teaching

classes, workshops, and teaching materials

Carnegie Mellon University

Probabilistic Graphical Models
Spring 2020: Guest Lecturer
10-708
  • Lecture 19: Reinforcement Learning as Inference in GM (part 1)
  • Lecture 20: Reinforcement Learning as Inference in GM (part 2)
  • Lecture 27: Elements of Meta-Learning
Probabilistic Graphical Models
Spring 2019: Head TA & Co-lecturer
10-708
  • Lecture 6: Parameter learning in partially observed BNs
  • Lecture 20: Sequential decision making (part 1): The framework
  • Lecture 21: Sequential decision making (part 2): The algorithms
  • Lecture 23: Bayesian non-parameterics
  • Lecture 26: Gaussian processes (GPs) and elements of meta-learning
Probabilistic Graphical Models
Spring 2017: TA & Guest Lecturer
10-708
  • Homeworks and recitations
    link link 
  • Lecture on Graphical Models and Deep Learning
Topics in Deep Learning
Fall 2016: TA
10-807


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