Syllabus

Credits: 5

Schedule: 11.01.2019 - 09.04.2019

Teaching Period (valid 01.08.2018-31.07.2020): 

III - IV (Spring)

Learning Outcomes (valid 01.08.2018-31.07.2020): 

After the course, the student understands how Bayesian networks are constructed with conditional independence assumptions and how they are applied in modeling of joint probability distributions. The students can explain the structure and usage of common probabilistic models in machine learning, such as sparse Bayesian linear models, Gaussian mixture models and factor analysis models. The students can apply Bayes’ theorem for computing probability statements and understand the fundamental role of Bayes’ theorem in probabilistic inference. The students can derive approximate inference algorithms for complex models, where exact probabilistic inference may not be applied. Furthermore, they can translate probabilistic models, inference, and learning algorithms into practical computer implementations.

Content (valid 01.08.2018-31.07.2020): 

The course covers concepts in probabilistic machine learning: independence, conditional independence, mixture models, EM algorithm, Bayesian networks, latent linear models, and algorithms for exact and approximate inference, with an emphasis on variational inference. The course emphasizes understanding fundamental principles and their use in practical machine learning problems.

Assessment Methods and Criteria (valid 01.08.2018-31.07.2020): 

Exercises and an exam (details provided on the first lecture).

Workload (valid 01.08.2018-31.07.2020): 

20 + 20 (2 + 2)

Study Material (valid 01.08.2018-31.07.2020): 

David Barber, Bayesian Reasoning and Machine Learning. Cambridge University Press, 2012.

Substitutes for Courses (valid 01.08.2018-31.07.2020): 

Replaces the former course T-61.5140 Machine Learning: Advanced Probabilistic Methods and T-61.5040 Learning Models and Methods.

Prerequisites (valid 01.08.2018-31.07.2020): 

CS-E3210 / T-61.3050 Machine Learning: Basic Principles

CS-E5710 Bayesian Data Analysis (recommended)

Grading Scale (valid 01.08.2018-31.07.2020): 

0-5

Description

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