Credit units: 3
Offered: Either Term 1 or Term 2
Weekly hours: 3 Lecture hours
College: Graduate and Postdoc Studies
Department: Computer Science
This course explores advanced techniques for management and analysis of data in unstructured application environments. Techniques covered may be chosen from the following: Bayesian modelling, data conditioning, machine learning (Bayesian inference, neural networks, decision trees, classifiers), user interface agents, and other similar techniques in the AI research literature as appropriate.
Prerequisite(s): Open to graduate students in computer science who have at least one undergraduate course (3 credit units) of Artificial Intelligence.
Upcoming class offerings
Examples of current or recently-offered class syllabus material can be found on the Open CourseWare website.
The syllabus is a public document that provides detail about a class, such as the schedule of activities, learning outcomes, and weighting of assignments and examinations. Please note that the examples provided in Open CourseWare do not represent a complete set of current or previous syllabus material. Rather, they are presented solely for the purpose of indicating what may be required for a given class.
For more information about syllabi, visit the Academic Courses Policy.