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Graduate courses

Departments' graduate courses for PhD-students.


Syllabus for

Academic year
TIF285 - Learning from data
Bayesiansk dataanalys och maskininlärning
Syllabus adopted 2019-02-14 by Head of Programme (or corresponding)
Owner: MPPHS
7,5 Credits
Grading: TH - Five, Four, Three, Fail
Education cycle: Second-cycle
Major subject: Engineering Physics
Department: 16 - PHYSICS

Teaching language: English
Application code: 85117
Open for exchange students: Yes
Block schedule: C+
Maximum participants: 60

Module   Credit distribution   Examination dates
Sp1 Sp2 Sp3 Sp4 Summer course No Sp
0119 Project 7,5 c Grading: TH   7,5 c    

In programs

MPPHS PHYSICS, MSC PROGR, Year 1 (compulsory)


Christian Forssén

  Go to Course Homepage


In order to be eligible for a second cycle course the applicant needs to fulfil the general and specific entry requirements of the programme that owns the course. (If the second cycle course is owned by a first cycle programme, second cycle entry requirements apply.)
Exemption from the eligibility requirement: Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling these requirements.

Course specific prerequisites

- Solid background in undergraduate mathematics (Multivariable analysis, linear algebra, mathematical statistics)
- Basic programming skills (The Python programming language will be introduced and used throughout the course. Experience from using Matlab or similar interpreted languages is sufficient.)
- General physics knowledge (Undergraduate-level, introductory physics helps to understand the context of the scientific examples that will be used.)


The course introduces a variety of central algorithms and methods essential for performing scientific data analysis using statistical inference and machine learning. Much emphasis is put on practical applications of Bayesian inference in the natural and engineering sciences, i.e. the ability to quantify the strength of inductive inference from facts (such as experimental data) to propositions such as scientific hypotheses and models.
The course is project-based, and the students will be exposed to fundamental research problems through the various projects, with the aim to reproduce state-of-the-art scientific results. The students will use the Python programming language, with relevant open-source libraries, and will learn to develop and structure computer codes for scientific data analysis projects. 

Learning outcomes (after completion of the course the student should be able to)

- integrate knowledge of common statistical distributions and central concepts in Bayesian statistics into the analysis of scientific data;
- explain central aspects of Monte Carlo methods and Markov chains, and numerically apply these methods to sample multivariate probability densities;
- quantify and critically assess uncertainties of model parameters that are statistically inferred from scientific data; perform model comparison using a Bayesian viewpoint;
- understand and numerically implement several basic algorithms used in data analysis and  machine learning such as linear methods for regression and classification, simple neural networks and gaussian processes;
- use python to perform scientific data analysis using statistical inference and machine learning and to visualize numerical results.
- write well-structured technical reports where results and conclusions from a scientific data analysis are communicated in a clear way.
- maintain a scientific and ethical conduct in the process of analyzing data and writing computer programs.


The course has two central parts
1. Bayesian inference and data analysis. 
2. Machine learning methods for data analysis. 

The following subtopics will be covered
- Basic concepts from statistics, expectation values, variance, covariance, correlation functions and errors; discrete versus continuous probability distributions;
- Review of simple statistics models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
- Central elements of Bayesian statistics and modeling;
- Monte Carlo methods, Markov chains, Metropolis-Hastings algorithm;
- Linear methods for regression and classification;
- Gaussian and Dirichlet processes;
- Neural networks;


Supervised computational exercises (group work on numerical projects)
Selected number of small analytical and numerical homework exercises
Two computational projects with written reports.


Lecture notes will be made available. 

Recommended textbook:
Phil Gregory, Bayesian Logical Data Analysis for the Physical Sciences, Cambridge University Press, 2010

Additional reading: 
David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 4th printing, 2005
Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, 2nd edition, 2009
Andrew Gelman et al, Bayesian Data Analysis, CRC Press, 3rd edition, 2014
Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, 1st edition, 2017

Examination including compulsory elements

The final grade is based on the performance on homework assignments and the graded numerical projects.

Page manager Published: Thu 04 Feb 2021.