"**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: **Jun 10, 2019**\n",
"Date: **Jul 22, 2019**\n",
"\n",
"Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license\n",
TITLE: Applied Data Analysis and Machine Learning: Introduction to the course
TITLE: Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities
AUTHOR: Morten Hjorth-Jensen {copyright, 1999-present|CC BY-NC} at Department of Physics, University of Oslo & Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
DATE: today
@@ -11,19 +11,9 @@ DATE: today
* Thursday: First lecture: Presentation of the course, aims and content
* Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra
* Friday: Linear regression
* Computer lab: Wednesday. First time: Tuesday August 27.
* Computer lab: Tuesday. First time: Tuesday August 27.
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===== Reading suggestions and exercises =====
The recommended textbooks
* 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"
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* HTF chapters 1-3 and "lecture notes":"https://compphysics.github.io/MachineLearning/doc/pub/Regression/html/Regression-bs.html"
* AG chapters 1 and 2
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===== Lectures and ComputerLab =====
@@ -34,7 +24,7 @@ The recommended textbooks
* Weekly exercises when not working on projects. You can hand in exercises if you want.
* First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.
* Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.
* Weekly plans and all other information are on the official webpage.
* No final exam, three projects that are graded and have to be approved.
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@@ -45,12 +35,11 @@ The recommended textbooks
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* Three compulsory projects. Electronic reports only using "devilry":"https://devilry.ifi.uio.no/" to hand in projects and "Git":"https://github.com/" for repository and all your material.
* Evaluation and grading: The three projects are graded and each counts 1/3 of the final mark. No final written or oral exam.
* The last project is organized as a workshop (duration approx 5-6 hours toward the end of the semester) where each group submits a proposal for a data sets to be analyzed with the different methods discussed during the course.
o Each group submits a proposal or works with suggested (by us) proposals for the project.
o Each group makes a poster and presents this to all other participants of the course
o Poster session where all participants can study and discuss the other proposals (the three best posters get additional score)
o For the last project Each group/participant submits a proposal or works with suggested (by us) proposals for the project.
o If possible, we would like to organize the last project as a workshop where each group makes a poster and presents this to all other participants of the course
o Poster session where all participants can study and discuss the other proposals.
o Based on feedback etc, each group finalizes the report and submits for grading.
* The computer lab (room FV329)consists of 16 Linux PCs, but many prefer own laptops. Python is the default programming language, but feel free to use C/C++ and/or Fortran. All source codes discussed during the lectures can be found at the webpage and "github address":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" of the course.
* Python is the default programming language, but feel free to use C/C++ and/or Fortran or other programmin languages. All source codes discussed during the lectures can be found at the webpage and "github address":"https://github.com/CompPhysics/MachineLearning/tree/master/doc/Programs" of the course.
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@@ -59,10 +48,11 @@ The recommended textbooks
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_Teachers :_
o "Hanna Svennevik":"https://www.mn.uio.no/ifi/english/?vrtx=person-view&uid=krisbhei"
o "Hanna Svennevik":"https://www.researchgate.net/profile/Hanna_Svennevik"
o "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/"
o "Lucas Charpentier":"https://www.mn.uio.no/fysikk/english/?vrtx=person-view&uid=bendiksa"
o "Øyvind Sigmundson Schøyen":"https://www.mn.uio.no/fysikk/english/?vrtx=person-view&uid=oyvinssc"
o "Lucas Charpentier":"https://no.linkedin.com/in/lucas-charpentier-176206171"
o "Stian Bilek":"https://www.researchgate.net/profile/Stian_Bilek"
@@ -82,9 +72,9 @@ o "Øyvind Sigmundson Schøyen":"https://www.mn.uio.no/fysikk/english/?vrtx=pers
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o Project 1: October 1 (graded with feedback)
o Project 2: November 5 (graded with feedback)
o Project 3: November 30, tentative (graded with feedback)
o Project 1: September 30 (graded with feedback)
o Project 2: November 4 (graded with feedback)
o Project 3: December 2 (graded with feedback)
Projects are handed in using devilry.ifi.uio.no. We use Github as repository for codes, benchmark calculations etc. Comments and feedback on projects only via devilry.
@@ -96,15 +86,17 @@ Projects are handed in using devilry.ifi.uio.no. We use Github as repository for
===== Learning outcomes =====
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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, you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. You 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;
* 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 you are expected to know modern programming languages like Python or C++.
* Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, 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
* Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications
* Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression
* Learn about various neural networks and deep learning methods for supervised and unsupervised learning
* Learn about about decision trees and random forests
* Learn about support vector machines and kernel transformations
* Reduction of data sets, from PCA to clustering, supervised and unsupervided methods
* Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++
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@@ -112,16 +104,14 @@ The course introduces a variety of central algorithms and methods essential for
===== Topics covered in this course: Statistical analysis and optimization of data =====
* Anaconda and other Python environments, see intro slides
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===== Other courses on Data science and Machine Learning at UiO =====
The link here URL:"https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/" gives an excellent overview of courses on Machine learning at UiO.
o "STK2100 Machine learning and statistical methods for prediction and classification":"http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html".
o "IN3050 Introduction to Artificial Intelligence and Machine Learning":"https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html". Introductory course in machine learning and AI with an algorithmic approach.
o "STK-INF3000/4000 Selected Topics in Data Science":"http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html". The course provides insight into selected contemporary relevant topics within Data Science.
o "IN4080 Natural Language Processing":"https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html". Probabilistic and machine learning techniques applied to natural language processing.
o "STK-IN4300 œôòó Statistical learning methods in Data Science":"https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html". An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
o "INF4490 Biologically Inspired Computing":"http://www.uio.no/studier/emner/matnat/ifi/INF4490/". An introduction to self-adapting methods also called artificial intelligence or machine learning.
o "IN-STK5000 Adaptive Methods for Data-Based Decision Making":"https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html". Methods for adaptive collection and processing of data based on machine learning techniques.
o "IN5400/INF5860 œôòó Machine Learning for Image Analysis":"https://www.uio.no/studier/emner/matnat/ifi/IN5400/". An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
o "TEK5040 œôòó Dyp l�æring for autonome systemer":"https://www.uio.no/studier/emner/matnat/its/TEK5040/". The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.
o "STK4051 Computational Statistics":"https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html"
o "STK4021 Applied Bayesian Analysis and Numerical Methods":"https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html"
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