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.