A survey of Machine Learning techniques, their underlying theory, and their application to realistic data. Machine learning techniques may include Neural Networks, Support Vector Machines, Bayesian networks, Hidden Markov Models, Particle Filtering; Expectation-Maximization; Sampling; Evaluation methodologies; Over-fitting and Regularization. Software tools will be introduced for practical application. Weekly hours: 3 Lecture hoursPrerequisite(s): CMPT 317.3; one of STAT 242.3 (preferred) or STAT 245.3 or EE 216.3; and MATH 164.3. Note: Costs in addition to tuition apply to this course.
A survey of Machine Learning techniques, their underlying theory, and their application to realistic data. Machine learning techniques may include Neural Networks, Support Vector Machines, Bayesian networks, Hidden Markov Models, Particle Filtering; Expectation-Maximization; Sampling; Evaluation methodologies; Over-fitting and Regularization. Software tools will be introduced for practical application. Weekly hours: 3 Lecture hoursPrerequisite(s): CMPT 317.3; one of STAT 242.3 (preferred) or STAT 245.3 or EE 216.3; and MATH 164.3. Note: Costs in addition to tuition apply to this course.
