Driven by recent breakthroughs, rapidly growing collections of data, and a plethora of exciting applications, artificial intelligence is experiencing massive interest and investment from both the academic and industrial scene.

This course selects a number of advanced topics to explore in machine learning and autonomous agents, in particular:

  • Probabilistic graphical models (Bayesian networks, ...)
  • Multi-output and structured-output prediction problems
  • Deep-learning architectures
  • Methods of search and optimization (Beam search, epsilon-approximate search, stochastic optimization, Monte Carlo methods, ...)
  • Sequential prediction and decision making (HMMs, Sequential Monte Carlo, Bayesian Filtering, MDPs, ...)
  • Reinforcement learning (Q-Learning, Deep Q-Learning, ...)

Although these topics are diverse and extensive, this course is developed around a common thread connecting them all, such that each topic builds off the others.

Lectures will cover the relevant theory, and labs will familiarize the students with these topics from a practical point of view. Several of the lab assignments will be graded, and a team project on reinforcement learning will form a major component of the grade - where the goal is to developing and deploy an agent in an environment and write a report analyzing the results.