2016/2017, Semester 1
School of Computing (Computer Science)
Modular Credits: 4
This module introduces basic concepts and algorithms in machine learning and neural networks. The main reason for studying computational learning is to make better use of powerful computers to learn knowledge (or regularities) from the raw data. The ultimate objective is to build self-learning systems to relieve human from some of already-too-many programming tasks. At the end of the course, students are expected to be familiar with the theories and paradigms of computational learning, and capable of implementing basic learning systems.
(CS2010 or its equivalent) and (ST1232 or ST2131 or ST2132 or ST2334)
2 hour in-class lecture, with 10 weekly tutorials (starting from Week 3). Tutorials should be attempted by students in advance of the tutorial itself, as the class will be discussing solutions rather than deriving the answers from scratch.
The course will cover three main topics:
When can machines learn?
How can machines learn? (the practical side)
Why can machines learn? (the theoretical side)
Workload Components : A-B-C-D-E
A: no. of lecture hours per week
B: no. of tutorial hours per week
C: no. of lab 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