Representation Learning
This reading assignment covers required fundamentals that are essential for understanding the probabilistic approaches to deep learning we will focus on in the last part of the course.
From the Deep Learning Book - Chapter 16: Structured Probabilistic Models for Deep Learning, please read these sections:
16.1 The Challenge of Unstructured Modeling
16.2 Using Graphs to Describe Model Structure
(This is the most important section.)
16.3 Sampling from Graphical Models
(This is the second most important section.)
16.4 Advantages of Structured Modeling
16.5 Learning about Dependencies
Sections 16.6 and 16.7 are optional. These topics will be covered later in depth.
Furthermore, please read Deep Learning Book - Chapter 17: Monte Carlo Methods with special emphasis on these sections:
17.1 Sampling and Monte Carlo Methods (for introduction)
17.3 Markov Chain Monte Carlo Methods
17.4 Gibbs Sampling
Deadline for questions to be considered in class is January 7, 7am. I will also try to accommodate things that come in later but I cannot make guarantees. The earlier you bring up questions, the better.
We will spend about 50% of the time in class for discussing the reading assignments. This will leave less time for the course project. Therefore, if you would like to present and/or discuss progress on a specific aspect of the project, please prepare accordingly. Also, please send an email with the topic and the approximate amount of time required until January 9, 10am.