Courses Offered

COMP3314 Machine Learning

2020-21

Instructor(s):

Schnieders Dirk

(Class A)

No. of credit(s):

Yu Y Z

(Class B)

Recommended Learning Hours:

Lecture:	34.0
Tutorial:	5.0

Pre-requisite(s):

COMP2119 or ELEC2543 or FITE2000; and MATH1853 or MATH2101

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 a	PLO c	PLO j
CLO 1	T		T
CLO 2	T,P	T,P
CLO 3	P	P

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 AI	1
Curve fitting	1, 2
Decision theory	1, 2
Supervised learning	Mapped to CLOs
Linear classifiers	2, 3
Logistic regression	2, 3
Neural networks	2, 3
Unsupervised learning	Mapped to CLOs
Clustering	2, 3
Mixture models	2, 3
Principal components analysis	2, 3
Advanced topics and applications	Mapped to CLOs
Gaussian processes	2, 3
Applications	1, 2

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

Teaching Plan

Please refer to the corresponding Moodle course.

Moodle Course(s)

Courses Offered

COMP3314 Machine Learning

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