Courses Offered

COMP3314 Machine Learning

2024-25

Instructor(s):

Zhao Hengshuang

(Class A)

No. of credit(s):

Zhao Hengshuang

(Class B)

Yu Y Z

(Class C)

Recommended Learning Hours:

Lecture:	34.0
Tutorial:	5.0

Pre-requisite(s):

COMP2119 or COMP2502 or ELEC2543 or FITE2000; and MATH1013 or MATH1853 or MATH2014

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:
This course introduces algorithms, tools, practices, and applications of machine learning. Topics include core methods such as supervised learning (classification and regression), unsupervised learning (clustering, principal component analysis), Bayesian estimation, neural networks; common practices in data pre-processing, hyper-parameter tuning, and model evaluation; tools/libraries/APIs such as scikit-learn, Theano/Keras, and multi/many-core CPU/GPU programming.

Detailed Description:

Introduction	Mapped to CLOs
Principles of data-driven systems and AI	1
Decision theory	1, 2
Supervised learning	Mapped to CLOs
Dataset assessment and pre-processing	2, 3
Linear classifiers	2, 3
Logistic regression	2, 3
Neural networks	2, 3
Performance evaluation and tuning	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
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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