lars/FYS-STK4155
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

updating lectures

Browse Source
This commit is contained in:
mhjensen
2020-10-04 21:32:03 +02:00
parent 3cb9aca9ba
commit 21e3a669fe
157 changed files with 31586 additions and 58823 deletions
Download Patch File Download Diff File Expand all files Collapse all files
doc/src/LectureNotes/chapter1.ipynb
+1 -41
View File
@@ -4,21 +4,7 @@
"cell_type": "markdown",
"metadata": {},
"source": [
"<!-- dom:TITLE: Introduction to Applied Data Analysis and Machine Learning -->\n",
"# Introduction to Applied Data Analysis and Machine Learning\n",
"<!-- dom:AUTHOR: Morten Hjorth-Jensen at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University -->\n",
"<!-- Author: --> \n",
"**Morten Hjorth-Jensen**, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University\n",
"\n",
"Date: **Sep 19, 2020**\n",
"\n",
"Copyright 1999-2020, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Introduction\n",
"\n",
"During the last two decades there has been a swift and amazing\n",
"development of Machine Learning techniques and algorithms that impact\n",
@@ -167,8 +153,6 @@
"role when we develop a specific machine learning algorithm. \n",
"\n",
"\n",
"## Machine Learning, an extremely rich field\n",
"\n",
"Machine learning is an extremely rich field, in spite of its young\n",
"age. The increases we have seen during the last decades in\n",
"computational capabilities have been followed by developments of\n",
@@ -184,8 +168,6 @@
"code developers and contributors keeps increasing.\n",
"\n",
"\n",
"## A multidisciplinary approach\n",
"\n",
"Not all the\n",
"algorithms and methods can be given a rigorous mathematical\n",
"justification (for example decision trees and random forests), opening up thereby large rooms for experimenting and\n",
@@ -196,12 +178,6 @@
"of the algorithms and methods we will discuss.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Types of Machine Learning\n",
"\n",
"\n",
"The approaches to machine learning are many, but are often split into\n",
"two main categories. In *supervised learning* we know the answer to a\n",
"problem, and let the computer deduce the logic behind it. On the other\n",
@@ -221,27 +197,15 @@
"\n",
"* Clustering: Data are divided into groups with certain common traits, without knowing the different groups beforehand. It is thus a form of unsupervised learning.\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## Essential elements of ML\n",
"\n",
"The methods we cover have three main topics in common, irrespective of\n",
"whether we deal with supervised or unsupervised learning.\n",
"\n",
"* The first ingredient is normally our data set (which can be subdivided into training, validation and test data). Many find the most difficult part of using Machine Learning to be the set up of your data in a meaningful way. \n",
"\n",
"* The second item is a model which is normally a function of some parameters. The model reflects our knowledge of the system (or lack thereof). As an example, if we know that our data show a behavior similar to what would be predicted by a polynomial, fitting our data to a polynomial of some degree would then determin our model. \n",
"\n",
"* The last ingredient is a so-called **cost/loss** function (or error function) which allows us to present an estimate on how good our model is in reproducing the data it is supposed to train. \n",
"\n",
"\n",
"\n",
"\n",
"\n",
"\n",
"## An optimization/minimization problem\n",
"\n",
"At the heart of basically all Machine Learning algorithms we will encounter so-called minimization or optimization algorithms. A large family of such methods are so-called **gradient methods**.\n",
"\n",
"\n",
@@ -297,10 +261,6 @@
"could easily be many different models that fit the given data set *equally well*.\n",
"\n",
"\n",
"\n",
"## What is a good model? Can we define it?\n",
"\n",
"\n",
"The central question is this: what leads us to say that a model is correct or\n",
"optimal for a given data set? To make the model inference problem well posed, i.e.,\n",
"to guarantee that there is a unique optimal model for the given data, we need to\n",