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Applied Data Analysis and Machine Learning: Introduction to the course, Logistics and Practicalities

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+Morten Hjorth-Jensen [1, 2] +
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[1] Department of Physics, University of Oslo
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[2] Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
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Jul 23, 2019

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+ © 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license +
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Overview of first week

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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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Lectures and ComputerLab

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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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Course Format

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  • Three compulsory projects. Electronic reports only using devilry to hand in projects and Git 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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    1. For the last project Each group/participant submits a proposal or works with suggested (by us) proposals for the project.
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    2. 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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    3. Poster session where all participants can study and discuss the other proposals.
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    4. 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 of the course.
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Teachers and ComputerLab

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+Teachers : + +

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  1. Hanna Svennevik
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  2. Morten Hjorth-Jensen
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  3. Lucas Charpentier
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  4. Stian Bilek
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day Time
Group 1: Tuesday 8am-10am
Group 2: Tuesday 10am-12pm
Group 3: Tuesday 12pm-2pm
Group 4: Tuesday 2pm-4pm
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Deadlines for projects (tentative)

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  1. Project 1: September 30 (graded with feedback)
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  2. Project 2: November 4 (graded with feedback)
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  3. 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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Learning outcomes

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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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Topics covered in this course: Statistical analysis and optimization of data

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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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Topics covered in this course: Machine Learning

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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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Extremely useful tools, strongly recommended

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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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Other courses on Data science and Machine Learning at UiO

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+The link here 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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  1. STK2100 Machine learning and statistical methods for prediction and classification.
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  2. IN3050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI with an algorithmic approach.
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  3. STK-INF3000/4000 Selected Topics in Data Science. The course provides insight into selected contemporary relevant topics within Data Science.
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  4. IN4080 Natural Language Processing. Probabilistic and machine learning techniques applied to natural language processing.
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  5. STK-IN4300 Statistical learning methods in Data Science. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
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  6. INF4490 Biologically Inspired Computing. An introduction to self-adapting methods also called artificial intelligence or machine learning.
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  7. IN-STK5000 Adaptive Methods for Data-Based Decision Making. Methods for adaptive collection and processing of data based on machine learning techniques.
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  8. IN5400/INF5860 Machine Learning for Image Analysis. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
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  9. TEK5040 Deep learning for autonomous systems. 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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  10. STK4051 Computational Statistics
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  11. STK4021 Applied Bayesian Analysis and Numerical Methods
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