The notes written by students and edited by instructors
Lecture 28: A Civil Engineering Perspective on AI
Lecture 27: Scalable Algorithms and Systems for Learning, Inference, and Prediction
Notes for Lecture 27
Lecture 26: Gaussian processes (GPs) and elements of meta-learning
GPs, kernel functions, (Deep) kernel learning and approximations, NPs, and meta-learning
Lecture 25: Spectral Learning for Graphical Models
Overview of the spectral learning for graphical models.
Lecture 24: Integrative Paradigms of GM: Regularized Bayesian Methods
Regularized Bayesian Methods and some applications.
Lecture 23: Bayesian non-parametrics (continued)
Overview of inference in Dirichlet processes, topic models and the hierarchical Dirichlet process, and infinite latent variable models.
Lecture 22: Bayesian non-parametrics
An introduction to Bayesian non-parametrics and the Dirichlet process.
Lecture 21: Sequential decision making (part 2): The algorithms
An introduction for max-entropy RL algorithms
Lecture 20: Reinforcement Learning & Control Through Inference in GM
Casting reinforcement learning as inference in a probabilistic graphical model.
Lecture 19: Case Study: Text Generation
Introduction to text generation as a case study for deep learning and generative modeling.