Bayesian Machine Learning and Information Processing (5SSD0)

academic year 2022/23

Note: This site is currently under construction.

The 2022/23 course “Bayesian Machine Learning and Information Processing” will start in November 2022 (Q2).

Course goals

This course provides an introduction to Bayesian machine learning and information processing systems. The Bayesian approach affords a unified and consistent treatment of many useful information processing systems.

Course summary

This course covers the fundamentals of a Bayesian (i.e., probabilistic) approach to machine learning and information processing systems. The Bayesian approach allows for a unified and consistent treatment of many model-based machine learning techniques. Initially, we focus on Linear Gaussian systems and will discuss many useful models and applications, including common regression and classification methods, Gaussian mixture models, hidden Markov models and Kalman filters. We will discuss important algorithms for parameter estimation in these models including the Expectation-Maximization (EM) algorithm and Variational Bayes (VB). The Bayesian method also provides tools for comparing the performance of different information processing systems by means of estimating the Bayesian evidence for each model. We will discuss several methods for approximating Bayesian evidence. Next, we will discuss intelligent agents that learn purposeful behavior from interactions with their environment. These agents are used for applications such as self-driving cars or interactive design of virtual and augmented realities. Indeed, in this course we relate synthetic Bayesian intelligent agents to natural intelligent agents such as the brain. You will be challenged to code Bayesian machine learning algorithms yourself and apply them to practical information processing problems.

News and Announcements

Instructors

Materials

In principle, you can download all needed materials from the links below.

Books

Please consider downloading the following books/resources:

Software

Lecture notes, videos and exercises

You can access all lecture notes, videos and exercises online through the links below:

Date lesson materials
video guides lecture notes exercises live class
16-Nov-2022 B0: Course Syllabus
B1: Machine Learning Overview
B1 B0, B1 B0-B1
18-Nov-2022 B2: Probability Theory Review B2.1, B2.2 B2 B2-ex
B2-sol
B2
23-Nov-2022 B3: Bayesian Machine Learning B3.1, B3.2 B3 B3-ex
B3-sol
B3
25-Nov-2022 W1: Probabilistic Programming 1 - Intro Bayesian ML W1.1, W1.2, W1.3 W1 W1-ex
W1-sol
W1
30-Nov-2022 B4: Factor Graphs and the Sum-Product Algorithm B4 B4 B4-ex
B4-sol
B4
02-Dec-2022 B5: Continuous Data and the Gaussian Distribution B5.1, B5.2, B5.3 B5 B5-ex
B5-sol
B5
07-Dec-2022 B6: Discrete Data and the Multinomial Distribution B6 B6 B6-ex
B6-sol
B6
09-Dec-2022 W2: ProbProg 2 - MP & Analytical Bayesian Solutions W2.1, W2.2, W2.3 W2 W2-ex
W2-sol
W2
14-Dec-2022 B7: Regression B7 B7 B7-ex
B7-sol
B7
catch-up
16-Dec-2022 B8: Generative Classification
B9: Discriminative Classification
B8, B9 B8, B9 B8-9-ex
B8-9-sol
B8-9
21-Dec-2022 B10: Latent Variable Models and Variational Bayes B10 B10 B10-ex
B10-sol
B10
break
11-Jan-2023 W3: ProbProg 3 - Regression and Classification W3.1, W3.2 W3 W3-ex
W3-sol
W3
13-Jan-2023 B11: Dynamic Models B11 B11 B11-ex
B11-sol
B11
18-Jan-2023 B12: Intelligent Agents and Active Inference B12 B12 B12-ex
B12-sol
B12
20-Jan-2023 W4: ProbProg 4: Latent Variable and Dynamic Models W4.1, W4.2 W4 W4-ex
W4-sol
W4
M1: Bonus Lecture: What is Life? M1 M1.1, M1.2
02-Feb-2023 written examination (13:30-16:30)
20-Apr-2023 resit written examination (18:00-21:00)

Study Guide

  • Please consult the Course Syllabus (lecture notes for 1st class) for advice on how to study the materials.

  • Each year there will be two written exam opportunities. You cannot bring notes or books to the written exam sessions. All needed formulas are supplied at the exam sheet.

Assignment

  • The assignment may be downloaded here. The Jupyter notebook explains the problem in detail.
  • Please hand in the completed notebook file on Canvas.
  • The solution to this year’s assignment can be found here.

Exam Preparation

In addition to the materials in the above table, we provide two representative practice written exams:

Instructors