Course syllabus adopted 2024-01-31 by Head of Programme (or corresponding).
Overview
- Swedish nameBayesiansk inferens och maskininlärning
- CodeTIF385
- Credits6 Credits
- OwnerTKTFY
- Education cycleFirst-cycle
- Main field of studyEngineering Physics
- DepartmentPHYSICS
- GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail
Course round 1
- Teaching language Swedish
- Application code 57114
- Open for exchange studentsNo
- Only students with the course round in the programme overview.
Credit distribution
Module | Sp1 | Sp2 | Sp3 | Sp4 | Summer | Not Sp | Examination dates |
|---|---|---|---|---|---|---|---|
| 0122 Project 2 c Grading: UG | 2 c | ||||||
| 0222 Examination 4 c Grading: TH | 4 c |
|
In programmes
Examiner
Christian Forssén- Full Professor, Subatomic, High Energy and Plasma Physics, Physics
Eligibility
General entry requirements for bachelor's level (first cycle)Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Specific entry requirements
The same as for the programme that owns the course.Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.
Course specific prerequisites
- Undergraduate mathematics: Multivariable analysis (MVE035 or equivalent), linear algebra (MVE670 or equivalent), mathematical statistics (TMA321 or equivalent) - Basic programming skills (MVE035 or equivalent). The Python programming language will be used in the course.Aim
Building on first courses in mathematical analysis and mathematical statistics this course aims to provide both theoretical understanding and practical experience with Bayesian inference and modern machine learning. In combination, this knowledge should provide a firm basis for modeling of physical systems and generally for applications of statistical models.The course is partly project-based, and students will learn to develop and structure small computer codes for statistical inference and machine learning with scientific applications. Specifically, students will perform tasks for physics projects using the Python programming language with relevant open-source libraries.
Learning outcomes (after completion of the course the student should be able to)
- understand and numerically implement several basic algorithms used in data analysis and machine learning such as linear methods for regression and classification and simple neural networks;
- use python for practical applications of statistical inference and machine learning methods and to visualize numerical results.
- describe fundamental concepts in Bayesian statistics.
- perform linear modeling of physical systems by applying the Bayesian paradigm for inference from data.
- explain central aspects of stochastic processes and Markov chains, and numerically apply these to perform MCMC sampling of multivariate probability densities;
- maintain a scientific and ethical conduct in the process of collecting, evaluating, and analyzing data and writing computer programs with a particular consideration of gender equality, equal treatment, and diversity.
- communicate results and conclusions from a scientific data analysis with clarity and critical assessment.
Content
The course consists of three integrated parts
- Machine learning
- Bayesian inference
- Stochastic processes and MCMC simulation
The following subtopics will be covered
- Central concepts in machine learning
- Linear methods for regression and classification
- Neural networks
- Bayesian statistics
- Stochastic processes, Markov chains, Markov Chain Monte Carlo (MCMC) simulation
Organisation
- Lectures
- Supervised exercise classes (also in the computer lab)
- Recommended analytical and numerical exercises
- Numerical projects
Literature
Course compendium. Lecture notes.
Extra course literature will be announced on the course web page.
Examination including compulsory elements
Written exam
Numerical projects
The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers about disability study support.