Course Information
COMP3314 Machine Learning

COMP3314 Machine Learning

2017-18
Instructor(s):Lau Francis
(Class A) No. of credit(s):6
Recommended Learning Hours:
Lecture: 26.0
Tutorial: 13.0
Pre-requisite(s):COMP2119 or CSIS1119 or ELEC1502 or ELEC1503 or ELEC2543; and MATH1853
Co-requisite(s):  
Mutually exclusive with:  
Remarks:

Course Learning Outcomes

1. [1]
understand the motivations and principles for building adaptive systems based on empirical data, and how machine learning relates to the broader field of artificial intelligence
2. [2]
formulate problems associated with domain specific data (e.g., image classification, document clustering) in terms of abstract models of machine learning
3. [3]
implement solutions to machine learning problems using tools such as Matlab or Octave, apply numerical optimization algorithms
Mapping from Course Learning Outcomes to Programme Learning Outcomes
 PLO aPLO bPLO cPLO dPLO ePLO fPLO gPLO hPLO iPLO j
CLO 1TT
CLO 2T,PT,P
CLO 3PP

T - Teach, P - Practice
For BEng(CompSc) Programme Learning Outcomes, please refer to here.

Syllabus

Calendar Entry:
An introduction to algorithms and applications of machine learning. Topics include: decision theory; parametric models; supervised learning (classification and regression); unsupervised learning (clustering, mixture models, principal component analysis); Bayesian methods.

Detailed Description:

Introduction Mapped to CLOs
Principles of data-driven systems and AI1
Curve fitting1, 2
Decision theory1, 2
Supervised learning Mapped to CLOs
Linear classifiers2, 3
Logistic regression2, 3
Neural networks2, 3
Unsupervised learning Mapped to CLOs
Clustering2, 3
Mixture models2, 3
Principal components analysis2, 3
Advanced topics and applications Mapped to CLOs
Gaussian processes2, 3
Applications1, 2

Assessment:
Continuous Assessment: 50%
Written Examination: 50%

Teaching Plan

Please refer to the corresponding Moodle course.

Moodle Course(s)