Course Notes
The notes written by students and edited by instructors
-
Lecture 19: The Attention Mechanism
An in-depth exploration of attention mechanisms in neural networks, from their motivation and foundational forms (hard and soft attention) to their applications in image captioning and the Transformer architecture.
-
Lecture 18 - Deep Generative Models
Introduction to Variational Autoencoders, Generative Adversarial Networks, and Diffusion Models.
-
Lecture 16 - Deep Learning from a GM Perspective
Deep learning foundations viewed through the lens of graphical models, covering perceptrons, neural networks, backpropagation, and probabilistic interpretations.
-
Lecture 12 - Parameter Learning of partially observed BNs
Parameter Learning of partially observed BNs
-
Lecture 05 - Undirected GMs (MRFs)
Introduction to Undirected GMs
-
Lecture 04 - Conditional Independence and Directed GMs (BNs)
Review of conditional independence, and an introduction to Directed GMs (BNs)
-
Lecture 03 - A Linear View of Discriminative and Generative Models
Introduction to LaTex & Distinction between Discriminative and Generative Models
-
Lecture 02 - Statistics review
A statistics review, including MLE and MAP
-
Lecture 01 - Course Introduction, Introduction to PGMs
Course Introductory Admin-related details and an introduction to PGMs in the scope of this course
-
Lecture Notes Template
An example of a distill-style lecture notes that showcases the main elements.