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Module Detailed Information for [CS5339]
Academic Year : 2018/2019 Semester : 2
Correct as at 21 Feb 2019 05:00

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Module Information
Module Code :
Module Title : Theory and Algorithms for Machine Learning
Module Description : The module aims to provide a broad theoretical understanding of machine learning and how the theory guides the development of algorithms and applications. Topics covered include the approximation capabilities of common function classes used for machine learning, such as decision trees, neural networks, and support vector machines, the sample complexity of learning different function classes and methods of reducing the estimation error such as regularization and model selection, and computational methods used for learning such as convex optimization, greedy methods, and stochastic gradient descent.
Module Examinable : -
Exam Date : 04-05-2019 PM
Modular Credits : 4
Pre-requisite : CS3244
Preclusion : Nil
Module Workload (A-B-C-D-E)* : 3-0-0-4-3
Remarks : Nil
* A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of laboratory hours per week
D: no. of hours for projects, assignments, fieldwork etc per week
E: no. of hours for preparatory work by a student per week

Lecture Time Table
Class TypeWeek TypeWeek DayStartEndRoom

Tutorial Time Table
Attention: The tutorial timetables could be updated from time to time. Students are advised to check regularly for the latest update on the change of tutorial timing.
No Tutorial Class or to be announced. Please check with the department offering this module.

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