Spring 2016 CS Course Listings

This file contains the Spring 2016 course listings for the Department of Computer Science and Engineering.

Archive of past courses

Course code: COMP5112
Course title: Parallel Programming
Instructor: Qiong Luo
Room: 3554
Telephone: 2358-6995
Email:
WWW Page:

Area in which course can be counted: ST

Course description:
Introduction to parallel computer architectures; principles of parallel algorithm design; shared-memory programming models; message passing programming models used for cluster computing; data-parallel programming models for GPUs; case studies of parallel algorithms, systems, and applications; hands-on experience with writing parallel programs for tasks of interest.
Exclusion(s):
COMP 6111B, COMP 6511A, COMP 6611A
Background:
COMP 3511 AND COMP 3711/COMP 3711H

Course objective:
Students will attain the following on completion of the course:
* knowledge of parallel computer architectures;
* understanding of principles of parallel algorithm design;
* knowledge of shared-memory and distributed-memory programming models;
* hands-on experience writing parallel programs of a task of interest.

Course outline/content (by major topics):
Introduction to parallel computer architectures;
principles of parallel algorithm design;
shared-memory programming models;
message passing programming models used for cluster computing;
data-parallel programming models for GPUs;
case studies of parallel algorithms, systems, and applications.

Textbooks:

Reference books/materials:
* Introduction to Parallel Computing, Second Edition. Ananth Grama, George Karypis, Vipin Kumar, Anshul Gupta. Addison-Wesley, 2003, ISBN: 0201648652.
* Programming Massively Parallel Processors: A Hands-on Approach. Second Edition. David B. Kirk and Wen-mei W. Hwu. Elsevier 2012. ISBN: 978-0124159921.

Grading scheme:
Survey paper and presentation 50%, Project 50%

Available for final year UG students to enroll:

Minimum CGA required for UG students:

Course code: COMP5212
Course title: Machine Learning
Instructor: Dit-Yan Yeung
Room: 3541
Telephone: 2358-6977
Email:
WWW Page: http://home.cse.ust.hk/~dyyeung/

Area in which course can be counted: AI

Course description:
Machine learning is the science of making computer artifacts improve their performance without requiring humans to program their behavior explicitly. Machine learning has accomplished successes in a wide variety of challenging applications, ranging from computational molecular biology to computer vision to social web analysis. This course is a postgraduate-level introductory course in machine learning with emphasis put on the computational and mathematical principles underlying the most common machine learning problems and methods. It is not only suitable for students pursuing or planning to pursue research in machine learning or other related areas that focus on model and algorithm development, but is also suitable for students who want to apply principled machine learning techniques competently to their application-oriented research areas.

Background:
Computer science: object-oriented programming and data structures, design and analysis of algorithms Mathematics: multivariable calculus, linear algebra and matrix analysis, probability and statistics

Course objective:
By the end of this course, students are expected to demonstrate competence in the following:
* Ability to take a real-world application and formulate the learning problems involved in it by identifying the major learning-related issues;
* Ability to choose and apply the most common methods available for each of the major learning problem types;
* Ability to compare different machine learning methods according to common performance criteria;
* Ability to design and conduct empirical studies in such a way that the experimental results can be interpreted in accordance with disciplined scientific and statistical principles.

Course outline/content (by major topics):
Bayesian decision theory
Parameter estimation for generative models
Linear and logistic regression
Feedforward neural networks
Support vector machines
Model assessment and selection
Deep learning models
Recurrent neural networks
Clustering and mixture models
Nearest neighbor classifiers
Decision trees
Dimensionality reduction
Ensemble learning
Matrix factorization
Probabilistic graphical models
Topic models
Hidden Markov models
State space models
Reinforcement learning

Reference books/materials:
* Kevin P. Murphy (2012). Machine Learning: A Probabilistic Perspective. MIT Press.
* Ethem Alpaydin (2014). Introduction to Machine Learning. Third Edition. MIT Press.
* Christopher M. Bishop (2006). Pattern Recognition and Machine Learning. Springer.
* Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2009). The Elements of Statistical Learning. Second Edition. Springer.
* Other assigned reading materials

Grading scheme:
Problem sets (25%) Programming projects (25%) Final exam (50%)

Available for final year UG students to enroll: No

Minimum CGA required for UG students: N/A

Course code: COMP5311
Course title: Database Architecture and Implementation
Instructor: Dimitris Papadias(send e-mail for questions regarding the class and for arranging individual meetings)
Room: 3555
Telephone: 2358-6971
Email:
WWW Page:

Area in which course can be counted: DB

Course description:
Introduction to the relational model and SQL. System architectures and implementation techniques of database management systems: disk and memory management, access methods, implementation of relational operators, query processing and optimization, transaction management and recovery. Hands on experience with building the components of a small DBMS.
Background: COMP 3511

Course objective:
Introductory database class for graduate students. The students are expected to learn basic concepts and implementation techniques of relational databases and advanced RDBMS applications.

Organization:
The course will be divided in two parts:
(i) Background material to be taught by the instructor
(ii) New specialized topics to be presented by students.
Students will form groups and in addition to their presentation, they will have to submit a survey on the topic by the end of the semester.

Background material (by major topics):
* E/R Model
* Relational Model and Algebra
* SQL
* Functional Dependencies and Relational Database Design
* File Systems
* Tree and Hash Indexes
* Query Processing and Implementation of Relational Operators
* Query Optimization
* Transactions

Textbooks:
* Database System Concepts, A. Silberschatz, H. Korth, and S. Sudarshan.

Reference books/materials:
* Database Management Systems, Raghu Ramakrishnan and Johannes Gehrke.

Grading scheme:
50% final exam, 20% presentation, 20% survey, 10% class participation

Available for final year UG students to enroll: No.


Course code: COMP5421
Course title: Computer Vision
Instructor: C K Tang
Room: 3561
Telephone: 8775
Email:
WWW Page: http://cse.hkust.edu.hk/~cktang/

Area in which course can be counted: VG

Course description:
Introduction to techniques for automatically describing visual data and tools for image analysis; perception of spatial organization; models of general purpose vision systems; computational and psychological models of perception.

Background: COMP3211 knowledge in linear algebra.

Course objective:
Same as listed in the course catalogue/academic calendar.

Course outline/content (by major topics):
1 Introduction
2 Image formation
3 Image filtering
4 Edge detection
5 Segmentation
6 Segmentation II
7 Texture
8 Projective geometry (handout)
9 Image warping
10 Stereo
11 Disparity by graph-cut
12 Surface from Stereo (Tensor voting)
13 Multiview stereo
14 Light
15 Photometric stereo
16 Optical flow
17 Structure from Motion

Textbooks:
Computer Vision : A Modern Approach, D. Forsyth and J. Ponce

Reference books/materials:
* Three-Dimensional Computer Vision, O. Faugeras, MIT Press, 1993
* Robot Vision, B.K.P. Horn, MIT Press, 1986 * A Guided Tour of Computer Vision, V. S. Nalwa, Addison Wesley, 1993
* Machine Perception, R. Nevatia, Prentice-Hall, 1982
* Computer Vision, L. G. Shapiro and G. C. Stockman, Prentice-Hall, 2001
* Machine Vision, R. Jain, R. Kasturi, and B.G. Schunck, McGraw-Hill, 1995
* Computer and Robot Vision vol. 2, R. Haralick and L. Shapiro, Addison-Wesley, 1992
* Object Recognition by Computer - The Role of Geometric Constraints, W.E.L.Grimson, MIT Press, 1990
* The Eye, the Brain and the Computer, Fischler and Firschein, Addison-Wesley, 1987
* Computer Vision, D. Ballard and C. Brown, Prentice-Hall, 1982
* Vision, David Marr, Freeman, 1982
* Digital Picture Processing, A. Rosenfeld and A. Kak, Academic Press, 1982


Grading scheme:
The breakdown is subject to change as a whole and adjustments on a per-student basis in exceptional cases.
This is the general breakdown we'll be using for Scheme 1:
Projects: 64%
Homeworks: 4%
Final Exam (Oral): 32%

Grading Scheme 2 targets at students in other research areas who need to fulfil the Vision/Graphics core requirement. The tentative breakdown for students signing up for Scheme 2 is as follow:
Project #1 and Papers Critique: 26%
Homeworks: 4%
Final Exam (Written): 70%

The two schemes will be described during the first and/or second lecture in September.
Computer projects and papers critique will be done in teams up to two students (three-student team is not permitted).
Homeworks are to be completed individually. Though you may discuss the problems with others, your answers must be your own.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A- and permission of the instructor

Course code: COMP5622
Course title: Advanced Computer Communications and Networking
Instructor: Qian Zhang
Room: 3533
Telephone: 2358 8766
Email:
WWW Page: http://cse.hkust.edu.hk/~qianzh

Area in which course can be counted: NE

Course description:
This course discusses the advanced principles in computer and communication networking. More particularly, the following topics will be addressed during this course, including multicast routing in the Internet; P2P networking; advanced topics for wireless networking; wireless sensor and senor networks; introduction to network security and wireless security; advanced topics related to congestion control.

Pre-requisites: COMP361/ELEC315/COMP561

Course objective:
Students taking this course will have a comprehensive training in all advanced and current aspects of computer networking. They will gain a thorough understanding of the theoretical issues, they will understand the basic principles behind some design choices and the will gain experience of some practical systems. They will understand the current evolution of the Internet and the future trends in the development of the field of networking, which will equip them with the necessary background to start their research in any area of networking.

Course outline/content (by major topics):
1) Review of the basic principles of computer networking
2) Broadcasting and Multicasting
3) P2P Networking
4) Advanced topics for wireless networking (multi-channel, multi-hop networks)
5) Wireless sensor and sensor networks
6) Network Security and Wireless Security
7) Advanced topics related to congestion control
8) Student Presentation (paper presentation and idea presentation)

Textbooks:
A collection of papers from journals, conference proceedings, and website need to be read.

Reference books/materials:
TBA

Grading scheme:
Paper and Idea Presentation 30 points
Project Report 30 points
Final Exam 40 points

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: A- and permission of the instructor

Course code: COMP5713
Course title: Computational Geometry
Instructor: Siu-Wing Cheng
Room: 3551
Telephone: 2358-6973
Email:
WWW Page:

Area in which course can be counted: TH

Course description:
An introductory course in Computational Geometry. Algorithms for manipulating geometric objects. Topics include Convex Hulls, Voronoi Diagrams, Point Location, Triangulations, Randomized Algorithms, Point-Line Duality.

Background: COMP 3711

Course objective:
To introduce postgraduate students to the area of computational geometry, the fundamental results and algorithms in the area.

Course outline/content (by major topics):
Basic problems and algorithms in the plane, convex hulls, arrangement and duality, Voronoi and Delaunay diagrams, randomized algorithms, approximation algorithms.

Textbooks:
Computational Geometry: Algorithms and Applications, Second Edition, Springer.

Reference books/materials: TBA

Grading scheme:
30% written assignment, 30% midterm, and 40% final.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: B

Please visit here for the timetable and quota.

Archive of past courses

Last modified by Qing Chen and Bo Liu on 2015-12-29.