Introduction to Artificial Intelligence & Machine Learning
(AI D245e)

The main goal of artificial intelligence (AI) and machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems that analyze past sales data to predict customer behaviour (financial management), recognize faces or spoken speech, optimize robot behaviour so that a task can be completed using minimum resources, and extract knowledge from bioinformatics data. Introduction to AI and Machine Learning is a comprehensive course on the subject, covering topics not usually included in introductory machine learning. It discusses AI methods based in different fields, including neural networks, signal processing, control, and data mining, in order to present a unified treatment of machine learning problems and solutions. All basic learning algorithms are explained so that the student can easily move from the equations to a computer program, such as BPNET or MATLAB. After an introduction that defines machine learning and AI paradigms, the course covers clustering, decision trees, supervised learning, competitive learning, reinforcement learning, multilayer perceptrons, and software tools for simulation of neural networks.