Course Notes
The notes written by students and edited by instructors
-
Lecture 8: Causal Discovery and Inference
Learning and inference algorithms for causal discovery.
-
Lecture 7: Maximum likelihood learning of undirected GM
Algorithms for learning UGMs along with a brief overview of CRFs.
-
Lecture 6: Learning Partially Observed GM and the EM Algorithm
Introduction to the process of estimating the parameters of graphical models from data using the EM (Baum-Welch) algorithm.
-
Lecture 5: Parameter Estimation in Fully Observed Bayesian Networks
Introduction to the problem of Parameter Estimation in fully observed Bayesian Networks
-
Lecture 4: Exact Inference
Introducing the problem of inference and finding exact solutions to it in graphical models.
-
Lecture 3: Undirected Graphical Models
An introduction to undirected graphical models
-
Lecture 2: Bayesian Networks
Overview of Bayesian Networks, their properties, and how they can be helpful to model the joint probability distribution over a set of random variables. Concludes with a summary of relevant sections from the textbook reading.
-
Lecture 1: Introduction to Graphical Models
Introducing why graphical models are useful, and an overview of the main types of graphical models.
-
Lecture Notes Template
An example of a distill-style lecture notes that showcases the main elements.