Morten Hjorth-Jensen, Department of Physics, University of Oslo and Department of Physics and Astronomy and National Superconducting Cyclotron Laboratory, Michigan State University
Date: Nov 12, 2019
Copyright 1999-2019, Morten Hjorth-Jensen. Released under CC Attribution-NonCommercial 4.0 license
Thursday August 22: First lecture: Presentation of the course, aims and content
Thursday: Second Lecture: Start with simple linear regression and repetition of linear algebra
Friday August 23: Linear regression
Computer lab: Tuesday. First time: Tuesday August 27.
Lectures: Thursday (2.15pm-4pm, this may change) and Friday (12.15pm-2pm).
Weekly reading assignments needed to solve projects and exercises.
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.
Computerlab: Tuesday (8am-4pm), VB IT-auditorium 3. Depending on how many enlist we may extend the lab sessions
Weekly plans and all other information are on the official webpage.
No final exam, three projects that are graded and have to be approved.
Three compulsory projects. Electronic reports only using devilry to hand in projects and Git 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.
a. For the last project Each group/participant submits a proposal or works with suggested (by us) proposals for the project.
b. 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
c. Poster session where all participants can study and discuss the other proposals.
d. Based on feedback etc, each group finalizes the report and submits for grading.
Teachers :
| day | Time |
|---|---|
| Group 1: Tuesday | 8am-10am |
| Group 2: Tuesday | 10am-12pm |
| Group 3: Tuesday | 12pm-2pm |
| Group 4: Tuesday | 2pm-4pm |
Project 1: September 30 (graded with feedback)
Project 2: November 13 (graded with feedback)
Project 3: December 15 (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.
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++
Basic concepts, expectation values, variance, covariance, correlation functions and errors
Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions
Central elements of Bayesian statistics and modeling
Gradient methods for data optimization
Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm
Linear methods for regression and classification
Estimation of errors using cross-validation, blocking, bootstrapping and jackknife methods
Practical optimization using Singular-value decomposition and least squares for parameterizing data
The following topics will be covered
Linear Regression and Logistic Regression
Neural networks and deep learning
Decisions trees and nearest neighbor algorithms
Support vector machines
Bayesian Neural Networks
Boltzmann Machines
Dimensionality reduction, from PCA to cluster models
and discussed at the lab sessions.
GIT for version control, highly recommended
Devilry for handing in projects, next week
Anaconda and other Python environments, see intro slides
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.
STK2100 Machine learning and statistical methods for prediction and classification.
IN3050 Introduction to Artificial Intelligence and Machine Learning. Introductory course in machine learning and AI with an algorithmic approach.
STK-INF3000/4000 Selected Topics in Data Science. The course provides insight into selected contemporary relevant topics within Data Science.
IN4080 Natural Language Processing. Probabilistic and machine learning techniques applied to natural language processing.
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.
INF4490 Biologically Inspired Computing. An introduction to self-adapting methods also called artificial intelligence or machine learning.
IN-STK5000 Adaptive Methods for Data-Based Decision Making. Methods for adaptive collection and processing of data based on machine learning techniques.
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.
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.