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# Strange way of testing for vortex...
# #ifdef NON_VORTEX
TITLE: Overview of course material: Computational Physics
TOC: off
# #else
TITLE: Overview of course material: Data Analysis and Machine Learning
AUTHOR: "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/" at Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University, USA & Department of Physics (office FV308), University of Oslo, Norway
# #endif
<%
pub_url = 'https://compphysics.github.io/MachineLearning/doc/pub'
published = ['Intro2Course', 'Introduction', 'How2ReadData', 'Linalg', 'Statistics', 'Regression', 'Splines', 'LogReg', 'NeuralNet', 'DimRed', 'DecisionTrees', 'svm', 'BM', 'Recurrent', 'Autoencoders', 'Reinforce', 'odenn', 'Bayesian', 'summary',]
chapters = {
'Intro2Course': 'Basic introduction to the course with schedule etc',
'Introduction': 'Introduction to Data Analysis and Machine Learning',
'How2ReadData': 'Getting started with Machine Learning with simple Examples',
'Linalg': 'Review of central linear algebra elements',
'Statistics': 'Monte Carlo methods and elements of probability theory',
'Regression': 'Regression Methods',
'Splines': 'Gradient methods and Minimization Algorithms',
'LogReg': 'Logistic Regression',
'NeuralNet': 'Neural Networks',
'DimRed': 'Reduction of dimensionality',
'DecisionTrees': 'Decision Trees and Random Forests',
'svm': 'Support Vector Machines',
'BM': 'Unsupervised Learning, Boltzmann Machines',
'Recurrent': 'Recurrent Neural Networks',
'Autoencoders': 'Autoencoders',
'Reinforce': 'Reinforcement Learning',
'odenn': 'Solving ordinary and Partial Differential Equations and Eigenvalue Problems with Neural Networks',
'Bayesian': 'Elements of Bayesian theory and Bayesian Neural Networks',
'summary': 'Summary',
}
%>
<%def name="text_types(name)">
* LaTeX PDF:
* For printing:
* "Standard one-page format": "${pub_url}/${name}/pdf/${name}-minted.pdf"
* HTML:
* "Plain html": "${pub_url}/${name}/html/${name}.html"
* "Bootstrap slide style, easy for reading on mobile devices": "${pub_url}/${name}/html/${name}-bs.html"
* Jupyter notebook:
* "ipynb file": "${pub_url}/${name}/ipynb/${name}.ipynb"
</%def>
<%def name="slide_types(name)">
</%def>
The teaching material is produced in various formats for printing and on-screen reading.
!split
!bwarning
The PDF files are based on LaTeX and have seldom technical
failures that cannot be easily corrected.
The HTML-based files, called ``HTML'' and ``ipynb'' below, apply MathJax
for rendering LaTeX formulas and sometimes this technology gives rise
to unexpected failures (e.g.,
incorrect rendering in a web page despite correct LaTeX syntax in the
formula). Consult the corresponding PDF
files if you find missing or incorrectly rendered
formulas in HTML or ipython notebook files.
!ewarning
% for ch in published:
===== ${chapters[ch]} =====
${text_types(ch)}
% endfor
!split
===== Python and Scikit Learn, a short guide =====
* HTML format only:
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/ComputationalPhysics/doc/pub/learningpython/html/learningpython-bs.html"
!split
===== Teach yourself C++ =====
* HTML format only:
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/ComputationalPhysics/doc/pub/learningcpp/html/learningcpp-bs.html"
!split
===== Projects and Exercises Fall 2018 =====
=== First homework set, week 35 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/hw1/pdf/hw1.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/hw1/pdf/hw1.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/hw1/html/hw1.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2018/hw1/html/hw1-bs.html"
=== Second homework set, week 36 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/hw2/pdf/hw2.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/hw2/pdf/hw2.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/hw2/html/hw2.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2018/hw2/html/hw2-bs.html"
=== Project 1, Deadline October 1 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/pdf/Project1.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/pdf/Project1.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/html/Project1.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/html/Project1-bs.html"
=== Project 2, Deadline November 5 ===
* LaTeX and PDF:
* "Latex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project2/pdf/Project2.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project2/pdf/Project2.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project2/html/Project2.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project2/html/Project2-bs.html"
=== Project 3, Deadline December 14 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project3/pdf/Project3.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project3/pdf/Project3.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project3/html/Project3.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2018/Project3/html/Project3-bs.html"
=== Course content ===
Probability theory and statistical methods play a central role in science. Nowadays we are
surrounded by huge amounts of data. For example, there are about one trillion web pages; more than one
hour of video is uploaded to YouTube every second, amounting to 10 years of content every
day; the genomes of 1000s of people, each of which has a length of $3.8\times 10^9$ base pairs, have
been sequenced by various labs and so on.
This deluge of data calls for automated methods of data analysis,
which is exactly what machine
learning provides. In this course the approach is to define machine learning as a set of methods that can
automatically detect patterns in data, and then use the uncovered patterns to predict future
data, or to perform other kinds of decision making under uncertainty. Since many of these problems can be studied using
tools of probability theory, the aim of this course is to expose you to central methods in probability theory linked with machine learning.
This course covers thus topics like Monte Carlo methods and Markov chains, Bayesian statistics, error estimates, various linear methods, optimization of data and error analysis and central algorithms in machine learning.
The course has several numerical projects and numerical exercises that are meant to illustrate the theory.
===== Learning outcomes =====
The course introduces a variety of central algorithms and methods
essential for studies of data analysis and machine learning. The course is project based and through the various projects, normally three, the students will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. The students will learn to develop and structure large codes for studying these systems, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
* Learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;
* Be capable of extending the acquired knowledge to other systems and cases;
* Have an understanding of central algorithms used in data analysis and machine learning;
* Have a basic knowledge of Bayesian statistics and learning and common distributions;
* Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications, from numerical integration to simulation of stock markets;
* Understand linear methods for regression and classification;
* Learn about neural network, genetic algorithms and Boltzmann machines;
* Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.
===== Prerequisites =====
Basic knowledge in programming and numerics. Required courses are the equivalents to the University of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one of the corresponding computing and programming courses INF1000/INF1110 or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1xxx.
===== The course has two central parts =====
o Statistical analysis and optimization of data
o Machine learning
=== Statistical analysis and optimization of data ===
The following topics will be covered
* Basic concepts, expectation values, variance, covariance, correlation functions and errors;
* Review of linear algebra methods;
* Splines and conjugate gradient methods for data optimization;
* Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
* Central elements of Bayesian statistics and modeling;
* Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm, ergodicity;
* Linear methods for regression and classification;
* Estimation of errors using blocking, bootstrapping and jackknife methods;
* Practical optimization using Singular-value decomposition and least squares for parameterizing data.
=== Machine learning ===
The following topics will be covered
* Gaussian and Dirichlet processes;
* Decision trees and nearest neighbors;
* Support vector machines;
* Boltzmann machines;
* Neural networks;
* Genetic algorithms.
All the above topics will be supported by examples, hands-on exercises and project work.
===== Recommended textbooks =====
* "Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer":"https://www.springer.com/gp/book/9780387848570"
* "Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly":"http://shop.oreilly.com/product/0636920052289.do"
===== "Other textbooks":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Textbooks" =====
_General learning book on statistical analysis_:
* Christian Robert and George Casella, Monte Carlo Statistical Methods, Springer
* Peter Hoff, A first course in Bayesian statistical models, Springer
_General Machine Learning Books_:
* Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
* Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer
* David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press
* Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning, Springer
* David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press
!split
===== Teaching schedule Fall 2018 =====
Acronyms for textbooks and references to chapter
* HTF: "Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer":"https://www.springer.com/gp/book/9780387848570"
* AG: "Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly":"http://shop.oreilly.com/product/0636920052289.do"
|----------------------------------------------------------------------------------------------------------------------------|
| Week and days | Topics to be covered | Projects, exercises and deadlines | Reading assignments| Lab activities |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 34| Introduction and regression analysis | Exercises TBD | HTF chapters 1-3 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html"| No lab first week |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 35 | Regression analysis | Exercises TBD | HTF chapter 3 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html"| Introduction to Git, GitHub and Python software, Python technicalities and work on exercises|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 36 | Regression analysis and nearest neighbors| Exercises TBD | HTF chapters 3, 4 and 13 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html" | Work on exercises|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 37 | Classification and logistic regression | "Presentation of Project 1, deadline October 1":"https://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/html/Project1-bs.html" | HTF chapter 4 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html" | Work on project 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 38 | Optimization methods | Exercises and project 1 | HTF chapter 5 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html" | Work on project 1, deadline October 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 39 | Logistic regression and optimization | Project 1 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-bs.html" | Work on Project 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 40 | Neural Networks | Presentation of project 2, deadline November 12 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Deadline project 1, October 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 41 | Neural Networks | Project 2 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 42 | Neural Networks | Project 2 | HTF chapter 11 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 43 | Dimensionality reduction and support vector machines | Project 2 | HTF chapters 3 and 12 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 44 | SVM and tree and forest models | Project 2 | HTF chapter 9 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 45 | SVM and tree and forest models | Presentation and discussion of project 3 | HTF chapter 14 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html" | Deadline project 2 November 12 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 46 | Bayesian statitics | Project 3, deadline December 14 | | Work on project 3 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 47 | Unsupervised learning, Boltzmann machines | Project 3 | | Work on project 3 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 48 | Summary of course and final workshop | Project 3 | Lecture notes | Final workshop with presentation of project 3 (TBA)|
|----------------------------------------------------------------------------------------------------------------------------|
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# Strange way of testing for vortex...
# #ifdef NON_VORTEX
TITLE: Overview of course material: Computational Physics
TOC: off
# #else
TITLE: Tentative schedule for FYS-STK3155/4155
AUTHOR: "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/" at Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University, USA & Department of Physics, University of Oslo, Norway
# #endif
!split
===== Teaching schedule Fall 2018 =====
Acronyms for textbooks and references to chapter
* HTF: "Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer":"https://www.springer.com/gp/book/9780387848570"
* AG: "Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly":"http://shop.oreilly.com/product/0636920052289.do"
Lectures are Thursday and Fridays 1215pm-2pm. The computer lab is (projects and exercises) is on Wednesdays. Depending on how many people register for the course, we may set up four lab groups as follows
* Group 1: Wednesdays 10am-12pm
* Group 2: Wednesdays 12pm-2pm
* Group 3: Wednesdays 2pm-4pm
* Group 4: Wednesdays 4pm-6pm
|----------------------------------------------------------------------------------------------------------------------------|
| Week | Topics to be covered | Projects, exercises and deadlines | Reading assignments| Lab activities |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 34| Introduction and regression analysis | Exercises TBD | HTF chapters 1-3 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html"| No lab first week |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 35 | Regression analysis | Exercises TBD | HTF chapter 3 and and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html"| Introduction to Git, GitHub and Python software, Python technicalities and work on exercises|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 36 | Regression analysis and nearest neighbors| Exercises TBD | HTF chapters 3, 4 and 13 and and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html" | Work on exercises|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 37 | Classification and logistic regression | "Presentation of Project 1, deadline October 1":"https://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/html/Project1-bs.html" | HTF chapter 4 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html" | Work on project 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 38 | Optimization methods | Exercises and project 1 | HTF chapter 5 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html" | Work on project 1, deadline October 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 39 | Statistics, Bayesian statistics | Project 1 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-bs.html" | Work on Project 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 40 | Statistics, Monte Carlo and Randow walks | Presentation of project 2, deadline November 5 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Deadline project 1, October 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 41 | Statistics, Monte Carlo, Gibbs and Metropolis sampling | Project 2 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 42 | Neural networks | Project 2 | HTF chapter 11 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 43 | Neural networks | Project 2 | HTF chapter 11 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 44 | Neural networks | Project 2 | HTF chapter 11 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 45 | Support Vector Machines | Presentation and discussion of project 3 | HTF chapter 12 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/svm/html/svm-bs.html" | Deadline project 2 November 5 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 46 | Decision trees | Project 3 | HTF chapter 9 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/html/DecisionTrees-bs.html" | Work on project 3 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 47 | Unsupervised learning, Boltzmann machines | Project 3 | HTF chapter 14 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html" | Work on project 3 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 48 | Unsupervised learning, summary of course and final workshop | Project 3 | Lecture notes | Final workshop with presentation of project 3 |
|----------------------------------------------------------------------------------------------------------------------------|
doc/web/tmp_mako__course.do.txt
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# Strange way of testing for vortex...
TITLE: Overview of course material: Data Analysis and Machine Learning
AUTHOR: "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/" at Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University, USA & Department of Physics (office FV308), University of Oslo, Norway
The teaching material is produced in various formats for printing and on-screen reading.
!split
!bwarning
The PDF files are based on LaTeX and have seldom technical
failures that cannot be easily corrected.
The HTML-based files, called ``HTML'' and ``ipynb'' below, apply MathJax
for rendering LaTeX formulas and sometimes this technology gives rise
to unexpected failures (e.g.,
incorrect rendering in a web page despite correct LaTeX syntax in the
formula). Consult the corresponding PDF
files if you find missing or incorrectly rendered
formulas in HTML or ipython notebook files.
!ewarning
===== Basic introduction to the course with schedule etc =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/pdf/Intro2Course-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/html/Intro2Course.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/html/Intro2Course-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Intro2Course/ipynb/Intro2Course.ipynb"
===== Introduction to Data Analysis and Machine Learning =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Introduction/pdf/Introduction-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Introduction/html/Introduction.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Introduction/html/Introduction-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Introduction/ipynb/Introduction.ipynb"
===== Getting started with Machine Learning with simple Examples =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/pdf/How2ReadData-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/html/How2ReadData.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/html/How2ReadData-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/How2ReadData/ipynb/How2ReadData.ipynb"
===== Review of central linear algebra elements =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Linalg/pdf/Linalg-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Linalg/html/Linalg.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Linalg/html/Linalg-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Linalg/ipynb/Linalg.ipynb"
===== Monte Carlo methods and elements of probability theory =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Statistics/pdf/Statistics-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Statistics/ipynb/Statistics.ipynb"
===== Regression Methods =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Regression/pdf/Regression-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Regression/ipynb/Regression.ipynb"
===== Gradient methods and Minimization Algorithms =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Splines/pdf/Splines-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Splines/ipynb/Splines.ipynb"
===== Logistic Regression =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/LogReg/pdf/LogReg-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/LogReg/html/LogReg-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/LogReg/ipynb/LogReg.ipynb"
===== Neural Networks =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/pdf/NeuralNet-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/ipynb/NeuralNet.ipynb"
===== Reduction of dimensionality =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/DimRed/pdf/DimRed-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/DimRed/html/DimRed-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/DimRed/ipynb/DimRed.ipynb"
===== Decision Trees and Random Forests =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/pdf/DecisionTrees-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/html/DecisionTrees.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/html/DecisionTrees-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/ipynb/DecisionTrees.ipynb"
===== Support Vector Machines =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/svm/pdf/svm-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/svm/html/svm.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/svm/html/svm-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/svm/ipynb/svm.ipynb"
===== Unsupervised Learning, Boltzmann Machines =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/BM/pdf/BM-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/BM/ipynb/BM.ipynb"
===== Recurrent Neural Networks =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/pdf/Recurrent-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/html/Recurrent.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/html/Recurrent-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Recurrent/ipynb/Recurrent.ipynb"
===== Autoencoders =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/pdf/Autoencoders-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/html/Autoencoders.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/html/Autoencoders-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Autoencoders/ipynb/Autoencoders.ipynb"
===== Reinforcement Learning =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/pdf/Reinforce-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/html/Reinforce.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/html/Reinforce-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Reinforce/ipynb/Reinforce.ipynb"
===== Solving ordinary and Partial Differential Equations and Eigenvalue Problems with Neural Networks =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/odenn/pdf/odenn-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/odenn/html/odenn-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/odenn/ipynb/odenn.ipynb"
===== Elements of Bayesian theory and Bayesian Neural Networks =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/pdf/Bayesian-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/ipynb/Bayesian.ipynb"
===== Summary =====
* LaTeX PDF:
* For printing:
* "Standard one-page format": "https://compphysics.github.io/MachineLearning/doc/pub/summary/pdf/summary-minted.pdf"
* HTML:
* "Plain html": "https://compphysics.github.io/MachineLearning/doc/pub/summary/html/summary.html"
* "Bootstrap slide style, easy for reading on mobile devices": "https://compphysics.github.io/MachineLearning/doc/pub/summary/html/summary-bs.html"
* Jupyter notebook:
* "ipynb file": "https://compphysics.github.io/MachineLearning/doc/pub/summary/ipynb/summary.ipynb"
!split
===== Python and Scikit Learn, a short guide =====
* HTML format only:
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/ComputationalPhysics/doc/pub/learningpython/html/learningpython-bs.html"
!split
===== Teach yourself C++ =====
* HTML format only:
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/ComputationalPhysics/doc/pub/learningcpp/html/learningcpp-bs.html"
!split
===== Projects and Exercises Fall 2019 =====
=== First homework set, week 35 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw1/pdf/hw1.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw1/pdf/hw1.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw1/html/hw1.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw1/html/hw1-bs.html"
=== Second homework set, week 36 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw2/pdf/hw2.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw2/pdf/hw2.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw2/html/hw2.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw2/html/hw2-bs.html"
=== Project 1, Deadline September 30 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project1/pdf/Project1.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project1/pdf/Project1.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project1/html/Project1.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project1/html/Project1-bs.html"
=== Project 2, Deadline November 4 ===
* LaTeX and PDF:
* "Latex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project2/pdf/Project2.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project2/pdf/Project2.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project2/html/Project2.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project2/html/Project2-bs.html"
=== Project 3, Deadline December 13 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project3/pdf/Project3.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project3/pdf/Project3.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project3/html/Project3.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project3/html/Project3-bs.html"
=== Course content ===
Probability theory and statistical methods play a central role in science. Nowadays we are
surrounded by huge amounts of data. For example, there are about one trillion web pages; more than one
hour of video is uploaded to YouTube every second, amounting to 10 years of content every
day; the genomes of 1000s of people, each of which has a length of $3.8\times 10^9$ base pairs, have
been sequenced by various labs and so on.
This deluge of data calls for automated methods of data analysis,
which is exactly what machine
learning provides. In this course the approach is to define machine learning as a set of methods that can
automatically detect patterns in data, and then use the uncovered patterns to predict future
data, or to perform other kinds of decision making under uncertainty. Since many of these problems can be studied using
tools of probability theory, the aim of this course is to expose you to central methods in probability theory linked with machine learning.
This course covers thus topics like Monte Carlo methods and Markov chains, Bayesian statistics, error estimates, various linear methods, optimization of data and error analysis and central algorithms in machine learning.
The course has several numerical projects and numerical exercises that are meant to illustrate the theory.
===== Learning outcomes =====
The course introduces a variety of central algorithms and methods
essential for studies of data analysis and machine learning. The course is project based and through the various projects, normally three, the students will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. The students will learn to develop and structure large codes for studying these systems, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
* Learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;
* Be capable of extending the acquired knowledge to other systems and cases;
* Have an understanding of central algorithms used in data analysis and machine learning;
* Have a basic knowledge of Bayesian statistics and learning and common distributions;
* Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications, from numerical integration to simulation of stock markets;
* Understand linear methods for regression and classification;
* Learn about neural network, genetic algorithms and Boltzmann machines;
* Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.
===== Prerequisites =====
Basic knowledge in programming and numerics. Required courses are the equivalents to the University of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one of the corresponding computing and programming courses INF1000/INF1110 or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1xxx.
===== The course has two central parts =====
o Statistical analysis and optimization of data
o Machine learning
=== Statistical analysis and optimization of data ===
The following topics will be covered
* Basic concepts, expectation values, variance, covariance, correlation functions and errors;
* Review of linear algebra methods;
* Splines and conjugate gradient methods for data optimization;
* Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
* Central elements of Bayesian statistics and modeling;
* Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm, ergodicity;
* Linear methods for regression and classification;
* Estimation of errors using blocking, bootstrapping and jackknife methods;
* Practical optimization using Singular-value decomposition and least squares for parameterizing data.
=== Machine learning ===
The following topics will be covered
* Gaussian and Dirichlet processes;
* Decision trees and nearest neighbors;
* Support vector machines;
* Boltzmann machines;
* Neural networks;
* Genetic algorithms.
All the above topics will be supported by examples, hands-on exercises and project work.
===== Recommended textbooks =====
* "Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer":"https://www.springer.com/gp/book/9780387848570"
* "Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly":"http://shop.oreilly.com/product/0636920052289.do"
===== "Other textbooks":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Textbooks" =====
_General learning book on statistical analysis_:
* Christian Robert and George Casella, Monte Carlo Statistical Methods, Springer
* Peter Hoff, A first course in Bayesian statistical models, Springer
_General Machine Learning Books_:
* Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
* Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer
* David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press
* Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning, Springer
* David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press
!split
===== Teaching schedule Fall 2019 =====
Acronyms for textbooks and references to chapter
* HTF: "Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer":"https://www.springer.com/gp/book/9780387848570"
* AG: "Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly":"http://shop.oreilly.com/product/0636920052289.do"
|----------------------------------------------------------------------------------------------------------------------------|
| Week and days | Topics to be covered | Projects, exercises and deadlines | Reading assignments| Lab activities |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 34| Introduction and regression analysis | Exercises TBD | HTF chapters 1-3 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html"| No lab first week |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 35 | Regression analysis | Exercises TBD | HTF chapter 3 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html"| Introduction to Git, GitHub and Python software, Python technicalities and work on exercises|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 36 | Regression analysis and nearest neighbors| Exercises TBD | HTF chapters 3, 4 and 13 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html" | Work on exercises|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 37 | Classification and logistic regression | "Presentation of Project 1, deadline October 1":"https://compphysics.github.io/MachineLearning/doc/Projects/2019/Project1/html/Project1-bs.html" | HTF chapter 4 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html" | Work on project 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 38 | Optimization methods | Exercises and project 1 | HTF chapter 5 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html" | Work on project 1, deadline October 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 39 | Logistic regression and optimization | Project 1 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-bs.html" | Work on Project 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 40 | Neural Networks | Presentation of project 2, deadline November 12 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Deadline project 1, October 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 41 | Neural Networks | Project 2 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 42 | Neural Networks | Project 2 | HTF chapter 11 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 43 | Dimensionality reduction and support vector machines | Project 2 | HTF chapters 3 and 12 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 44 | SVM and tree and forest models | Project 2 | HTF chapter 9 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 45 | SVM and tree and forest models | Presentation and discussion of project 3 | HTF chapter 14 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html" | Deadline project 2 November 12 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 46 | Bayesian statitics | Project 3, deadline December 14 | | Work on project 3 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 47 | Unsupervised learning, Boltzmann machines | Project 3 | | Work on project 3 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 48 | Summary of course and final workshop | Project 3 | Lecture notes | Final workshop with presentation of project 3 (TBA)|
|----------------------------------------------------------------------------------------------------------------------------|
doc/web/tmp_preprocess__course.do.txt
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# Strange way of testing for vortex...
TITLE: Overview of course material: Data Analysis and Machine Learning
AUTHOR: "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/" at Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University, USA & Department of Physics (office FV308), University of Oslo, Norway
<%
pub_url = 'https://compphysics.github.io/MachineLearning/doc/pub'
published = ['Intro2Course', 'Introduction', 'How2ReadData', 'Linalg', 'Statistics', 'Regression', 'Splines', 'LogReg', 'NeuralNet', 'DimRed', 'DecisionTrees', 'svm', 'BM', 'Recurrent', 'Autoencoders', 'Reinforce', 'odenn', 'Bayesian', 'summary',]
chapters = {
'Intro2Course': 'Basic introduction to the course with schedule etc',
'Introduction': 'Introduction to Data Analysis and Machine Learning',
'How2ReadData': 'Getting started with Machine Learning with simple Examples',
'Linalg': 'Review of central linear algebra elements',
'Statistics': 'Monte Carlo methods and elements of probability theory',
'Regression': 'Regression Methods',
'Splines': 'Gradient methods and Minimization Algorithms',
'LogReg': 'Logistic Regression',
'NeuralNet': 'Neural Networks',
'DimRed': 'Reduction of dimensionality',
'DecisionTrees': 'Decision Trees and Random Forests',
'svm': 'Support Vector Machines',
'BM': 'Unsupervised Learning, Boltzmann Machines',
'Recurrent': 'Recurrent Neural Networks',
'Autoencoders': 'Autoencoders',
'Reinforce': 'Reinforcement Learning',
'odenn': 'Solving ordinary and Partial Differential Equations and Eigenvalue Problems with Neural Networks',
'Bayesian': 'Elements of Bayesian theory and Bayesian Neural Networks',
'summary': 'Summary',
}
%>
<%def name="text_types(name)">
* LaTeX PDF:
* For printing:
* "Standard one-page format": "${pub_url}/${name}/pdf/${name}-minted.pdf"
* HTML:
* "Plain html": "${pub_url}/${name}/html/${name}.html"
* "Bootstrap slide style, easy for reading on mobile devices": "${pub_url}/${name}/html/${name}-bs.html"
* Jupyter notebook:
* "ipynb file": "${pub_url}/${name}/ipynb/${name}.ipynb"
</%def>
<%def name="slide_types(name)">
</%def>
The teaching material is produced in various formats for printing and on-screen reading.
!split
!bwarning
The PDF files are based on LaTeX and have seldom technical
failures that cannot be easily corrected.
The HTML-based files, called ``HTML'' and ``ipynb'' below, apply MathJax
for rendering LaTeX formulas and sometimes this technology gives rise
to unexpected failures (e.g.,
incorrect rendering in a web page despite correct LaTeX syntax in the
formula). Consult the corresponding PDF
files if you find missing or incorrectly rendered
formulas in HTML or ipython notebook files.
!ewarning
% for ch in published:
===== ${chapters[ch]} =====
${text_types(ch)}
% endfor
!split
===== Python and Scikit Learn, a short guide =====
* HTML format only:
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/ComputationalPhysics/doc/pub/learningpython/html/learningpython-bs.html"
!split
===== Teach yourself C++ =====
* HTML format only:
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/ComputationalPhysics/doc/pub/learningcpp/html/learningcpp-bs.html"
!split
===== Projects and Exercises Fall 2019 =====
=== First homework set, week 35 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw1/pdf/hw1.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw1/pdf/hw1.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw1/html/hw1.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw1/html/hw1-bs.html"
=== Second homework set, week 36 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw2/pdf/hw2.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw2/pdf/hw2.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw2/html/hw2.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2019/hw2/html/hw2-bs.html"
=== Project 1, Deadline September 30 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project1/pdf/Project1.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project1/pdf/Project1.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project1/html/Project1.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project1/html/Project1-bs.html"
=== Project 2, Deadline November 4 ===
* LaTeX and PDF:
* "Latex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project2/pdf/Project2.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project2/pdf/Project2.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project2/html/Project2.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project2/html/Project2-bs.html"
=== Project 3, Deadline December 13 ===
* LaTeX and PDF:
* "LaTex file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project3/pdf/Project3.tex"
* "PDF file":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project3/pdf/Project3.pdf"
* HTML:
* "Plain html":"http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project3/html/Project3.html"
* "Bootstrap slide style, easy for reading on mobile devices": "http://compphysics.github.io/MachineLearning/doc/Projects/2019/Project3/html/Project3-bs.html"
=== Course content ===
Probability theory and statistical methods play a central role in science. Nowadays we are
surrounded by huge amounts of data. For example, there are about one trillion web pages; more than one
hour of video is uploaded to YouTube every second, amounting to 10 years of content every
day; the genomes of 1000s of people, each of which has a length of $3.8\times 10^9$ base pairs, have
been sequenced by various labs and so on.
This deluge of data calls for automated methods of data analysis,
which is exactly what machine
learning provides. In this course the approach is to define machine learning as a set of methods that can
automatically detect patterns in data, and then use the uncovered patterns to predict future
data, or to perform other kinds of decision making under uncertainty. Since many of these problems can be studied using
tools of probability theory, the aim of this course is to expose you to central methods in probability theory linked with machine learning.
This course covers thus topics like Monte Carlo methods and Markov chains, Bayesian statistics, error estimates, various linear methods, optimization of data and error analysis and central algorithms in machine learning.
The course has several numerical projects and numerical exercises that are meant to illustrate the theory.
===== Learning outcomes =====
The course introduces a variety of central algorithms and methods
essential for studies of data analysis and machine learning. The course is project based and through the various projects, normally three, the students will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. The students will learn to develop and structure large codes for studying these systems, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
* Learn about basic data analysis, Bayesian statistics, Monte Carlo methods, data optimization and machine learning;
* Be capable of extending the acquired knowledge to other systems and cases;
* Have an understanding of central algorithms used in data analysis and machine learning;
* Have a basic knowledge of Bayesian statistics and learning and common distributions;
* Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications, from numerical integration to simulation of stock markets;
* Understand linear methods for regression and classification;
* Learn about neural network, genetic algorithms and Boltzmann machines;
* Work on numerical projects to illustrate the theory. The projects play a central role and students are expected to know modern programming languages like Python or C++.
===== Prerequisites =====
Basic knowledge in programming and numerics. Required courses are the equivalents to the University of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one of the corresponding computing and programming courses INF1000/INF1110 or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1xxx.
===== The course has two central parts =====
o Statistical analysis and optimization of data
o Machine learning
=== Statistical analysis and optimization of data ===
The following topics will be covered
* Basic concepts, expectation values, variance, covariance, correlation functions and errors;
* Review of linear algebra methods;
* Splines and conjugate gradient methods for data optimization;
* Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
* Central elements of Bayesian statistics and modeling;
* Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm, ergodicity;
* Linear methods for regression and classification;
* Estimation of errors using blocking, bootstrapping and jackknife methods;
* Practical optimization using Singular-value decomposition and least squares for parameterizing data.
=== Machine learning ===
The following topics will be covered
* Gaussian and Dirichlet processes;
* Decision trees and nearest neighbors;
* Support vector machines;
* Boltzmann machines;
* Neural networks;
* Genetic algorithms.
All the above topics will be supported by examples, hands-on exercises and project work.
===== Recommended textbooks =====
* "Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer":"https://www.springer.com/gp/book/9780387848570"
* "Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly":"http://shop.oreilly.com/product/0636920052289.do"
===== "Other textbooks":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Textbooks" =====
_General learning book on statistical analysis_:
* Christian Robert and George Casella, Monte Carlo Statistical Methods, Springer
* Peter Hoff, A first course in Bayesian statistical models, Springer
_General Machine Learning Books_:
* Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
* Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer
* David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press
* Trevor Hastie, Robert Tibshirani, and Jerome Friedman, The Elements of Statistical Learning, Springer
* David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press
!split
===== Teaching schedule Fall 2019 =====
Acronyms for textbooks and references to chapter
* HTF: "Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer":"https://www.springer.com/gp/book/9780387848570"
* AG: "Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly":"http://shop.oreilly.com/product/0636920052289.do"
|----------------------------------------------------------------------------------------------------------------------------|
| Week and days | Topics to be covered | Projects, exercises and deadlines | Reading assignments| Lab activities |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 34| Introduction and regression analysis | Exercises TBD | HTF chapters 1-3 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html"| No lab first week |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 35 | Regression analysis | Exercises TBD | HTF chapter 3 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html"| Introduction to Git, GitHub and Python software, Python technicalities and work on exercises|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 36 | Regression analysis and nearest neighbors| Exercises TBD | HTF chapters 3, 4 and 13 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html" | Work on exercises|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 37 | Classification and logistic regression | "Presentation of Project 1, deadline October 1":"https://compphysics.github.io/MachineLearning/doc/Projects/2019/Project1/html/Project1-bs.html" | HTF chapter 4 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html" | Work on project 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 38 | Optimization methods | Exercises and project 1 | HTF chapter 5 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html" | Work on project 1, deadline October 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 39 | Logistic regression and optimization | Project 1 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-bs.html" | Work on Project 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 40 | Neural Networks | Presentation of project 2, deadline November 12 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Deadline project 1, October 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 41 | Neural Networks | Project 2 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 42 | Neural Networks | Project 2 | HTF chapter 11 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 43 | Dimensionality reduction and support vector machines | Project 2 | HTF chapters 3 and 12 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 44 | SVM and tree and forest models | Project 2 | HTF chapter 9 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 45 | SVM and tree and forest models | Presentation and discussion of project 3 | HTF chapter 14 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html" | Deadline project 2 November 12 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 46 | Bayesian statitics | Project 3, deadline December 14 | | Work on project 3 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 47 | Unsupervised learning, Boltzmann machines | Project 3 | | Work on project 3 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 48 | Summary of course and final workshop | Project 3 | Lecture notes | Final workshop with presentation of project 3 (TBA)|
|----------------------------------------------------------------------------------------------------------------------------|
doc/web/tmp_preprocess__schedule.do.txt
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# Strange way of testing for vortex...
TITLE: Tentative schedule for FYS-STK3155/4155
AUTHOR: "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/" at Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University, USA & Department of Physics, University of Oslo, Norway
!split
===== Teaching schedule Fall 2018 =====
Acronyms for textbooks and references to chapter
* HTF: "Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer":"https://www.springer.com/gp/book/9780387848570"
* AG: "Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly":"http://shop.oreilly.com/product/0636920052289.do"
Lectures are Thursday and Fridays 1215pm-2pm. The computer lab is (projects and exercises) is on Wednesdays. Depending on how many people register for the course, we may set up four lab groups as follows
* Group 1: Wednesdays 10am-12pm
* Group 2: Wednesdays 12pm-2pm
* Group 3: Wednesdays 2pm-4pm
* Group 4: Wednesdays 4pm-6pm
|----------------------------------------------------------------------------------------------------------------------------|
| Week | Topics to be covered | Projects, exercises and deadlines | Reading assignments| Lab activities |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 34| Introduction and regression analysis | Exercises TBD | HTF chapters 1-3 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html"| No lab first week |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 35 | Regression analysis | Exercises TBD | HTF chapter 3 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html"| Introduction to Git, GitHub and Python software, Python technicalities and work on exercises|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 36 | Regression analysis and nearest neighbors| Exercises TBD | HTF chapters 3, 4 and 13 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html" | Work on exercises|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 37 | Classification and logistic regression | "Presentation of Project 1, deadline October 1":"https://compphysics.github.io/MachineLearning/doc/Projects/2018/Project1/html/Project1-bs.html" | HTF chapter 4 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html" | Work on project 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 38 | Optimization methods | Exercises and project 1 | HTF chapter 5 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Splines/html/Splines-bs.html" | Work on project 1, deadline October 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 39 | Statistics, Bayesian statistics | Project 1 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Bayesian/html/Bayesian-bs.html" | Work on Project 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 40 | Statistics, Monte Carlo and Randow walks | Presentation of project 2, deadline November 5 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Deadline project 1, October 1|
|----------------------------------------------------------------------------------------------------------------------------|
| Week 41 | Statistics, Monte Carlo, Gibbs and Metropolis sampling | Project 2 | "Lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Statistics/html/Statistics-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 42 | Neural networks | Project 2 | HTF chapter 11 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 43 | Neural networks | Project 2 | HTF chapter 11 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 44 | Neural networks | Project 2 | HTF chapter 11 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/NeuralNet/html/NeuralNet-bs.html" | Work on project 2 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 45 | Support Vector Machines | Presentation and discussion of project 3 | HTF chapter 12 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/svm/html/svm-bs.html" | Deadline project 2 November 5 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 46 | Decision trees | Project 3 | HTF chapter 9 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/DecisionTrees/html/DecisionTrees-bs.html" | Work on project 3 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 47 | Unsupervised learning, Boltzmann machines | Project 3 | HTF chapter 14 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/BM/html/BM-bs.html" | Work on project 3 |
|----------------------------------------------------------------------------------------------------------------------------|
| Week 48 | Unsupervised learning, summary of course and final workshop | Project 3 | Lecture notes | Final workshop with presentation of project 3 |
|----------------------------------------------------------------------------------------------------------------------------|