The course aims to develop skills in reading, understanding and writing a document critically, and the ability of discussing rationally. The objective of the course is to introduce the design of programs, understanding man pages, debugging programs, interpreting specifications, writing content-structured documents and how to ask smart questions. This course includes three parts: Meta-Skills (MS), How to Design Programs (HtDP) and Discrete Mathematics (DM)

This course covers the basic science behind the use of computers to provide effective and efficient methods for carrying out tasks. Tasks examined include data storage and retrieval, sorting and searching, semi-numerical tasks such as encryption, planning and optimization tasks, problems, space searches and games playing. To carry out these tasks, both algorithms and structures for the storage of data need to be specified. Mathematical tools have to be developed that enable us to measure the fundamental effectiveness of algorithms and in particular the way these algorithms scale as the size of the task being performed increases. This course introduces the basic sorting and searching methods and dynamic data structures such as linked lists, trees and hash tables.

This course builds on COMP-231 to apply similar techniques to analyse more sophisticated algorithms and data structures. Topics covered include graphs and graph algorithms, greedy algorithms, random algorithms, divide and conquer algorithms, dynamic programming and string searching algorithms.

This course is about the time and frequency domain analysis of signals and systems. Periodic signals, Fourier transforms, DFT, convolution integral, filters, transmission of information by orthogonal functions, analysis of discrete time signals and systems are the highlighted topics. The course is designed to familiarize students with the techniques for analyzing and synthesizing continuous-time and discrete-time systems as well as utilizing simulation tools such as Matlab and simulink.

This course builds on COMP 261(Object Oriented Programming) to move on to the techniques involved in producing larger computer systems in an object-oriented programming environment. Subjects covered will include various types of layered and modularized implementation, code reusability and implementations involving more than one execution environment.

Operating systems play a fundamental role in the use of computers and are of enormous commercial importance. This course looks at how they work. Contents include, classification and structure of operating systems, storage media, memory management and dynamic storage strategies, scheduling algorithms, I/O and interrupt structures, protection and security, queuing and network control models, systems software, linkers, loaders, assemblers, translators and programming environments, case studies of existing operating systems and implementation of operating system modules.

This course examines from first principles the nature of computer programs. Starting from first principles, students will investigate different paradigms for the construction and evaluation of computer programs.

Due to recent progress in the computer field, digital pictures are all around us; computing with image is no longer just for the realm of the science, but also for the arts and social sciences and even for hobbyists. By the end of the course, the enthusiastic student will be able to perform image acquisition, enhancement, restoration, wavelet transformation, compression, segmentation and objects recognition.

Machine learning concerns the construction of systems that can learn from data. We need learning every time we cannot directly write a computer program to solve a given task; e.g. programs that learn to recognize human faces, to digitalize an acoustic speech signal and/or written carachters, and to drive autonomous robots. This course covers the theory and practical algorithms for machine learning focusing on classification and dimensionality reduction.

The implementation of compilers has enabled the production of increasingly large computer systems. Compilers and assemblers play a crucial role and at the same time are good examples of large and complex programs that have at least in part a sound mathematical basis for the way they work. This course studies the different phases of compiler construction and engineering and scientific issue in each stage.

This course aims to study the basic concepts of artificial intelligence. Topics covered include representation of knowledge, search and heuristic programming, logic and logic programming, application areas of artificial intelligence: problem-solving, game and puzzles, expert systems, planning, natural language understanding and learning.

Programs running on one computer to provide services to users or computers elsewhere are becoming increasingly important. A number of programming languages and programming environments have evolved specifically to address this need. This course looks at the issues involved in writing service-providing programs, using the latest programming languages and technology.