Click on the lecture titles to download the annotated slides for each lecture, or click on the slides link next to each section label to download the combined slides for the whole section. For further reading, we have also provided relevant references to the class textbook next to each lecture.
Overview and Motivation Chapter 1.
Distributions Chapters 2.1.1 to 2.1.3.
Factors. Chapter 4.2.1.
Bayesian Network Fundamentals (combined slides)
Semantics and Factorization Chapters 3.2.1, 3.2.2. If you are unfamiliar with genetic inheritance, please watch this short Khan Academy video for some background.
Reasoning Patterns. Chapter 3.2.1.
Flow of Probabilistic Influence. Chapter 3.3.1.
Conditional Independence. Chapters 2.1.4, 3.1.
Independencies in Bayesian Networks. Chapter 3.2.2.
Naive Bayes. Chapter 3.1.3.
Application - Medical Diagnosis Chapter 3.2: Box 3.D (p. 67).
Overview. Chapter 6.1.
Temporal Models - DBNs. Chapters 6.2, 6.3.
Temporal Models - HMMs. Chapters 6.2, 6.3.
Plate Models. Chapter 6.4.1.
Octave Tutorial Code
Overview. Chapters 5.1, 5.2.
Tree-Structured CPDs. Chapter 5.3.
Independence of Causal Influence. Chapter 5.4.
Continuous Variables. Chapter 5.5.
Pairwise Markov Networks. Chapter 4.1.
General Gibbs Distribution. Chapter 4.2.2.
Conditional Random Fields. Chapter 4.6.1.
Independencies in Markov Networks. Chapter 4.3.1.
I-Maps and Perfect Maps. Chapter 3.3.4.
Log-Linear Models. Chapter 4.4, p. 125.
Shared Features in Log-Linear Models. Chapter 4: Box 4.B (p. 112), Box 4.C (p. 126), Box 4.D (p. 127).
Representation Wrapup: Knowledge Engineering (combined slides)