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Module Detailed Information for [DSA4212]
Academic Year : 2017/2018 Semester : 1
Correct as at 30 Sep 2017 13:30

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Module Information
Module Code :
Module Title : Optimisation for Large-Scale Data-Driven Inference
Module Description : Computational optimisation is ubiquitous in statistical learning and machine learning. The module covers several current and advanced topics in optimisation, with an emphasis on efficient algorithms for solving large scale data-driven inference problems. Topics include first and second order methods, stochastic gradient type approaches and duality principles. Many relevant examples in statistical learning and machine learning will be covered in detail. The algorithms will be implemented using the Python programming language.
Module Examinable : -
Exam Date : 04-12-2017 PM
Modular Credits : 4
Pre-requisite : MA1101R and {MA1104 or MA2311} and ST2132
Preclusion : Nil
Module Workload (A-B-C-D-E)* : 3-1-0-3-3
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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