Example courses

• Algorithms and Data Structures

  This course covers a range of algorithms and data structures building on the fundamental structures and algorithms from COMP 103. The major areas covered are: graph algorithms, graphics algorithms and advanced data structures. This course takes a practical approach focusing on the implementation of a wide variety of algorithms.

• Data Mining, Neural Networks and Genetic Programming

  This course is concerned with data mining concepts and techniques, especially neural networks and genetic programming. It mainly focuses on the following topics: data mining and knowledge discovery in databases; data mining techniques such as nearest neighbour, naive Bayes, support vector machines, neural networks, genetic algorithms and genetic (automatic) programming; image analysis operations such as feature extraction and image recognition; and performance evaluation of data mining/ machine learning/image recognition systems. The course considers applications ranging from general classification, clustering and optimisation tasks to engineering applications (Being replaced by AIML 426 from 2021).

• Design and Analysis of Algorithms

  This course examines techniques for developing correct and efficient algorithms for some important classes of problems in Computer Science. It explores methods for designing algorithms, including greedy algorithms, divide and conquer, dynamic programming and graph algorithms. It covers techniques for demonstrating the correctness of algorithms and for analysing their efficiency.

• Discrete Mathematics and Logic

  Logic underlies all of mathematics. This course introduces the basic notions of logic and discusses what makes some arguments good or valid, and others invalid. This leads to a definition of a mathematical proof, whereby the truth of mathematical statements is guaranteed. Other topics include sets, relations, functions, elementary counting principles, and an introduction to number theory. The second half of the course introduces the fundamental concepts of graph theory, which is the study of networks, which have applications from computing to disease transmission.

• Introduction to Data Structures and Algorithms

  This course focuses on the techniques for designing, building and analysing computer programs that deal with large collections of data. The course addresses techniques for programming with collections of data, and the data structures and algorithms needed to implement these collections. The course expands programming skills and provides an understanding of the principles of data abstraction, algorithm design, and the analysis of algorithms fundamental to computer science.

• Security Engineering

  The Internet's role as a large, public, distributed system has raised security to an issue of critical importance. This course examines security mechanisms, security policies, security evaluation and risk management, security issues in networks and operating systems, and case studies that show how these security techniques can be used to solve real- world problems such as conducting trustworthy auctions and secret ballots.