Bayesian Learning, 6 credits

Bayesianska metoder, 6 hp

TDDE07

Main field of study

Computer Science and Engineering Computer Science

Course level

Second cycle

Course type

Programme course

Examiner

Mattias Villani

Director of studies or equivalent

Ann-Charlotte Hallberg

Education components

Preliminary scheduled hours: 48 h
Recommended self-study hours: 112 h

Available for exchange students

Yes
ECV = Elective / Compulsory / Voluntary
Course offered for Semester Period Timetable module Language Campus ECV
6CDDD Computer Science and Engineering, M Sc in Engineering 8 (Spring 2017) 2 2 English Linköping, Valla E
6CDDD Computer Science and Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2017) 2 2 English Linköping, Valla E
6CMJU Computer Science and Software Engineering, M Sc in Engineering 8 (Spring 2017) 2 2 English Linköping, Valla E
6CMJU Computer Science and Software Engineering, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2017) 2 2 English Linköping, Valla E
6CITE Information Technology, M Sc in Engineering 8 (Spring 2017) 2 2 English Linköping, Valla E
6CITE Information Technology, M Sc in Engineering (AI and Machine Learning) 8 (Spring 2017) 2 2 English Linköping, Valla E

Main field of study

Computer Science and Engineering, Computer Science

Course level

Second cycle

Advancement level

A1X

Course offered for

  • Computer Science and Engineering, M Sc in Engineering
  • Information Technology, M Sc in Engineering
  • Computer Science and Software Engineering, M Sc in Engineering

Entry requirements

Note: Admission requirements for non-programme students usually also include admission requirements for the programme and threshold requirements for progression within the programme, or corresponding.

Prerequisites

Mathematical analysis; Linear Algebra; Probability and Statistics; Machine Learning; Basic programming.

 

Intended learning outcomes

The course gives a solid introduction to Bayesian learning, with special emphasis on theory, models and methods used in machine learning applications. The student will learn about the basic ideas and concepts in Bayesian analysis from detailed analysis of simple probability models. The course presents simulation algorithms typically used in practical Bayesian work, and course participants will learn how to apply those algorithms to analyze complex machine learning models.
After completing the course the student should be able to:

  • derive the posterior distribution for a number of basic probability models
  • use simulation algorithms to perform a Bayesian analysis of more complex models
  • perform Bayesian prediction and decision making
  • perform Bayesian model inference.

Course content

Likelihood, Subjective probability, Bayes theorem, Prior and posterior distribution, Bayesian analysis of the following models: Bernoulli, Normal, Multinomial, Multivariate normal, Linear and nonlinear regression, Binary regression, Mixture models; Regularization priors, Classification, Naïve Bayes, Marginalization, Posterior approximation, Prediction, Decision theory, Markov Chain Monte Carlo, Gibbs sampling, Bayesian variable selection, Model selection, Model averaging.
 

Teaching and working methods

The course consists of lectures, exercises, seminars and computer labs. The lectures introduce concepts and theories that students then use in problem solving at the exercises and computer labs. The seminars are used for student presentations of the computer lab reports and discussions.
 

Examination

UPG1Computer assignments3 creditsU, G
DAT1Computer examination3 creditsU, 3, 4, 5


DAT1 is an exam in a computer hall that tests students' theoretical knowledge and problem-solving skills in Bayesian learning.
UPG1 consists of computer exercises that tests the students' ability to translate theoretical knowledge into practical problem solving in Bayesian learning.

Grades

Four-grade scale, LiU, U, 3, 4, 5

Other information

Supplementary courses:
Advanced Machine Learning, Text Mining, Visual Object Recognition and Detection
 

Department

Institutionen för datavetenskap

Director of Studies or equivalent

Ann-Charlotte Hallberg

Examiner

Mattias Villani

Education components

Preliminary scheduled hours: 48 h
Recommended self-study hours: 112 h

Course literature

Additional literature

Books

  • Gelman, A., Carlin, J.B., Stern, H. S., Dunson, D. B., Vehtari, A., and Donald Rubin, D.B., (2013) Bayesian Data Analysis 3rd edition Chapman & Hall
Code Name Scope Grading scale
UPG1 Computer assignments 3 credits U, G
DAT1 Computer examination 3 credits U, 3, 4, 5


DAT1 is an exam in a computer hall that tests students' theoretical knowledge and problem-solving skills in Bayesian learning.
UPG1 consists of computer exercises that tests the students' ability to translate theoretical knowledge into practical problem solving in Bayesian learning.

Additional literature

Books

Gelman, A., Carlin, J.B., Stern, H. S., Dunson, D. B., Vehtari, A., and Donald Rubin, D.B., (2013) Bayesian Data Analysis 3rd edition Chapman & Hall

Note: The course matrix might contain more information in Swedish.

I = Introduce, U = Teach, A = Utilize
I U A Modules Comment
1. DISCIPLINARY KNOWLEDGE AND REASONING
1.1 Knowledge of underlying mathematics and science (G1X level)
X
X
X
DAT1

                            
1.2 Fundamental engineering knowledge (G1X level)
X
X
X
DAT1
UPG1

                            
1.3 Further knowledge, methods, and tools in one or several subjects in engineering or natural science (G2X level)

                            
1.4 Advanced knowledge, methods, and tools in one or several subjects in engineering or natural sciences (A1X level)

                            
1.5 Insight into current research and development work

                            
2. PERSONAL AND PROFESSIONAL SKILLS AND ATTRIBUTES
2.1 Analytical reasoning and problem solving
X
X
X
DAT1
UPG1

                            
2.2 Experimentation, investigation, and knowledge discovery
X
X
X
DAT1
UPG1

                            
2.3 System thinking
X
X
X
DAT1

                            
2.4 Attitudes, thought, and learning
X
DAT1

                            
2.5 Ethics, equity, and other responsibilities

                            
3. INTERPERSONAL SKILLS: TEAMWORK AND COMMUNICATION
3.1 Teamwork
X
UPG1

                            
3.2 Communications
X
UPG1

                            
3.3 Communication in foreign languages

                            
4. CONCEIVING, DESIGNING, IMPLEMENTING AND OPERATING SYSTEMS IN THE ENTERPRISE, SOCIETAL AND ENVIRONMENTAL CONTEXT
4.1 External, societal, and environmental context
X

                            
4.2 Enterprise and business context

                            
4.3 Conceiving, system engineering and management

                            
4.4 Designing

                            
4.5 Implementing

                            
4.6 Operating

                            
5. PLANNING, EXECUTION AND PRESENTATION OF RESEARCH DEVELOPMENT PROJECTS WITH RESPECT TO SCIENTIFIC AND SOCIETAL NEEDS AND REQUIREMENTS
5.1 Societal conditions, including economic, social, and ecological aspects of sustainable development for knowledge development

                            
5.2 Economic conditions for knowledge development

                            
5.3 Identification of needs, structuring and planning of research or development projects

                            
5.4 Execution of research or development projects

                            
5.5 Presentation and evaluation of research or development projects

                            

This tab contains public material from the course room in Lisam. The information published here is not legally binding, such material can be found under the other tabs on this page.

There are no files available for this course.