PML: Course material

To pass the course you must understand the material provided here unless marked as not examinable. Working through some of the optional material may help you to better understand the content.

The material will be added as we proceed. For reference, please see the previous editions of the course.

Note on the self-study exercises: These are additional exercises to support your learning and exam preparation. Work through them at your own pace. Do some to support your understanding of the lectures and tutorials. Reserve some for exam preparations (they include former exam questions). Try to solve each exercise yourself first. If you get stuck, that's fine, consult the solution gradually, starting with the next step.  Try to understand why you got stuck; was it a concept or a technical step? To help you generalise, after each exercise, (1) connect it to relevant lecture concepts and (2) note any useful techniques/tricks in a "technical log" to review before the exam.

Schedule

WeekDateMaterialsTutorial
1Tue
Sept 22
 
Wed
Sept 23
Thu
Sept 24
2Tue
Sept 29
  • Directed Graphical Models
 
Wed 
Sept 30
  • Directed Graphical Models
Thu
Oct 1
3Tue
Oct 6
  • Undirected Graphical Models

Tutorial 1

Wed
Oct 7
Thu
Oct 8
4Tue
Oct 13
  • Exact inference

Tutorial 2

Wed
Oct 14
  • Exact inference
Thu 
Oct 15
5Tue
Oct 20

Tutorial 3

Wed
Oct 21
  • Actions and their Effects (Causality)
Thu
Oct 22
6Tue
Oct 27
  • Decision making under uncertainty

Tutorial 4

Wed
Oct 28
Thu
Oct 29
  • Basics of Model-Based Learning
7

Tue

Nov 3

Tutorial 5

Wed
Nov 4
  • Factor Analysis and Independent Component Analysis
  • Intractable Likelihood Functions  [slides][notes]
Thu
Nov 5
  • Intractable Likelihood Functions
8Tue
Nov 10
  • Variational Inference and Learning I: Fundamentals, mean-field VI, and the EM algorithm
    [slides] [notes] [self-study exercises] [self-study solutions]

Tutorial 6

  • Questions
  • Solutions
Wed
Nov 11
  • Variational Inference and Learning I: Fundamentals, mean-field VI, and the EM algorithm
Thu 
Nov 12
  • Learning for Hidden Markov Models
    [slides] [notes] [self-study exercises] [self-study solutions]
  • Optional: Python notebook on HMMs (check out the learning notebooks)
9Tue
Nov 17
  • Variational Inference and Learning II: Modern VI and Variational Autoencoders
    [slides] [notes] [self-study exercises] [self-study solutions]
  • Optional: Python notebook on VAEs

Tutorial 7

  • Questions
  • Solutions
Wed 
Nov 18
  • Variational Inference and Learning II: Modern VI and Variational Autoencoders
Thu
Nov 19
  • Sampling and Monte Carlo Integration
    [slides] [notes] [self-study exercises] [self-study solutions]
10Tue
Nov 24
  • Sampling and Monte Carlo Integration

Tutorial 8

  • Questions
  • Solutions
Wed
Nov 25
  • Sampling and Monte Carlo Integration
Thu
Nov 26
  • Course recap [slides]
  • Exam info [slides]
License
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