CSCI 4050U – Machine Learning, Theory and Application

Machine learning is a branch of Computer Science that enables machines to identify patterns, make predictions and organize data by synthesizing models of the world through learning. In this course, we will cover the theory and application of machine learning. We will provide a survey of the fundamental building blocks of machine learning covering areas such as general probabilistic models and parameter estimation, regression models, statistical data analysis, neural networks and neural computation. We will place special emphasis on the application of the machine learning techniques in data representation, pattern recognition, classification and prediction. Students will gain understanding and working knowledge on a wide range of machine learning algorithms including but not limited to: linear, logistic and auto – regression models; multidimensional scaling and PCA; deep learning with multilayer perceptrons and other neural networks, support vector machines, etc.
Credit hours: 3
Lecture hours: 3
Laboratory hours: 1.5
Prerequisite(s): (CSCI 3070U or equivalent) and (MATH 2050U or equivalent)
Credit restriction(s): INFR 4320U, SOFE 3720U

Experiential learning: Yes