Spring 2018 CS Course Listings

This file contains the Spring 2018 course listings for the Department of Computer Science and Engineering.

Archive of past courses


Course code: COMP5111
Course title: Fundamentals of Software Analysis
Instructor: Shing-Chi Cheung
Room: 2534
Email:
WWW Page:

Area in which course can be counted: ST

Course description:
The goal of this course is to introduce how various analysis techniques can be used to manage the quality of a software application. Students will acquire fundamental knowledge of program abstraction, features, verification, testing, refactoring, concurrency, reliability, aspect orientation, and fault analysis. The course will also discuss how to carry out the empirical experimentation for program analysis. Wherever applicable, concepts will be complemented by tools developed in academia and industry. This enables students to understand the maturity and limitations of various analysis techniques.
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):
Program Analysis, Testing, Fault Localization, Design Patterns, Concurrency, Empirical Experimentation
Textbooks:

Reference books/materials:
* Conferences: Proceedings of ICSE, FSE, PLDI, OOPSLA, ISSTA and ASE.
* Journals: ACM TOSEM & IEEE TSE.
* Software Engineering, Ivan Marsic, Rutgers University, 2012.(Download here)
* Introduction to Software Testing, Paul Ammann and Jeff Offutt, Cambridge University Press, 2008.
* Software Testing and Analysis: Process, Principles and Techniques, Mauro Pezze and Michal Young, John Wiley and Sons, 2007.
* How Google Tests Software, James A. Whittaker, Jason Arbon, Jeff Carollo, Addison-Wesley, 2012.
* Head First Java, Kathy Sierra and Bert Bates, O'Reilly Media, Inc.
* Head First Design Patterns, Eric Freeman, Elisabeth Robson, Bert Bates, Kathy Sierra, O'Reilly Media, Inc.
* Design Patterns, Enrich Gamma, et al, Addison-Wesley, 1995.


Grading scheme:
Class Participation: 5%
Assignments: 40%
Reading Report, Presentation & Participation: 15%
Final Exam: 40%

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor


Course code: COMP5212
Course title: Machine Learning
Instructor: Dit-Yan Yeung
Room: 3531
Telephone: 2358-7009
Email:
WWW Page: http://www.cse.ust.hk/faculty/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 experiment 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.
* Ian Goodfellow, Yoshua Bengio, and Aaron Courville (2016). Deep Learning. 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: COMP5221
Course title: Natural Language Processing
Instructor: Dekai Wu
Room: 3556
Telephone: 2358-6989
Email:
WWW Page:
(TBA)
Area in which course can be counted: AI

Course description:
Techniques for parsing, interpretation, context modeling, plan recognition, generation. Emphasis on statistical approaches, neuropsychological and linguistic constraints, large text corpora. Applications include machine translation, dialogue systems, cognitive modeling, and knowledge acquisition.
Background: COMP 3211

Exclusion(s): COMP 4221

Course objective:
(TBA)
Course outline/content (by major topics):
(TBA)
Textbooks:
(TBA)
Reference books/materials:
(TBA)
Grading scheme:
30% - 3 to 4 assignments
5% - participation (in class and on forum)
15% - midterm
20% - final
30% - project

Available for final year UG students to enroll: Yes.

Minimum CGA required for UG students: Permission of instructor


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.
Exclusion(s): 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: CK Tang
Room: 3538
Telephone: 2358-8775
Email: WWW Page:

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: COMP3711 knowledge in Algorithms

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://www.cse.ust.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 multimedia networking; P2P networking for content delivery; wireless networking and 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) Multimedia Networking
3) P2P Networking for content delivery
4) General wireless networking and the advanced topics for wireless networking
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: COMP5712
Course title: Introduction to Combinatorial Optimization
Instructor: Sunil Arya
Room: 3514
Telephone: 2358-8769
Email:
WWW Page: http://www.cse.ust.hk/~arya

Area in which course can be counted: TH

Course description:
* An introduction to the basic tools of Combinatorial Optimization.
* Includes: Linear Programming, Matching, Network Flow, Approximation Algorithms.

Background: COMP3711 or equivalent + Linear Algebra

Course objective:
Upon completion of this course students will have been introduced to many of the most basic tools of combinatorial optimization and will be able to apply them towards designing efficient algorithms in their own research domains.

Course outline/content (by major topics):

Textbooks:

Reference books/materials:
Jiri Matousek and Bernd Gartner. Understanding and using linear programming, Springer, 2006.
Vijay Vazirani. Approximation algorithms, Springer, 2001.
David P. Williamson and David B. Shmoys. The design of approximation algorithms, Cambridge University Press, 2011
Jon Kleinberg and Eva Tardos. Algorithm design, Pearson/Addison-Wesley, 2006.

Grading scheme:
Homeworks, midterm and final examination.

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: Permission of the instructor.


Course code: COMP6211B
Course title: Topics in Statistical Learning for Text Data Analytics
Instructor: Yangqiu SONG
Room: 3518
Telephone: 2358-6987
Email:
WWW Page: http://www.cse.ust.hk/~yqsong

Area in which course can be counted: AI

Course description:
Statistical machine learning has been widely used in natural language processing. Instead of considering general-purpose data processing, natural language data is discrete, symbolic, noisy, ambiguous, and large scale. These characters raise challenges for machine learning algorithms to handle natural language processing. This course will provide an overview of key challenges and typical modeling principles that have been developed in the past decades to handle the challenges. This course is a postgraduate-level introductory course, which will include fundamental algorithms and solutions. It will also provide some discussion of open problems that are still not solved in the whole community to inspire new research topics.
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:
Demonstrate machine learning algorithm design skills for NLP tasks;
Analyze the quality of NLP results to domain problems;
Develop a program that can handle existing real problems

Course outline/content (by major topics):
Representation: language models, word embeddings, topic models;
Learning: supervised learning, semi-supervised learning, sequence models, deep learning, optimization techniques;
Inference: constrained modeling, joint inference, search algorithms.

Reference books/materials:
Jurafsky and Martin (2008), Speech and Language Processing, 2nd edition.
Kevin P. Murphy (2012). Machine Learning: A Probabilistic Perspective. 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.

Grading scheme:
50% homework+paper reading, 50% projects

Available for final year UG students to enroll: Yes

Minimum CGA required for UG students: 3.7


Course code: COMP6211C
Course title: Robotic Perception and Learning
Instructor: Prof. Ming LIU
Room: CYT 2011
Telephone: 2358-7058
Email:
WWW Page: http://www.ram-lab.com

Area in which course can be counted: AI

Course description:
This course introduces the essential theoretical frameworks, methods, concepts, tools and techniques used to enable robotic perception and behavior, with particular emphasis on applications in autonomous mobile robots. The course starts from Bayesian programming and probabilistic methods, and then moves on to cover generic machine learning, especially deep learning. It also includes coverage of reinforcement learning. Important libraries for hand-on experiments for mobile robotic systems will be introduced. The students will have the opportunity to test their algorithms and implementations on real platforms.

Background:
Students are expected to have basic concepts in probability and linear algebra.
Course objective:
This course intends to deliver the state-of-the-art analysis and design tools for building a typical mobile robotic system. Students, after taking this course, are expected to know the basic concepts of machine learning, deep learning, sensor perception, and design tools for building a control system with desired performance and intelligence level.

Course outline/content (by major topics):
Week 1: Bayesian programming and probabilistic models
Week 2: Graphical Model and HMM
Week 3: Dynamic Bayesian Model and online filtering
Week 4: Kernel Methods and Computer Vision - classification
Week 5: Kernel Methods and Computer Vision - general methods
Week 6: Gaussian Process and finger-print-based modeling
Week 7: Sampling and filtering
Week 8: Supervised and Unsupervised Learning
Week 9: Point-cloud perception and representation
Week 10: Reinforcement Learning
Week 11: Deep Learning and convolutional networks
Week 12: Deep Reinforcement Learning
Week 13: Robotic Challenges (final project)

Reference books/materials:
J1. Sebastian Thurn, Probabilistic Robotics, Published in Communications of the ACM, Volume 45 Issue 3, 2002.
2. Christopher Bishop, Pattern Recognition and Machine Learning, Published by Springer, 2007.
3. Richard Hartley and Andrew Zisserman, Muliview Geometry in Computer Vision, Published by Cambridge University Press, 2000.

Grading scheme:
Homework: 3 sets each weights 10%
Mid-term exam: 20%
Final exam: 40%
Final project: 10%

Available for final year UG students to enroll: N/A

Minimum CGA required for UG students: N/A


Please visit Class Schedule & Quota (Spring 2018) for the timetable and quota.


Archive of past courses

Last modified by Xuanwu YUE on 2018-01-15.