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TITLE: Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities
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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
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DATE: today
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!split
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===== Overview of first week =====
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!bblock
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* Thursday: First lecture: Presentation of the course, aims and content
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* Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra
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* Friday: Linear regression
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* Computer lab: Tuesday. First time: Tuesday August 27.
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!eblock
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!split
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===== Lectures and ComputerLab =====
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!bblock
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* Lectures: Thursday (12.15pm-2pm) and Friday (12.15pm-2pm).
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* Weekly reading assignments needed to solve projects and exercises.
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* Weekly exercises when not working on projects. You can hand in exercises if you want.
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* First hour of each lab session may be used to discuss technicalities, address questions etc linked with projects and exercises.
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* Detailed lecture notes, exercises, all programs presented, projects etc can be found at the homepage of the course.
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* Computerlab: Tuesday (8am-6pm), VB IT-auditorium 3
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* Weekly plans and all other information are on the official webpage.
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* No final exam, three projects that are graded and have to be approved.
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!eblock
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!split
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===== Course Format =====
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!bblock
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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.
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* Evaluation and grading: The three projects are graded and each counts 1/3 of the final mark. No final written or oral exam.
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o For the last project Each group/participant submits a proposal or works with suggested (by us) proposals for the project.
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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
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o Poster session where all participants can study and discuss the other proposals.
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o Based on feedback etc, each group finalizes the report and submits for grading.
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* 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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!eblock
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!split
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===== Teachers and ComputerLab =====
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!bblock
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_Teachers :_
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o "Hanna Svennevik":"https://www.researchgate.net/profile/Hanna_Svennevik"
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o "Morten Hjorth-Jensen":"http://mhjgit.github.io/info/doc/web/"
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o "Lucas Charpentier":"https://no.linkedin.com/in/lucas-charpentier-176206171"
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o "Stian Bilek":"https://www.researchgate.net/profile/Stian_Bilek"
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|------------------------------------------------------|
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| day | Time |
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|----------------------------------------------------|
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| Group 1: Tuesday | 8am-10am |
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| Group 2: Tuesday | 10am-12pm |
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| Group 3: Tuesday | 12pm-2pm |
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| Group 4: Tuesday | 2pm-4pm |
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|-------------------------------------------------|
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!eblock
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!split
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===== Deadlines for projects (end of day) =====
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!bblock
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o Project 1: September 30 (graded with feedback)
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o Project 2: November 4 (graded with feedback)
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o Project 3: December 2 (graded with feedback)
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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.
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!eblock
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!split
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===== Learning outcomes =====
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!bblock
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* Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning
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* Be capable of extending the acquired knowledge to other systems and cases
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* Have an understanding of central algorithms used in data analysis and machine learning
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* Gain knowledge of central aspects of Monte Carlo methods, Markov chains, Gibbs samplers and their possible applications
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* Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression
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* Learn about various neural networks and deep learning methods for supervised and unsupervised learning
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* Learn about about decision trees and random forests
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* Learn about support vector machines and kernel transformations
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* Reduction of data sets, from PCA to clustering, supervised and unsupervided methods
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* 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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!eblock
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!split
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===== Topics covered in this course: Statistical analysis and optimization of data =====
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!bblock
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* Basic concepts, expectation values, variance, covariance, correlation functions and errors
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* Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions
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* Central elements of Bayesian statistics and modeling
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* Gradient methods for data optimization
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* Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm
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* Linear methods for regression and classification
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* Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods
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* Practical optimization using Singular-value decomposition and least squares for parameterizing data
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!eblock
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!split
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===== Topics covered in this course: Machine Learning =====
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!bblock
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The following topics will be covered
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* Linear Regression and Logistic Regression
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* Neural networks and deep learning
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* Decisions trees and nearest neighbor algorithms
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* Support vector machines
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* Bayesian Neural Networks
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* Boltzmann Machines
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* Dimensionality reduction, from PCA to cluster models
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!eblock
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!split
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===== Extremely useful tools, strongly recommended =====
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!bblock and discussed at the lab sessions
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* GIT for version control, highly recommended
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* Devilry for handing in projects, next week
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* Anaconda and other Python environments, see intro slides
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!eblock
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!split
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===== Other courses on Data science and Machine Learning at UiO =====
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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.
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o "STK2100 Machine learning and statistical methods for prediction and classification":"http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html".
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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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.
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o "STK4051 Computational Statistics":"https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html"
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o "STK4021 Applied Bayesian Analysis and Numerical Methods":"https://www.uio.no/studier/emner/matnat/math/STK4021/index-eng.html"
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