The course gives 6 CFU and can be attended by any student enrolled in the Master degrees in Artificial Intelligence and Robotics, Computer Science and Control Engineering.

The goal of the course is to present techniques and tools for machine learning in complex dynamic systems and autonomous agents. In particular, the course will describe probabilistic models for representing dynamic systems and autonomous agents, reinforcement learning techniques, learning in graphical models, state estimation techniques. The course will also present many examples of successful application of Machine Learning algorithms in different application scenarios.

At the end of the course the student will be able to use the addressed techniques and tools in modeling and solving learning problems for complex dynamic systems. Students will gain the capability of solving complex learning problems with dynamic systems, by devising a proper formulation of the problem, performing adequate design and implementation choices, designing and executing effective experiments to evaluate the results obtained.

Syllabus

1. Introduction

• Typical Problems for robotic applications
• Basics of probabilities and linear algebra

1. Models of dynamic systems

• General concepts
• Model taxonomy
• Markov Decision Processes
• Hidden Markov Models
• Dynamic Bayesian Networks
• Probabilistic Graphical Models

• Q-Learning algorithm
• Non-deterministic algorithms

• Learning on DBN and HMM
• Estimating CPD from supervised data sets

• Discrete filters (forward)
• Particle filters

• Discrete smoother (backward)

• Viterbi algorithm
• Methods based on least square