadata": { "editable": true }, @@ -246,7 +248,7 @@ }, { "cell_type": "markdown", - "id": "3214fcef", + "id": "dedf9f0e", "metadata": { "editable": true }, @@ -263,7 +265,7 @@ }, { "cell_type": "markdown", - "id": "db0ae7e7", + "id": "bd8b54ab", "metadata": { "editable": true }, @@ -277,7 +279,7 @@ }, { "cell_type": "markdown", - "id": "81b0f404", + "id": "57bfb17f", "metadata": { "editable": true }, @@ -287,7 +289,7 @@ }, { "cell_type": "markdown", - "id": "a1b9b4b7", + "id": "00aee268", "metadata": { "editable": true }, @@ -299,7 +301,7 @@ }, { "cell_type": "markdown", - "id": "9ee3938f", + "id": "e12940f3", "metadata": { "editable": true }, @@ -311,7 +313,7 @@ }, { "cell_type": "markdown", - "id": "3a4c630e", + "id": "e5b2b29e", "metadata": { "editable": true }, @@ -323,7 +325,7 @@ }, { "cell_type": "markdown", - "id": "38250af6", + "id": "c6c0ba4c", "metadata": { "editable": true }, @@ -334,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "122e8f3d", + "id": "46ee2ea8", "metadata": { "editable": true }, @@ -346,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "b41b63c5", + "id": "9a05709b", "metadata": { "editable": true }, @@ -357,7 +359,7 @@ }, { "cell_type": "markdown", - "id": "80534aba", + "id": "ae1362c9", "metadata": { "editable": true }, @@ -373,7 +375,7 @@ }, { "cell_type": "markdown", - "id": "eb1b9888", + "id": "57f4670b", "metadata": { "editable": true }, @@ -385,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "ab198853", + "id": "1dc19f59", "metadata": { "editable": true }, @@ -395,7 +397,7 @@ }, { "cell_type": "markdown", - "id": "f5b74ed9", + "id": "4e96dc87", "metadata": { "editable": true }, @@ -407,7 +409,7 @@ }, { "cell_type": "markdown", - "id": "7c684bba", + "id": "fa77bec9", "metadata": { "editable": true }, @@ -422,7 +424,7 @@ }, { "cell_type": "markdown", - "id": "7c8b3019", + "id": "1b013fd2", "metadata": { "editable": true }, @@ -434,7 +436,7 @@ }, { "cell_type": "markdown", - "id": "8e203747", + "id": "910f36dd", "metadata": { "editable": true }, @@ -445,7 +447,7 @@ }, { "cell_type": "markdown", - "id": "fbf05462", + "id": "8212d0ed", "metadata": { "editable": true }, @@ -457,7 +459,7 @@ }, { "cell_type": "markdown", - "id": "00138c6f", + "id": "7ae7078b", "metadata": { "editable": true }, @@ -469,7 +471,7 @@ }, { "cell_type": "markdown", - "id": "33e45216", + "id": "59e57d7c", "metadata": { "editable": true }, @@ -481,7 +483,7 @@ }, { "cell_type": "markdown", - "id": "6e353bf4", + "id": "6ffe0955", "metadata": { "editable": true }, @@ -491,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "004a41f8", + "id": "56e9bd82", "metadata": { "editable": true }, @@ -503,7 +505,7 @@ }, { "cell_type": "markdown", - "id": "a232ef44", + "id": "86b12946", "metadata": { "editable": true }, @@ -517,7 +519,7 @@ }, { "cell_type": "markdown", - "id": "d090847e", + "id": "d55394df", "metadata": { "editable": true }, @@ -529,7 +531,7 @@ }, { "cell_type": "markdown", - "id": "46a9546e", + "id": "ee01378a", "metadata": { "editable": true }, @@ -539,7 +541,7 @@ }, { "cell_type": "markdown", - "id": "f85b4c49", + "id": "c7fadfbb", "metadata": { "editable": true }, @@ -551,7 +553,7 @@ }, { "cell_type": "markdown", - "id": "4ba76229", + "id": "e8310f63", "metadata": { "editable": true }, @@ -561,7 +563,7 @@ }, { "cell_type": "markdown", - "id": "9f40376d", + "id": "be651647", "metadata": { "editable": true }, @@ -573,7 +575,7 @@ }, { "cell_type": "markdown", - "id": "e344416d", + "id": "e277c601", "metadata": { "editable": true }, @@ -584,7 +586,7 @@ }, { "cell_type": "markdown", - "id": "f9107f2a", + "id": "aea3a410", "metadata": { "editable": true }, @@ -607,7 +609,7 @@ }, { "cell_type": "markdown", - "id": "36ce9b10", + "id": "bfa7221f", "metadata": { "editable": true }, @@ -619,7 +621,7 @@ }, { "cell_type": "markdown", - "id": "2cd2dfd0", + "id": "3d749c39", "metadata": { "editable": true }, @@ -629,7 +631,7 @@ }, { "cell_type": "markdown", - "id": "ce302774", + "id": "dc061a39", "metadata": { "editable": true }, @@ -641,7 +643,7 @@ }, { "cell_type": "markdown", - "id": "d443be10", + "id": "8ea10488", "metadata": { "editable": true }, @@ -658,7 +660,7 @@ }, { "cell_type": "markdown", - "id": "6755463b", + "id": "9cb3baf8", "metadata": { "editable": true }, @@ -677,7 +679,7 @@ }, { "cell_type": "markdown", - "id": "84f1d6cb", + "id": "387393d7", "metadata": { "editable": true }, @@ -694,7 +696,7 @@ }, { "cell_type": "markdown", - "id": "d1d136eb", + "id": "30f64659", "metadata": { "editable": true }, @@ -709,7 +711,7 @@ }, { "cell_type": "markdown", - "id": "34bda880", + "id": "3ba65422", "metadata": { "editable": true }, @@ -719,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "853aa76a", + "id": "005f46d7", "metadata": { "editable": true }, @@ -735,7 +737,7 @@ }, { "cell_type": "markdown", - "id": "b3fb4491", + "id": "61a638bc", "metadata": { "editable": true }, @@ -747,7 +749,7 @@ }, { "cell_type": "markdown", - "id": "453c22a3", + "id": "469c0042", "metadata": { "editable": true }, @@ -758,7 +760,7 @@ }, { "cell_type": "markdown", - "id": "611c9632", + "id": "0af5449a", "metadata": { "editable": true }, @@ -770,7 +772,7 @@ }, { "cell_type": "markdown", - "id": "c45aca41", + "id": "f4c16b4f", "metadata": { "editable": true }, @@ -780,7 +782,7 @@ }, { "cell_type": "markdown", - "id": "35751022", + "id": "ddbe7f50", "metadata": { "editable": true }, @@ -794,7 +796,7 @@ }, { "cell_type": "markdown", - "id": "781c6916", + "id": "52830f96", "metadata": { "editable": true }, @@ -806,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "446ec153", + "id": "1b8a1c14", "metadata": { "editable": true }, @@ -816,7 +818,7 @@ }, { "cell_type": "markdown", - "id": "fa87caf0", + "id": "8ad73cea", "metadata": { "editable": true }, @@ -828,7 +830,7 @@ }, { "cell_type": "markdown", - "id": "6a5dfd86", + "id": "6d47dd0b", "metadata": { "editable": true }, @@ -840,7 +842,7 @@ }, { "cell_type": "markdown", - "id": "a0e56fe0", + "id": "f399c2f4", "metadata": { "editable": true }, @@ -853,7 +855,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "ea0068e6", + "id": "79f6b6fc", "metadata": { "collapsed": false, "editable": true @@ -996,7 +998,7 @@ }, { "cell_type": "markdown", - "id": "9fe762a6", + "id": "24e84b29", "metadata": { "editable": true }, @@ -1015,7 +1017,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "b6f32217", + "id": "7a73eca4", "metadata": { "collapsed": false, "editable": true @@ -1058,7 +1060,7 @@ }, { "cell_type": "markdown", - "id": "9f278530", + "id": "40d4b30f", "metadata": { "editable": true }, @@ -1072,7 +1074,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "88e13c71", + "id": "ac0089bf", "metadata": { "collapsed": false, "editable": true @@ -1161,7 +1163,7 @@ }, { "cell_type": "markdown", - "id": "938925ff", + "id": "1e9acef3", "metadata": { "editable": true }, @@ -1178,7 +1180,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "c01e1379", + "id": "9153234a", "metadata": { "collapsed": false, "editable": true @@ -1207,7 +1209,7 @@ }, { "cell_type": "markdown", - "id": "a2b8d6a4", + "id": "908d547b", "metadata": { "editable": true }, @@ -1221,7 +1223,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "6ec30a76", + "id": "8a46f4f3", "metadata": { "collapsed": false, "editable": true @@ -1266,7 +1268,7 @@ }, { "cell_type": "markdown", - "id": "1c00160e", + "id": "ba0275a7", "metadata": { "editable": true }, @@ -1291,7 +1293,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "1468b093", + "id": "1af34f8e", "metadata": { "collapsed": false, "editable": true @@ -1303,7 +1305,7 @@ }, { "cell_type": "markdown", - "id": "509b083d", + "id": "1eac30d3", "metadata": { "editable": true }, @@ -1314,7 +1316,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "c58d0fa7", + "id": "a0cdd9c9", "metadata": { "collapsed": false, "editable": true @@ -1326,7 +1328,7 @@ }, { "cell_type": "markdown", - "id": "a0445ed4", + "id": "013777ad", "metadata": { "editable": true }, @@ -1339,7 +1341,7 @@ }, { "cell_type": "markdown", - "id": "93e60a48", + "id": "410f90ac", "metadata": { "editable": true }, @@ -1350,7 +1352,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "c0c0be2f", + "id": "fa16a459", "metadata": { "collapsed": false, "editable": true @@ -1393,7 +1395,7 @@ }, { "cell_type": "markdown", - "id": "c4e95738", + "id": "a721de53", "metadata": { "editable": true }, @@ -1411,7 +1413,7 @@ }, { "cell_type": "markdown", - "id": "455b9abe", + "id": "68de5052", "metadata": { "editable": true }, @@ -1435,7 +1437,7 @@ }, { "cell_type": "markdown", - "id": "fafe38d0", + "id": "7685af02", "metadata": { "editable": true }, @@ -1453,7 +1455,7 @@ }, { "cell_type": "markdown", - "id": "a2b62ddd", + "id": "3dfcfcb0", "metadata": { "editable": true }, @@ -1493,7 +1495,7 @@ }, { "cell_type": "markdown", - "id": "86b9d89a", + "id": "0d037ca7", "metadata": { "editable": true }, @@ -1522,7 +1524,7 @@ }, { "cell_type": "markdown", - "id": "b252c4a7", + "id": "7bcf7188", "metadata": { "editable": true }, @@ -1543,7 +1545,7 @@ }, { "cell_type": "markdown", - "id": "91b833ad", + "id": "cd094e20", "metadata": { "editable": true }, @@ -1572,7 +1574,7 @@ }, { "cell_type": "markdown", - "id": "e4346288", + "id": "ea99157e", "metadata": { "editable": true }, @@ -1593,7 +1595,7 @@ }, { "cell_type": "markdown", - "id": "341895b3", + "id": "b73754c2", "metadata": { "editable": true }, @@ -1614,7 +1616,7 @@ }, { "cell_type": "markdown", - "id": "37369181", + "id": "aa97c83d", "metadata": { "editable": true }, @@ -1631,7 +1633,7 @@ }, { "cell_type": "markdown", - "id": "130c62a2", + "id": "abe84919", "metadata": { "editable": true }, @@ -1652,7 +1654,7 @@ }, { "cell_type": "markdown", - "id": "8be7d076", + "id": "d3ff207b", "metadata": { "editable": true }, @@ -1668,7 +1670,7 @@ }, { "cell_type": "markdown", - "id": "27f3849a", + "id": "f982c11f", "metadata": { "editable": true }, @@ -1685,7 +1687,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "6485b942", + "id": "04a3e090", "metadata": { "collapsed": false, "editable": true @@ -1726,7 +1728,7 @@ }, { "cell_type": "markdown", - "id": "33f6d6bb", + "id": "95b1f5a5", "metadata": { "editable": true }, @@ -1736,7 +1738,7 @@ }, { "cell_type": "markdown", - "id": "28ab766e", + "id": "0d200eff", "metadata": { "editable": true }, @@ -1747,7 +1749,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "ec9476bf", + "id": "040a69d0", "metadata": { "collapsed": false, "editable": true @@ -1807,7 +1809,7 @@ }, { "cell_type": "markdown", - "id": "77fa7a86", + "id": "49f17f65", "metadata": { "editable": true }, @@ -1817,7 +1819,7 @@ }, { "cell_type": "markdown", - "id": "2222f781", + "id": "714e0891", "metadata": { "editable": true }, @@ -1828,7 +1830,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "4ddc9629", + "id": "28bde670", "metadata": { "collapsed": false, "editable": true @@ -1848,7 +1850,7 @@ }, { "cell_type": "markdown", - "id": "78af4075", + "id": "4440856f", "metadata": { "editable": true }, @@ -1860,7 +1862,7 @@ }, { "cell_type": "markdown", - "id": "16109aee", + "id": "6199da92", "metadata": { "editable": true }, @@ -1872,7 +1874,7 @@ }, { "cell_type": "markdown", - "id": "e0ba2d42", + "id": "62c964e3", "metadata": { "editable": true }, @@ -1887,7 +1889,7 @@ }, { "cell_type": "markdown", - "id": "59a5a47a", + "id": "64ba4c70", "metadata": { "editable": true }, @@ -1899,7 +1901,7 @@ }, { "cell_type": "markdown", - "id": "204ea252", + "id": "66c11135", "metadata": { "editable": true }, @@ -1916,7 +1918,7 @@ }, { "cell_type": "markdown", - "id": "cf69a239", + "id": "0f47b20a", "metadata": { "editable": true }, @@ -1931,7 +1933,7 @@ }, { "cell_type": "markdown", - "id": "634b25c8", + "id": "bda56156", "metadata": { "editable": true }, @@ -1949,7 +1951,7 @@ }, { "cell_type": "markdown", - "id": "c3a9df64", + "id": "1330fab9", "metadata": { "editable": true }, @@ -1961,7 +1963,7 @@ }, { "cell_type": "markdown", - "id": "419f4ca1", + "id": "ae474dfb", "metadata": { "editable": true }, @@ -1979,7 +1981,7 @@ }, { "cell_type": "markdown", - "id": "af3386ac", + "id": "b6cb6fed", "metadata": { "editable": true }, @@ -1992,7 +1994,7 @@ }, { "cell_type": "markdown", - "id": "e254da43", + "id": "2f8f9b4e", "metadata": { "editable": true }, @@ -2004,7 +2006,7 @@ }, { "cell_type": "markdown", - "id": "96ebff08", + "id": "18e74238", "metadata": { "editable": true }, @@ -2022,7 +2024,7 @@ }, { "cell_type": "markdown", - "id": "e482ce9c", + "id": "d10df3e7", "metadata": { "editable": true }, @@ -2040,7 +2042,7 @@ }, { "cell_type": "markdown", - "id": "a5002fb2", + "id": "da21a316", "metadata": { "editable": true }, @@ -2050,7 +2052,7 @@ }, { "cell_type": "markdown", - "id": "95c66242", + "id": "76938a28", "metadata": { "editable": true }, @@ -2068,7 +2070,7 @@ }, { "cell_type": "markdown", - "id": "9590e411", + "id": "65434967", "metadata": { "editable": true }, @@ -2087,7 +2089,7 @@ }, { "cell_type": "markdown", - "id": "8c4ff6f9", + "id": "31d4f5aa", "metadata": { "editable": true }, @@ -2100,7 +2102,7 @@ }, { "cell_type": "markdown", - "id": "4b6ffa92", + "id": "114030e5", "metadata": { "editable": true }, @@ -2118,7 +2120,7 @@ }, { "cell_type": "markdown", - "id": "eb180d8b", + "id": "a93aec4e", "metadata": { "editable": true }, @@ -2129,7 +2131,7 @@ }, { "cell_type": "markdown", - "id": "f1d0bda0", + "id": "7c85562d", "metadata": { "editable": true }, @@ -2148,7 +2150,7 @@ }, { "cell_type": "markdown", - "id": "99837971", + "id": "1152ea5e", "metadata": { "editable": true }, @@ -2166,7 +2168,7 @@ }, { "cell_type": "markdown", - "id": "16029266", + "id": "4f3d4b33", "metadata": { "editable": true }, @@ -2179,7 +2181,7 @@ }, { "cell_type": "markdown", - "id": "cc602c6c", + "id": "4c1ac54e", "metadata": { "editable": true }, @@ -2199,7 +2201,7 @@ }, { "cell_type": "markdown", - "id": "2b825937", + "id": "5c4a861f", "metadata": { "editable": true }, @@ -2232,7 +2234,7 @@ }, { "cell_type": "markdown", - "id": "8ef00888", + "id": "276b271b", "metadata": { "editable": true }, @@ -2244,7 +2246,7 @@ }, { "cell_type": "markdown", - "id": "2617b084", + "id": "63a5b8f1", "metadata": { "editable": true }, @@ -2263,7 +2265,7 @@ }, { "cell_type": "markdown", - "id": "c42c607f", + "id": "316b8c32", "metadata": { "editable": true }, @@ -2277,7 +2279,7 @@ }, { "cell_type": "markdown", - "id": "ca6286ac", + "id": "34ba90c8", "metadata": { "editable": true }, @@ -2300,7 +2302,7 @@ }, { "cell_type": "markdown", - "id": "cb2c654a", + "id": "3019fcaf", "metadata": { "editable": true }, @@ -2319,7 +2321,7 @@ }, { "cell_type": "markdown", - "id": "01494130", + "id": "389ff36b", "metadata": { "editable": true }, @@ -2331,7 +2333,7 @@ }, { "cell_type": "markdown", - "id": "8d4f8bee", + "id": "ee9b399a", "metadata": { "editable": true }, @@ -2341,7 +2343,7 @@ }, { "cell_type": "markdown", - "id": "4d9c8595", + "id": "36f98b26", "metadata": { "editable": true }, @@ -2353,7 +2355,7 @@ }, { "cell_type": "markdown", - "id": "6a5663c8", + "id": "cb7b8839", "metadata": { "editable": true }, @@ -2370,7 +2372,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "2f4f91b8", + "id": "db8d28b5", "metadata": { "collapsed": false, "editable": true diff --git a/doc/LectureNotes/week40.ipynb b/doc/LectureNotes/week40.ipynb index ec90d5954..aa3733b88 100644 --- a/doc/LectureNotes/week40.ipynb +++ b/doc/LectureNotes/week40.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "a8c8eb55", + "id": "2303c986", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "fb2c3381", + "id": "75c3b33e", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "a2dfe61e", + "id": "4ba50982", "metadata": { "editable": true }, @@ -37,13 +37,15 @@ "\n", "\n", "2. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model\n", - "\n", - "" + "\n", + "3. Video of lecture at \n", + "\n", + "4. Whiteboard notes at " ] }, { "cell_type": "markdown", - "id": "e342e71b", + "id": "1d527020", "metadata": { "editable": true }, @@ -65,7 +67,7 @@ }, { "cell_type": "markdown", - "id": "97a574c5", + "id": "63a4d497", "metadata": { "editable": true }, @@ -84,7 +86,7 @@ }, { "cell_type": "markdown", - "id": "22f40b12", + "id": "73621d6b", "metadata": { "editable": true }, @@ -106,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "729f5aba", + "id": "fc1df17b", "metadata": { "editable": true }, @@ -132,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "e2782fb0", + "id": "a3d311e6", "metadata": { "editable": true }, @@ -158,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "7968ba20", + "id": "4120d6f9", "metadata": { "editable": true }, @@ -183,7 +185,7 @@ }, { "cell_type": "markdown", - "id": "8b89d296", + "id": "9e85d1e4", "metadata": { "editable": true }, @@ -195,7 +197,7 @@ }, { "cell_type": "markdown", - "id": "4380d27a", + "id": "a0d8c838", "metadata": { "editable": true }, @@ -207,7 +209,7 @@ }, { "cell_type": "markdown", - "id": "930a5781", + "id": "7cea7945", "metadata": { "editable": true }, @@ -222,7 +224,7 @@ }, { "cell_type": "markdown", - "id": "5c7f6446", + "id": "6adc5106", "metadata": { "editable": true }, @@ -234,7 +236,7 @@ }, { "cell_type": "markdown", - "id": "964ad807", + "id": "f976068e", "metadata": { "editable": true }, @@ -246,7 +248,7 @@ }, { "cell_type": "markdown", - "id": "3214fcef", + "id": "dedf9f0e", "metadata": { "editable": true }, @@ -263,7 +265,7 @@ }, { "cell_type": "markdown", - "id": "db0ae7e7", + "id": "bd8b54ab", "metadata": { "editable": true }, @@ -277,7 +279,7 @@ }, { "cell_type": "markdown", - "id": "81b0f404", + "id": "57bfb17f", "metadata": { "editable": true }, @@ -287,7 +289,7 @@ }, { "cell_type": "markdown", - "id": "a1b9b4b7", + "id": "00aee268", "metadata": { "editable": true }, @@ -299,7 +301,7 @@ }, { "cell_type": "markdown", - "id": "9ee3938f", + "id": "e12940f3", "metadata": { "editable": true }, @@ -311,7 +313,7 @@ }, { "cell_type": "markdown", - "id": "3a4c630e", + "id": "e5b2b29e", "metadata": { "editable": true }, @@ -323,7 +325,7 @@ }, { "cell_type": "markdown", - "id": "38250af6", + "id": "c6c0ba4c", "metadata": { "editable": true }, @@ -334,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "122e8f3d", + "id": "46ee2ea8", "metadata": { "editable": true }, @@ -346,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "b41b63c5", + "id": "9a05709b", "metadata": { "editable": true }, @@ -357,7 +359,7 @@ }, { "cell_type": "markdown", - "id": "80534aba", + "id": "ae1362c9", "metadata": { "editable": true }, @@ -373,7 +375,7 @@ }, { "cell_type": "markdown", - "id": "eb1b9888", + "id": "57f4670b", "metadata": { "editable": true }, @@ -385,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "ab198853", + "id": "1dc19f59", "metadata": { "editable": true }, @@ -395,7 +397,7 @@ }, { "cell_type": "markdown", - "id": "f5b74ed9", + "id": "4e96dc87", "metadata": { "editable": true }, @@ -407,7 +409,7 @@ }, { "cell_type": "markdown", - "id": "7c684bba", + "id": "fa77bec9", "metadata": { "editable": true }, @@ -422,7 +424,7 @@ }, { "cell_type": "markdown", - "id": "7c8b3019", + "id": "1b013fd2", "metadata": { "editable": true }, @@ -434,7 +436,7 @@ }, { "cell_type": "markdown", - "id": "8e203747", + "id": "910f36dd", "metadata": { "editable": true }, @@ -445,7 +447,7 @@ }, { "cell_type": "markdown", - "id": "fbf05462", + "id": "8212d0ed", "metadata": { "editable": true }, @@ -457,7 +459,7 @@ }, { "cell_type": "markdown", - "id": "00138c6f", + "id": "7ae7078b", "metadata": { "editable": true }, @@ -469,7 +471,7 @@ }, { "cell_type": "markdown", - "id": "33e45216", + "id": "59e57d7c", "metadata": { "editable": true }, @@ -481,7 +483,7 @@ }, { "cell_type": "markdown", - "id": "6e353bf4", + "id": "6ffe0955", "metadata": { "editable": true }, @@ -491,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "004a41f8", + "id": "56e9bd82", "metadata": { "editable": true }, @@ -503,7 +505,7 @@ }, { "cell_type": "markdown", - "id": "a232ef44", + "id": "86b12946", "metadata": { "editable": true }, @@ -517,7 +519,7 @@ }, { "cell_type": "markdown", - "id": "d090847e", + "id": "d55394df", "metadata": { "editable": true }, @@ -529,7 +531,7 @@ }, { "cell_type": "markdown", - "id": "46a9546e", + "id": "ee01378a", "metadata": { "editable": true }, @@ -539,7 +541,7 @@ }, { "cell_type": "markdown", - "id": "f85b4c49", + "id": "c7fadfbb", "metadata": { "editable": true }, @@ -551,7 +553,7 @@ }, { "cell_type": "markdown", - "id": "4ba76229", + "id": "e8310f63", "metadata": { "editable": true }, @@ -561,7 +563,7 @@ }, { "cell_type": "markdown", - "id": "9f40376d", + "id": "be651647", "metadata": { "editable": true }, @@ -573,7 +575,7 @@ }, { "cell_type": "markdown", - "id": "e344416d", + "id": "e277c601", "metadata": { "editable": true }, @@ -584,7 +586,7 @@ }, { "cell_type": "markdown", - "id": "f9107f2a", + "id": "aea3a410", "metadata": { "editable": true }, @@ -607,7 +609,7 @@ }, { "cell_type": "markdown", - "id": "36ce9b10", + "id": "bfa7221f", "metadata": { "editable": true }, @@ -619,7 +621,7 @@ }, { "cell_type": "markdown", - "id": "2cd2dfd0", + "id": "3d749c39", "metadata": { "editable": true }, @@ -629,7 +631,7 @@ }, { "cell_type": "markdown", - "id": "ce302774", + "id": "dc061a39", "metadata": { "editable": true }, @@ -641,7 +643,7 @@ }, { "cell_type": "markdown", - "id": "d443be10", + "id": "8ea10488", "metadata": { "editable": true }, @@ -658,7 +660,7 @@ }, { "cell_type": "markdown", - "id": "6755463b", + "id": "9cb3baf8", "metadata": { "editable": true }, @@ -677,7 +679,7 @@ }, { "cell_type": "markdown", - "id": "84f1d6cb", + "id": "387393d7", "metadata": { "editable": true }, @@ -694,7 +696,7 @@ }, { "cell_type": "markdown", - "id": "d1d136eb", + "id": "30f64659", "metadata": { "editable": true }, @@ -709,7 +711,7 @@ }, { "cell_type": "markdown", - "id": "34bda880", + "id": "3ba65422", "metadata": { "editable": true }, @@ -719,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "853aa76a", + "id": "005f46d7", "metadata": { "editable": true }, @@ -735,7 +737,7 @@ }, { "cell_type": "markdown", - "id": "b3fb4491", + "id": "61a638bc", "metadata": { "editable": true }, @@ -747,7 +749,7 @@ }, { "cell_type": "markdown", - "id": "453c22a3", + "id": "469c0042", "metadata": { "editable": true }, @@ -758,7 +760,7 @@ }, { "cell_type": "markdown", - "id": "611c9632", + "id": "0af5449a", "metadata": { "editable": true }, @@ -770,7 +772,7 @@ }, { "cell_type": "markdown", - "id": "c45aca41", + "id": "f4c16b4f", "metadata": { "editable": true }, @@ -780,7 +782,7 @@ }, { "cell_type": "markdown", - "id": "35751022", + "id": "ddbe7f50", "metadata": { "editable": true }, @@ -794,7 +796,7 @@ }, { "cell_type": "markdown", - "id": "781c6916", + "id": "52830f96", "metadata": { "editable": true }, @@ -806,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "446ec153", + "id": "1b8a1c14", "metadata": { "editable": true }, @@ -816,7 +818,7 @@ }, { "cell_type": "markdown", - "id": "fa87caf0", + "id": "8ad73cea", "metadata": { "editable": true }, @@ -828,7 +830,7 @@ }, { "cell_type": "markdown", - "id": "6a5dfd86", + "id": "6d47dd0b", "metadata": { "editable": true }, @@ -840,7 +842,7 @@ }, { "cell_type": "markdown", - "id": "a0e56fe0", + "id": "f399c2f4", "metadata": { "editable": true }, @@ -853,7 +855,7 @@ { "cell_type": "code", "execution_count": 1, - "id": "ea0068e6", + "id": "79f6b6fc", "metadata": { "collapsed": false, "editable": true @@ -996,7 +998,7 @@ }, { "cell_type": "markdown", - "id": "9fe762a6", + "id": "24e84b29", "metadata": { "editable": true }, @@ -1015,7 +1017,7 @@ { "cell_type": "code", "execution_count": 2, - "id": "b6f32217", + "id": "7a73eca4", "metadata": { "collapsed": false, "editable": true @@ -1058,7 +1060,7 @@ }, { "cell_type": "markdown", - "id": "9f278530", + "id": "40d4b30f", "metadata": { "editable": true }, @@ -1072,7 +1074,7 @@ { "cell_type": "code", "execution_count": 3, - "id": "88e13c71", + "id": "ac0089bf", "metadata": { "collapsed": false, "editable": true @@ -1161,7 +1163,7 @@ }, { "cell_type": "markdown", - "id": "938925ff", + "id": "1e9acef3", "metadata": { "editable": true }, @@ -1178,7 +1180,7 @@ { "cell_type": "code", "execution_count": 4, - "id": "c01e1379", + "id": "9153234a", "metadata": { "collapsed": false, "editable": true @@ -1207,7 +1209,7 @@ }, { "cell_type": "markdown", - "id": "a2b8d6a4", + "id": "908d547b", "metadata": { "editable": true }, @@ -1221,7 +1223,7 @@ { "cell_type": "code", "execution_count": 5, - "id": "6ec30a76", + "id": "8a46f4f3", "metadata": { "collapsed": false, "editable": true @@ -1266,7 +1268,7 @@ }, { "cell_type": "markdown", - "id": "1c00160e", + "id": "ba0275a7", "metadata": { "editable": true }, @@ -1291,7 +1293,7 @@ { "cell_type": "code", "execution_count": 6, - "id": "1468b093", + "id": "1af34f8e", "metadata": { "collapsed": false, "editable": true @@ -1303,7 +1305,7 @@ }, { "cell_type": "markdown", - "id": "509b083d", + "id": "1eac30d3", "metadata": { "editable": true }, @@ -1314,7 +1316,7 @@ { "cell_type": "code", "execution_count": 7, - "id": "c58d0fa7", + "id": "a0cdd9c9", "metadata": { "collapsed": false, "editable": true @@ -1326,7 +1328,7 @@ }, { "cell_type": "markdown", - "id": "a0445ed4", + "id": "013777ad", "metadata": { "editable": true }, @@ -1339,7 +1341,7 @@ }, { "cell_type": "markdown", - "id": "93e60a48", + "id": "410f90ac", "metadata": { "editable": true }, @@ -1350,7 +1352,7 @@ { "cell_type": "code", "execution_count": 8, - "id": "c0c0be2f", + "id": "fa16a459", "metadata": { "collapsed": false, "editable": true @@ -1393,7 +1395,7 @@ }, { "cell_type": "markdown", - "id": "c4e95738", + "id": "a721de53", "metadata": { "editable": true }, @@ -1411,7 +1413,7 @@ }, { "cell_type": "markdown", - "id": "455b9abe", + "id": "68de5052", "metadata": { "editable": true }, @@ -1435,7 +1437,7 @@ }, { "cell_type": "markdown", - "id": "fafe38d0", + "id": "7685af02", "metadata": { "editable": true }, @@ -1453,7 +1455,7 @@ }, { "cell_type": "markdown", - "id": "a2b62ddd", + "id": "3dfcfcb0", "metadata": { "editable": true }, @@ -1493,7 +1495,7 @@ }, { "cell_type": "markdown", - "id": "86b9d89a", + "id": "0d037ca7", "metadata": { "editable": true }, @@ -1522,7 +1524,7 @@ }, { "cell_type": "markdown", - "id": "b252c4a7", + "id": "7bcf7188", "metadata": { "editable": true }, @@ -1543,7 +1545,7 @@ }, { "cell_type": "markdown", - "id": "91b833ad", + "id": "cd094e20", "metadata": { "editable": true }, @@ -1572,7 +1574,7 @@ }, { "cell_type": "markdown", - "id": "e4346288", + "id": "ea99157e", "metadata": { "editable": true }, @@ -1593,7 +1595,7 @@ }, { "cell_type": "markdown", - "id": "341895b3", + "id": "b73754c2", "metadata": { "editable": true }, @@ -1614,7 +1616,7 @@ }, { "cell_type": "markdown", - "id": "37369181", + "id": "aa97c83d", "metadata": { "editable": true }, @@ -1631,7 +1633,7 @@ }, { "cell_type": "markdown", - "id": "130c62a2", + "id": "abe84919", "metadata": { "editable": true }, @@ -1652,7 +1654,7 @@ }, { "cell_type": "markdown", - "id": "8be7d076", + "id": "d3ff207b", "metadata": { "editable": true }, @@ -1668,7 +1670,7 @@ }, { "cell_type": "markdown", - "id": "27f3849a", + "id": "f982c11f", "metadata": { "editable": true }, @@ -1685,7 +1687,7 @@ { "cell_type": "code", "execution_count": 9, - "id": "6485b942", + "id": "04a3e090", "metadata": { "collapsed": false, "editable": true @@ -1726,7 +1728,7 @@ }, { "cell_type": "markdown", - "id": "33f6d6bb", + "id": "95b1f5a5", "metadata": { "editable": true }, @@ -1736,7 +1738,7 @@ }, { "cell_type": "markdown", - "id": "28ab766e", + "id": "0d200eff", "metadata": { "editable": true }, @@ -1747,7 +1749,7 @@ { "cell_type": "code", "execution_count": 10, - "id": "ec9476bf", + "id": "040a69d0", "metadata": { "collapsed": false, "editable": true @@ -1807,7 +1809,7 @@ }, { "cell_type": "markdown", - "id": "77fa7a86", + "id": "49f17f65", "metadata": { "editable": true }, @@ -1817,7 +1819,7 @@ }, { "cell_type": "markdown", - "id": "2222f781", + "id": "714e0891", "metadata": { "editable": true }, @@ -1828,7 +1830,7 @@ { "cell_type": "code", "execution_count": 11, - "id": "4ddc9629", + "id": "28bde670", "metadata": { "collapsed": false, "editable": true @@ -1848,7 +1850,7 @@ }, { "cell_type": "markdown", - "id": "78af4075", + "id": "4440856f", "metadata": { "editable": true }, @@ -1860,7 +1862,7 @@ }, { "cell_type": "markdown", - "id": "16109aee", + "id": "6199da92", "metadata": { "editable": true }, @@ -1872,7 +1874,7 @@ }, { "cell_type": "markdown", - "id": "e0ba2d42", + "id": "62c964e3", "metadata": { "editable": true }, @@ -1887,7 +1889,7 @@ }, { "cell_type": "markdown", - "id": "59a5a47a", + "id": "64ba4c70", "metadata": { "editable": true }, @@ -1899,7 +1901,7 @@ }, { "cell_type": "markdown", - "id": "204ea252", + "id": "66c11135", "metadata": { "editable": true }, @@ -1916,7 +1918,7 @@ }, { "cell_type": "markdown", - "id": "cf69a239", + "id": "0f47b20a", "metadata": { "editable": true }, @@ -1931,7 +1933,7 @@ }, { "cell_type": "markdown", - "id": "634b25c8", + "id": "bda56156", "metadata": { "editable": true }, @@ -1949,7 +1951,7 @@ }, { "cell_type": "markdown", - "id": "c3a9df64", + "id": "1330fab9", "metadata": { "editable": true }, @@ -1961,7 +1963,7 @@ }, { "cell_type": "markdown", - "id": "419f4ca1", + "id": "ae474dfb", "metadata": { "editable": true }, @@ -1979,7 +1981,7 @@ }, { "cell_type": "markdown", - "id": "af3386ac", + "id": "b6cb6fed", "metadata": { "editable": true }, @@ -1992,7 +1994,7 @@ }, { "cell_type": "markdown", - "id": "e254da43", + "id": "2f8f9b4e", "metadata": { "editable": true }, @@ -2004,7 +2006,7 @@ }, { "cell_type": "markdown", - "id": "96ebff08", + "id": "18e74238", "metadata": { "editable": true }, @@ -2022,7 +2024,7 @@ }, { "cell_type": "markdown", - "id": "e482ce9c", + "id": "d10df3e7", "metadata": { "editable": true }, @@ -2040,7 +2042,7 @@ }, { "cell_type": "markdown", - "id": "a5002fb2", + "id": "da21a316", "metadata": { "editable": true }, @@ -2050,7 +2052,7 @@ }, { "cell_type": "markdown", - "id": "95c66242", + "id": "76938a28", "metadata": { "editable": true }, @@ -2068,7 +2070,7 @@ }, { "cell_type": "markdown", - "id": "9590e411", + "id": "65434967", "metadata": { "editable": true }, @@ -2087,7 +2089,7 @@ }, { "cell_type": "markdown", - "id": "8c4ff6f9", + "id": "31d4f5aa", "metadata": { "editable": true }, @@ -2100,7 +2102,7 @@ }, { "cell_type": "markdown", - "id": "4b6ffa92", + "id": "114030e5", "metadata": { "editable": true }, @@ -2118,7 +2120,7 @@ }, { "cell_type": "markdown", - "id": "eb180d8b", + "id": "a93aec4e", "metadata": { "editable": true }, @@ -2129,7 +2131,7 @@ }, { "cell_type": "markdown", - "id": "f1d0bda0", + "id": "7c85562d", "metadata": { "editable": true }, @@ -2148,7 +2150,7 @@ }, { "cell_type": "markdown", - "id": "99837971", + "id": "1152ea5e", "metadata": { "editable": true }, @@ -2166,7 +2168,7 @@ }, { "cell_type": "markdown", - "id": "16029266", + "id": "4f3d4b33", "metadata": { "editable": true }, @@ -2179,7 +2181,7 @@ }, { "cell_type": "markdown", - "id": "cc602c6c", + "id": "4c1ac54e", "metadata": { "editable": true }, @@ -2199,7 +2201,7 @@ }, { "cell_type": "markdown", - "id": "2b825937", + "id": "5c4a861f", "metadata": { "editable": true }, @@ -2232,7 +2234,7 @@ }, { "cell_type": "markdown", - "id": "8ef00888", + "id": "276b271b", "metadata": { "editable": true }, @@ -2244,7 +2246,7 @@ }, { "cell_type": "markdown", - "id": "2617b084", + "id": "63a5b8f1", "metadata": { "editable": true }, @@ -2263,7 +2265,7 @@ }, { "cell_type": "markdown", - "id": "c42c607f", + "id": "316b8c32", "metadata": { "editable": true }, @@ -2277,7 +2279,7 @@ }, { "cell_type": "markdown", - "id": "ca6286ac", + "id": "34ba90c8", "metadata": { "editable": true }, @@ -2300,7 +2302,7 @@ }, { "cell_type": "markdown", - "id": "cb2c654a", + "id": "3019fcaf", "metadata": { "editable": true }, @@ -2319,7 +2321,7 @@ }, { "cell_type": "markdown", - "id": "01494130", + "id": "389ff36b", "metadata": { "editable": true }, @@ -2331,7 +2333,7 @@ }, { "cell_type": "markdown", - "id": "8d4f8bee", + "id": "ee9b399a", "metadata": { "editable": true }, @@ -2341,7 +2343,7 @@ }, { "cell_type": "markdown", - "id": "4d9c8595", + "id": "36f98b26", "metadata": { "editable": true }, @@ -2353,7 +2355,7 @@ }, { "cell_type": "markdown", - "id": "6a5663c8", + "id": "cb7b8839", "metadata": { "editable": true }, @@ -2370,7 +2372,7 @@ { "cell_type": "code", "execution_count": 12, - "id": "2f4f91b8", + "id": "db8d28b5", "metadata": { "collapsed": false, "editable": true diff --git a/doc/pub/week40/html/._week40-bs001.html b/doc/pub/week40/html/._week40-bs001.html index 81a8bfdab..f3f860452 100644 --- a/doc/pub/week40/html/._week40-bs001.html +++ b/doc/pub/week40/html/._week40-bs001.html @@ -267,9 +267,9 @@ MathJax.Hub.Config({
  1. Logistic regression and gradient descent, examples on how to code
    2. -
  2. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model - -
    3. +
  3. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
    4. +
  4. Video of lecture at https://youtu.be/MS3Tv8FVArs
    5. +
  5. Whiteboard notes at https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf
diff --git a/doc/pub/week40/html/week40-reveal.html b/doc/pub/week40/html/week40-reveal.html index a5f1ec242..83dec048c 100644 --- a/doc/pub/week40/html/week40-reveal.html +++ b/doc/pub/week40/html/week40-reveal.html @@ -199,9 +199,9 @@ MathJax.Hub.Config({

  1. Logistic regression and gradient descent, examples on how to code
    2. -

  2. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model - -
    3. +

  3. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
    4. +

  4. Video of lecture at https://youtu.be/MS3Tv8FVArs
    5. +

  5. Whiteboard notes at https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf
diff --git a/doc/pub/week40/html/week40-solarized.html b/doc/pub/week40/html/week40-solarized.html index fb1a4149e..92aaf3cdb 100644 --- a/doc/pub/week40/html/week40-solarized.html +++ b/doc/pub/week40/html/week40-solarized.html @@ -240,9 +240,9 @@ MathJax.Hub.Config({
  1. Logistic regression and gradient descent, examples on how to code
    2. -
  2. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model - -
    3. +
  3. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
    4. +
  4. Video of lecture at https://youtu.be/MS3Tv8FVArs
    5. +
  5. Whiteboard notes at https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf
diff --git a/doc/pub/week40/html/week40.html b/doc/pub/week40/html/week40.html index c266462d9..4c3526ca6 100644 --- a/doc/pub/week40/html/week40.html +++ b/doc/pub/week40/html/week40.html @@ -317,9 +317,9 @@ MathJax.Hub.Config({
  1. Logistic regression and gradient descent, examples on how to code
    2. -
  2. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model - -
    3. +
  3. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model
    4. +
  4. Video of lecture at https://youtu.be/MS3Tv8FVArs
    5. +
  5. Whiteboard notes at https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf
diff --git a/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz b/doc/pub/week40/ipynb/ipynb-week40-src.tar.gz index be49d0b9e2441d65df903a7d8a6f47234d2e31a4..cbb8081d6975a3b340332cca49f03e8631a11b5c 100644 GIT binary patch delta 17 YcmbQ-!!)^viA}zngF$}UMmF9y057Qo`Tzg` delta 17 YcmbQ-!!)^viA}zngMm+CBO7lU04&V}YXATM diff --git a/doc/pub/week40/ipynb/week40.ipynb b/doc/pub/week40/ipynb/week40.ipynb index b8cd53b1f..aa3733b88 100644 --- a/doc/pub/week40/ipynb/week40.ipynb +++ b/doc/pub/week40/ipynb/week40.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "markdown", - "id": "a8c8eb55", + "id": "2303c986", "metadata": { "editable": true }, @@ -14,7 +14,7 @@ }, { "cell_type": "markdown", - "id": "fb2c3381", + "id": "75c3b33e", "metadata": { "editable": true }, @@ -27,7 +27,7 @@ }, { "cell_type": "markdown", - "id": "a2dfe61e", + "id": "4ba50982", "metadata": { "editable": true }, @@ -37,13 +37,15 @@ "\n", "\n", "2. Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model\n", - "\n", - "" + "\n", + "3. Video of lecture at \n", + "\n", + "4. Whiteboard notes at " ] }, { "cell_type": "markdown", - "id": "e342e71b", + "id": "1d527020", "metadata": { "editable": true }, @@ -65,7 +67,7 @@ }, { "cell_type": "markdown", - "id": "97a574c5", + "id": "63a4d497", "metadata": { "editable": true }, @@ -84,7 +86,7 @@ }, { "cell_type": "markdown", - "id": "22f40b12", + "id": "73621d6b", "metadata": { "editable": true }, @@ -106,7 +108,7 @@ }, { "cell_type": "markdown", - "id": "729f5aba", + "id": "fc1df17b", "metadata": { "editable": true }, @@ -132,7 +134,7 @@ }, { "cell_type": "markdown", - "id": "e2782fb0", + "id": "a3d311e6", "metadata": { "editable": true }, @@ -158,7 +160,7 @@ }, { "cell_type": "markdown", - "id": "7968ba20", + "id": "4120d6f9", "metadata": { "editable": true }, @@ -183,7 +185,7 @@ }, { "cell_type": "markdown", - "id": "8b89d296", + "id": "9e85d1e4", "metadata": { "editable": true }, @@ -195,7 +197,7 @@ }, { "cell_type": "markdown", - "id": "4380d27a", + "id": "a0d8c838", "metadata": { "editable": true }, @@ -207,7 +209,7 @@ }, { "cell_type": "markdown", - "id": "930a5781", + "id": "7cea7945", "metadata": { "editable": true }, @@ -222,7 +224,7 @@ }, { "cell_type": "markdown", - "id": "5c7f6446", + "id": "6adc5106", "metadata": { "editable": true }, @@ -234,7 +236,7 @@ }, { "cell_type": "markdown", - "id": "964ad807", + "id": "f976068e", "metadata": { "editable": true }, @@ -246,7 +248,7 @@ }, { "cell_type": "markdown", - "id": "3214fcef", + "id": "dedf9f0e", "metadata": { "editable": true }, @@ -263,7 +265,7 @@ }, { "cell_type": "markdown", - "id": "db0ae7e7", + "id": "bd8b54ab", "metadata": { "editable": true }, @@ -277,7 +279,7 @@ }, { "cell_type": "markdown", - "id": "81b0f404", + "id": "57bfb17f", "metadata": { "editable": true }, @@ -287,7 +289,7 @@ }, { "cell_type": "markdown", - "id": "a1b9b4b7", + "id": "00aee268", "metadata": { "editable": true }, @@ -299,7 +301,7 @@ }, { "cell_type": "markdown", - "id": "9ee3938f", + "id": "e12940f3", "metadata": { "editable": true }, @@ -311,7 +313,7 @@ }, { "cell_type": "markdown", - "id": "3a4c630e", + "id": "e5b2b29e", "metadata": { "editable": true }, @@ -323,7 +325,7 @@ }, { "cell_type": "markdown", - "id": "38250af6", + "id": "c6c0ba4c", "metadata": { "editable": true }, @@ -334,7 +336,7 @@ }, { "cell_type": "markdown", - "id": "122e8f3d", + "id": "46ee2ea8", "metadata": { "editable": true }, @@ -346,7 +348,7 @@ }, { "cell_type": "markdown", - "id": "b41b63c5", + "id": "9a05709b", "metadata": { "editable": true }, @@ -357,7 +359,7 @@ }, { "cell_type": "markdown", - "id": "80534aba", + "id": "ae1362c9", "metadata": { "editable": true }, @@ -373,7 +375,7 @@ }, { "cell_type": "markdown", - "id": "eb1b9888", + "id": "57f4670b", "metadata": { "editable": true }, @@ -385,7 +387,7 @@ }, { "cell_type": "markdown", - "id": "ab198853", + "id": "1dc19f59", "metadata": { "editable": true }, @@ -395,7 +397,7 @@ }, { "cell_type": "markdown", - "id": "f5b74ed9", + "id": "4e96dc87", "metadata": { "editable": true }, @@ -407,7 +409,7 @@ }, { "cell_type": "markdown", - "id": "7c684bba", + "id": "fa77bec9", "metadata": { "editable": true }, @@ -422,7 +424,7 @@ }, { "cell_type": "markdown", - "id": "7c8b3019", + "id": "1b013fd2", "metadata": { "editable": true }, @@ -434,7 +436,7 @@ }, { "cell_type": "markdown", - "id": "8e203747", + "id": "910f36dd", "metadata": { "editable": true }, @@ -445,7 +447,7 @@ }, { "cell_type": "markdown", - "id": "fbf05462", + "id": "8212d0ed", "metadata": { "editable": true }, @@ -457,7 +459,7 @@ }, { "cell_type": "markdown", - "id": "00138c6f", + "id": "7ae7078b", "metadata": { "editable": true }, @@ -469,7 +471,7 @@ }, { "cell_type": "markdown", - "id": "33e45216", + "id": "59e57d7c", "metadata": { "editable": true }, @@ -481,7 +483,7 @@ }, { "cell_type": "markdown", - "id": "6e353bf4", + "id": "6ffe0955", "metadata": { "editable": true }, @@ -491,7 +493,7 @@ }, { "cell_type": "markdown", - "id": "004a41f8", + "id": "56e9bd82", "metadata": { "editable": true }, @@ -503,7 +505,7 @@ }, { "cell_type": "markdown", - "id": "a232ef44", + "id": "86b12946", "metadata": { "editable": true }, @@ -517,7 +519,7 @@ }, { "cell_type": "markdown", - "id": "d090847e", + "id": "d55394df", "metadata": { "editable": true }, @@ -529,7 +531,7 @@ }, { "cell_type": "markdown", - "id": "46a9546e", + "id": "ee01378a", "metadata": { "editable": true }, @@ -539,7 +541,7 @@ }, { "cell_type": "markdown", - "id": "f85b4c49", + "id": "c7fadfbb", "metadata": { "editable": true }, @@ -551,7 +553,7 @@ }, { "cell_type": "markdown", - "id": "4ba76229", + "id": "e8310f63", "metadata": { "editable": true }, @@ -561,7 +563,7 @@ }, { "cell_type": "markdown", - "id": "9f40376d", + "id": "be651647", "metadata": { "editable": true }, @@ -573,7 +575,7 @@ }, { "cell_type": "markdown", - "id": "e344416d", + "id": "e277c601", "metadata": { "editable": true }, @@ -584,7 +586,7 @@ }, { "cell_type": "markdown", - "id": "f9107f2a", + "id": "aea3a410", "metadata": { "editable": true }, @@ -607,7 +609,7 @@ }, { "cell_type": "markdown", - "id": "36ce9b10", + "id": "bfa7221f", "metadata": { "editable": true }, @@ -619,7 +621,7 @@ }, { "cell_type": "markdown", - "id": "2cd2dfd0", + "id": "3d749c39", "metadata": { "editable": true }, @@ -629,7 +631,7 @@ }, { "cell_type": "markdown", - "id": "ce302774", + "id": "dc061a39", "metadata": { "editable": true }, @@ -641,7 +643,7 @@ }, { "cell_type": "markdown", - "id": "d443be10", + "id": "8ea10488", "metadata": { "editable": true }, @@ -658,7 +660,7 @@ }, { "cell_type": "markdown", - "id": "6755463b", + "id": "9cb3baf8", "metadata": { "editable": true }, @@ -677,7 +679,7 @@ }, { "cell_type": "markdown", - "id": "84f1d6cb", + "id": "387393d7", "metadata": { "editable": true }, @@ -694,7 +696,7 @@ }, { "cell_type": "markdown", - "id": "d1d136eb", + "id": "30f64659", "metadata": { "editable": true }, @@ -709,7 +711,7 @@ }, { "cell_type": "markdown", - "id": "34bda880", + "id": "3ba65422", "metadata": { "editable": true }, @@ -719,7 +721,7 @@ }, { "cell_type": "markdown", - "id": "853aa76a", + "id": "005f46d7", "metadata": { "editable": true }, @@ -735,7 +737,7 @@ }, { "cell_type": "markdown", - "id": "b3fb4491", + "id": "61a638bc", "metadata": { "editable": true }, @@ -747,7 +749,7 @@ }, { "cell_type": "markdown", - "id": "453c22a3", + "id": "469c0042", "metadata": { "editable": true }, @@ -758,7 +760,7 @@ }, { "cell_type": "markdown", - "id": "611c9632", + "id": "0af5449a", "metadata": { "editable": true }, @@ -770,7 +772,7 @@ }, { "cell_type": "markdown", - "id": "c45aca41", + "id": "f4c16b4f", "metadata": { "editable": true }, @@ -780,7 +782,7 @@ }, { "cell_type": "markdown", - "id": "35751022", + "id": "ddbe7f50", "metadata": { "editable": true }, @@ -794,7 +796,7 @@ }, { "cell_type": "markdown", - "id": "781c6916", + "id": "52830f96", "metadata": { "editable": true }, @@ -806,7 +808,7 @@ }, { "cell_type": "markdown", - "id": "446ec153", + "id": "1b8a1c14", "metadata": { "editable": true }, @@ -816,7 +818,7 @@ }, { "cell_type": "markdown", - "id": "fa87caf0", + "id": "8ad73cea", "metadata": { "editable": true }, @@ -828,7 +830,7 @@ }, { "cell_type": "markdown", - "id": "6a5dfd86", + "id": "6d47dd0b", "metadata": { "editable": true }, @@ -840,7 +842,7 @@ }, { "cell_type": "markdown", - "id": "a0e56fe0", + "id": "f399c2f4", "metadata": { "editable": true }, @@ -853,13 +855,10 @@ { "cell_type": "code", "execution_count": 1, - "id": "ea0068e6", + "id": "79f6b6fc", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -999,7 +998,7 @@ }, { "cell_type": "markdown", - "id": "9fe762a6", + "id": "24e84b29", "metadata": { "editable": true }, @@ -1017,14 +1016,11 @@ }, { "cell_type": "code", - "execution_count": 4, - "id": "b6f32217", + "execution_count": 2, + "id": "7a73eca4", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1064,7 +1060,7 @@ }, { "cell_type": "markdown", - "id": "9f278530", + "id": "40d4b30f", "metadata": { "editable": true }, @@ -1077,25 +1073,13 @@ }, { "cell_type": "code", - "execution_count": 5, - "id": "88e13c71", + "execution_count": 3, + "id": "ac0089bf", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Binary Classification - Accuracy: 0.99, Cross-Entropy Loss: 0.01\n", - "Multiclass Classification - Accuracy: 0.97, Cross-Entropy Loss: 0.12\n" - ] - } - ], + "outputs": [], "source": [ "import numpy as np\n", "\n", @@ -1179,7 +1163,7 @@ }, { "cell_type": "markdown", - "id": "938925ff", + "id": "1e9acef3", "metadata": { "editable": true }, @@ -1195,40 +1179,13 @@ }, { "cell_type": "code", - "execution_count": 6, - "id": "c01e1379", + "execution_count": 4, + "id": "9153234a", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(426, 30)\n", - "(143, 30)\n", - "Test set accuracy with Logistic Regression: 0.95\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n" - ] - } - ], + "outputs": [], "source": [ "%matplotlib inline\n", "\n", @@ -1252,7 +1209,7 @@ }, { "cell_type": "markdown", - "id": "a2b8d6a4", + "id": "908d547b", "metadata": { "editable": true }, @@ -1265,37 +1222,13 @@ }, { "cell_type": "code", - "execution_count": 7, - "id": "6ec30a76", + "execution_count": 5, + "id": "8a46f4f3", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "data": { - "image/png": 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", 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", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1335,7 +1268,7 @@ }, { "cell_type": "markdown", - "id": "1c00160e", + "id": "ba0275a7", "metadata": { "editable": true }, @@ -1360,13 +1293,10 @@ { "cell_type": "code", "execution_count": 6, - "id": "1468b093", + "id": "1af34f8e", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1375,7 +1305,7 @@ }, { "cell_type": "markdown", - "id": "509b083d", + "id": "1eac30d3", "metadata": { "editable": true }, @@ -1386,13 +1316,10 @@ { "cell_type": "code", "execution_count": 7, - "id": "c58d0fa7", + "id": "a0cdd9c9", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -1401,7 +1328,7 @@ }, { "cell_type": "markdown", - "id": "a0445ed4", + "id": "013777ad", "metadata": { "editable": true }, @@ -1414,7 +1341,7 @@ }, { "cell_type": "markdown", - "id": "93e60a48", + "id": "410f90ac", "metadata": { "editable": true }, @@ -1425,157 +1352,12 @@ { "cell_type": "code", "execution_count": 8, - "id": "c0c0be2f", + "id": "fa16a459", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(426, 30)\n", - "(143, 30)\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n", - "/Users/mhjensen/miniforge3/envs/myenv/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:465: ConvergenceWarning: lbfgs failed to converge (status=1):\n", - "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n", - "\n", - "Increase the number of iterations (max_iter) or scale the data as shown in:\n", - " https://scikit-learn.org/stable/modules/preprocessing.html\n", - "Please also refer to the documentation for alternative solver options:\n", - " https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n", - " n_iter_i = _check_optimize_result(\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[1. 0.86666667 1. 0.85714286 1. 0.85714286\n", - " 1. 0.92857143 0.92857143 1. ]\n", - "Test set accuracy with Logistic Regression: 0.95\n" - ] - }, - { - "data": { - "image/png": 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", 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", 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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "import numpy as np\n", @@ -1613,7 +1395,7 @@ }, { "cell_type": "markdown", - "id": "c4e95738", + "id": "a721de53", "metadata": { "editable": true }, @@ -1631,7 +1413,7 @@ }, { "cell_type": "markdown", - "id": "455b9abe", + "id": "68de5052", "metadata": { "editable": true }, @@ -1655,7 +1437,7 @@ }, { "cell_type": "markdown", - "id": "fafe38d0", + "id": "7685af02", "metadata": { "editable": true }, @@ -1673,7 +1455,7 @@ }, { "cell_type": "markdown", - "id": "a2b62ddd", + "id": "3dfcfcb0", "metadata": { "editable": true }, @@ -1713,7 +1495,7 @@ }, { "cell_type": "markdown", - "id": "86b9d89a", + "id": "0d037ca7", "metadata": { "editable": true }, @@ -1742,7 +1524,7 @@ }, { "cell_type": "markdown", - "id": "b252c4a7", + "id": "7bcf7188", "metadata": { "editable": true }, @@ -1763,7 +1545,7 @@ }, { "cell_type": "markdown", - "id": "91b833ad", + "id": "cd094e20", "metadata": { "editable": true }, @@ -1792,7 +1574,7 @@ }, { "cell_type": "markdown", - "id": "e4346288", + "id": "ea99157e", "metadata": { "editable": true }, @@ -1813,7 +1595,7 @@ }, { "cell_type": "markdown", - "id": "341895b3", + "id": "b73754c2", "metadata": { "editable": true }, @@ -1834,7 +1616,7 @@ }, { "cell_type": "markdown", - "id": "37369181", + "id": "aa97c83d", "metadata": { "editable": true }, @@ -1851,7 +1633,7 @@ }, { "cell_type": "markdown", - "id": "130c62a2", + "id": "abe84919", "metadata": { "editable": true }, @@ -1872,7 +1654,7 @@ }, { "cell_type": "markdown", - "id": "8be7d076", + "id": "d3ff207b", "metadata": { "editable": true }, @@ -1888,7 +1670,7 @@ }, { "cell_type": "markdown", - "id": "27f3849a", + "id": "f982c11f", "metadata": { "editable": true }, @@ -1905,34 +1687,12 @@ { "cell_type": "code", "execution_count": 9, - "id": "6485b942", + "id": "04a3e090", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The X.TX matrix:[[4. 2. 2.]\n", - " [2. 2. 1.]\n", - " [2. 1. 2.]]\n", - "The invers of X.TX matrix:[[ 7.50000000e-01 -5.00000000e-01 -5.00000000e-01]\n", - " [-5.00000000e-01 1.00000000e+00 -2.27693602e-16]\n", - " [-5.00000000e-01 9.94484047e-17 1.00000000e+00]]\n", - "The values of theta for the XOR gate:[ 5.00000000e-01 -2.22044605e-16 -1.11022302e-16]\n", - "The linear regression prediction for the XOR gate:[0.5 0.5 0.5 0.5]\n", - "The values of theta for the OR gate:[0.25 0.5 0.5 ]\n", - "The linear regression prediction for the OR gate:[0.25 0.75 0.75 1.25]\n", - "The values of theta for the AND gate:[-0.25 0.5 0.5 ]\n", - "The linear regression prediction for the AND gate:[-0.25 0.25 0.25 0.75]\n" - ] - } - ], + "outputs": [], "source": [ "\"\"\"\n", "Simple code that tests XOR, OR and AND gates with linear regression\n", @@ -1968,7 +1728,7 @@ }, { "cell_type": "markdown", - "id": "33f6d6bb", + "id": "95b1f5a5", "metadata": { "editable": true }, @@ -1978,7 +1738,7 @@ }, { "cell_type": "markdown", - "id": "28ab766e", + "id": "0d200eff", "metadata": { "editable": true }, @@ -1989,37 +1749,12 @@ { "cell_type": "code", "execution_count": 10, - "id": "ec9476bf", + "id": "040a69d0", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "The X.TX matrix:[[4. 2. 2.]\n", - " [2. 2. 1.]\n", - " [2. 1. 2.]]\n", - "The invers of X.TX matrix:[[ 7.50000000e-01 -5.00000000e-01 -5.00000000e-01]\n", - " [-5.00000000e-01 1.00000000e+00 -2.27693602e-16]\n", - " [-5.00000000e-01 9.94484047e-17 1.00000000e+00]]\n", - "The values of theta for the XOR gate:[ 5.00000000e-01 -2.22044605e-16 -1.11022302e-16]\n", - "The linear regression prediction for the XOR gate:[0.5 0.5 0.5 0.5]\n", - "The values of theta for the OR gate:[0.25 0.5 0.5 ]\n", - "The linear regression prediction for the OR gate:[0.25 0.75 0.75 1.25]\n", - "The values of theta for the AND gate:[-0.25 0.5 0.5 ]\n", - "The linear regression prediction for the AND gate:[-0.25 0.25 0.25 0.75]\n", - "Test set accuracy with Logistic Regression for OR gate: 0.75\n", - "Test set accuracy with Logistic Regression for XOR gate: 0.50\n", - "Test set accuracy with Logistic Regression for AND gate: 0.75\n" - ] - } - ], + "outputs": [], "source": [ "\"\"\"\n", "Simple code that tests XOR and OR gates with linear regression\n", @@ -2074,7 +1809,7 @@ }, { "cell_type": "markdown", - "id": "77fa7a86", + "id": "49f17f65", "metadata": { "editable": true }, @@ -2084,7 +1819,7 @@ }, { "cell_type": "markdown", - "id": "2222f781", + "id": "714e0891", "metadata": { "editable": true }, @@ -2095,23 +1830,12 @@ { "cell_type": "code", "execution_count": 11, - "id": "4ddc9629", + "id": "28bde670", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Test set accuracy with Feed Forward Neural Network for XOR gate:1.0\n" - ] - } - ], + "outputs": [], "source": [ "\n", "# and now neural networks with Scikit-Learn and the XOR\n", @@ -2126,7 +1850,7 @@ }, { "cell_type": "markdown", - "id": "78af4075", + "id": "4440856f", "metadata": { "editable": true }, @@ -2138,7 +1862,7 @@ }, { "cell_type": "markdown", - "id": "16109aee", + "id": "6199da92", "metadata": { "editable": true }, @@ -2150,7 +1874,7 @@ }, { "cell_type": "markdown", - "id": "e0ba2d42", + "id": "62c964e3", "metadata": { "editable": true }, @@ -2165,7 +1889,7 @@ }, { "cell_type": "markdown", - "id": "59a5a47a", + "id": "64ba4c70", "metadata": { "editable": true }, @@ -2177,7 +1901,7 @@ }, { "cell_type": "markdown", - "id": "204ea252", + "id": "66c11135", "metadata": { "editable": true }, @@ -2194,7 +1918,7 @@ }, { "cell_type": "markdown", - "id": "cf69a239", + "id": "0f47b20a", "metadata": { "editable": true }, @@ -2209,7 +1933,7 @@ }, { "cell_type": "markdown", - "id": "634b25c8", + "id": "bda56156", "metadata": { "editable": true }, @@ -2227,7 +1951,7 @@ }, { "cell_type": "markdown", - "id": "c3a9df64", + "id": "1330fab9", "metadata": { "editable": true }, @@ -2239,7 +1963,7 @@ }, { "cell_type": "markdown", - "id": "419f4ca1", + "id": "ae474dfb", "metadata": { "editable": true }, @@ -2257,7 +1981,7 @@ }, { "cell_type": "markdown", - "id": "af3386ac", + "id": "b6cb6fed", "metadata": { "editable": true }, @@ -2270,7 +1994,7 @@ }, { "cell_type": "markdown", - "id": "e254da43", + "id": "2f8f9b4e", "metadata": { "editable": true }, @@ -2282,7 +2006,7 @@ }, { "cell_type": "markdown", - "id": "96ebff08", + "id": "18e74238", "metadata": { "editable": true }, @@ -2300,7 +2024,7 @@ }, { "cell_type": "markdown", - "id": "e482ce9c", + "id": "d10df3e7", "metadata": { "editable": true }, @@ -2318,7 +2042,7 @@ }, { "cell_type": "markdown", - "id": "a5002fb2", + "id": "da21a316", "metadata": { "editable": true }, @@ -2328,7 +2052,7 @@ }, { "cell_type": "markdown", - "id": "95c66242", + "id": "76938a28", "metadata": { "editable": true }, @@ -2346,7 +2070,7 @@ }, { "cell_type": "markdown", - "id": "9590e411", + "id": "65434967", "metadata": { "editable": true }, @@ -2365,7 +2089,7 @@ }, { "cell_type": "markdown", - "id": "8c4ff6f9", + "id": "31d4f5aa", "metadata": { "editable": true }, @@ -2378,7 +2102,7 @@ }, { "cell_type": "markdown", - "id": "4b6ffa92", + "id": "114030e5", "metadata": { "editable": true }, @@ -2396,7 +2120,7 @@ }, { "cell_type": "markdown", - "id": "eb180d8b", + "id": "a93aec4e", "metadata": { "editable": true }, @@ -2407,7 +2131,7 @@ }, { "cell_type": "markdown", - "id": "f1d0bda0", + "id": "7c85562d", "metadata": { "editable": true }, @@ -2426,7 +2150,7 @@ }, { "cell_type": "markdown", - "id": "99837971", + "id": "1152ea5e", "metadata": { "editable": true }, @@ -2444,7 +2168,7 @@ }, { "cell_type": "markdown", - "id": "16029266", + "id": "4f3d4b33", "metadata": { "editable": true }, @@ -2457,7 +2181,7 @@ }, { "cell_type": "markdown", - "id": "cc602c6c", + "id": "4c1ac54e", "metadata": { "editable": true }, @@ -2477,7 +2201,7 @@ }, { "cell_type": "markdown", - "id": "2b825937", + "id": "5c4a861f", "metadata": { "editable": true }, @@ -2510,7 +2234,7 @@ }, { "cell_type": "markdown", - "id": "8ef00888", + "id": "276b271b", "metadata": { "editable": true }, @@ -2522,7 +2246,7 @@ }, { "cell_type": "markdown", - "id": "2617b084", + "id": "63a5b8f1", "metadata": { "editable": true }, @@ -2541,7 +2265,7 @@ }, { "cell_type": "markdown", - "id": "c42c607f", + "id": "316b8c32", "metadata": { "editable": true }, @@ -2555,7 +2279,7 @@ }, { "cell_type": "markdown", - "id": "ca6286ac", + "id": "34ba90c8", "metadata": { "editable": true }, @@ -2578,7 +2302,7 @@ }, { "cell_type": "markdown", - "id": "cb2c654a", + "id": "3019fcaf", "metadata": { "editable": true }, @@ -2597,7 +2321,7 @@ }, { "cell_type": "markdown", - "id": "01494130", + "id": "389ff36b", "metadata": { "editable": true }, @@ -2609,7 +2333,7 @@ }, { "cell_type": "markdown", - "id": "8d4f8bee", + "id": "ee9b399a", "metadata": { "editable": true }, @@ -2619,7 +2343,7 @@ }, { "cell_type": "markdown", - "id": "4d9c8595", + "id": "36f98b26", "metadata": { "editable": true }, @@ -2631,7 +2355,7 @@ }, { "cell_type": "markdown", - "id": "6a5663c8", + "id": "cb7b8839", "metadata": { "editable": true }, @@ -2648,13 +2372,10 @@ { "cell_type": "code", "execution_count": 12, - "id": "2f4f91b8", + "id": "db8d28b5", "metadata": { "collapsed": false, - "editable": true, - "jupyter": { - "outputs_hidden": false - } + "editable": true }, "outputs": [], "source": [ @@ -2732,25 +2453,7 @@ ] } ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.9.15" - } - }, + "metadata": {}, "nbformat": 4, "nbformat_minor": 5 } diff --git a/doc/src/week40/week40.do.txt b/doc/src/week40/week40.do.txt index e2e3db263..917bfc307 100644 --- a/doc/src/week40/week40.do.txt +++ b/doc/src/week40/week40.do.txt @@ -10,8 +10,8 @@ DATE: September 29-October 3, 2025 o Logistic regression and gradient descent, examples on how to code #o Automatic differentiation and gradient descent, examples using Logistic regression o Start with the basics of Neural Networks, setting up the basic steps, from the simple perceptron model to the multi-layer perceptron model -# o "Video of lecture":"https://youtu.be/jdJoOrCIdII" -# o Whiteboard notes at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2024/NotesSeptember30.pdf" +o Video of lecture at URL:"https://youtu.be/MS3Tv8FVArs" +o Whiteboard notes at URL:"https://github.com/CompPhysics/MachineLearning/blob/master/doc/HandWrittenNotes/2025/FYSSTKweek40.pdf" !eblock !split