Course Syllabus

Instructor: Daniel Brown
Class Room:  BU C (Business Class Room Building) 105
Meeting Times: MoWe/11:50AM - 01:10PM
Professor Office Hours: By appointment (email daniel.s.brown@utah.edu)
Class Website: https://dsbrown1331.github.io/advanced-ai/ Links to an external site.
Teaching Assistants: Connor Mattson
Email: c.mattson@utah.edu
TA Office Hours

Appointment Only via Zoom

This course focuses on advanced algorithms for intelligent sequential decision making with a focus on modern deep learning-based methods. The class will cover both the theory and practical details of the algorithms behind recent breakthroughs in many types of AI decision making, including game playing, robotics, recommendation systems, and large language models. Topics include bandit algorithms, Markov decision processes, partially observable Markov decision processes, reinforcement learning, imitation learning, inverse reinforcement learning, and reinforcement learning from human feedback.

This will be a fun, but challenging class.  It is an advanced AI class so we will assume a basic understanding of machine learning basics (supervised learning, loss functions, gradient descent) and a basic understanding of AI basics (search problems, MDPs, RL high-level ideas). Note that these topics can be picked up during the class as we will try to keep things self-contained, but we will go over basic topics quickly to get to more advanced materials. Students should be comfortable writing Python code and digging through and understanding code written by others. I will try and stretch you and challenge you, but my goal is for everyone to learn lots and get an A in the class.