Computer Science

The course covers elementary programming concepts (arithmetic expressions, for-loops, logical expressions, if-statements, functions and classes) that are closely connected to mathematical/technical problems and examples, as well as mathematical manipulations (setting up matrices, solving linear problems, solving differential equations, finding roots). 

This course provides a study of the design principles and the development of applications for mobile terminals. Topics are divided in three blocks: operating systems for mobile devices and general concepts of development of mobile applications; development of mobile applications; design and implementation of a mobile application. Students are expected to have completed coursework in systems architecture, multimedia applications, programming and systems programming.

This course covers the general ideas and methods of solving problems with computers, to master the basic processes and methods of programming, and to use Python as a tool for analysis and processing large data. This course teaches the current popular Python language. Python is very suitable as the first programming language for non-computer science majors. Learning content includes: Python sequences: lists, tuples, dictionaries, collections, control structures: selection, looping, string common operation methods, string functions, regular expressions, function definition and usage, module definition and usage, file operations, exception handling, etc., and basic understanding of graphical interface and drawing.

This course addresses the effective use of cloud technology in digital cultural and creative projects including: versatile digital publishing, website design, web community development, digital imaging, animation, video and media production management. As a specific objective, we emphasize to cap off a team project more than an individual task. This course takes account of the inter-discipline of humanities and information technology through media application. The learning method focuses on problem solving oriented approach. The learning activity designates a hands-on assignment and requires a complete output in the form of publishable presentation.

This course covers the application of deep learning and how to implement it in practice. It explores the background of machine learning, neural network for deep learning, and includes explanations of the basic structure of deep learning and advanced deep learning structure. Students learn real-world problems ranging from computer vision to natural-language processing.

Prerequisite: Basic Python Programming

This course introduces the design, development and debugging of parallel programs. It builds on the concurrency concepts gained from the Operating Systems course. It covers concepts and modelling tools for specifying and reasoning (about the properties of) concurrent systems and parallel programs. It also covers principles of performance analysis, asynchronous and asynchronous parallel programming, and engineering concurrent systems and parallel programs. This course requires prerequisites. 

The course establishes a foundation for understanding and analyzing information and information systems in organizations. It provides an overview of technical and organizational aspects of decision support systems (DSS), including individual, group, and organizational DSS as well as executive information systems (EIS). Management of DSS and EIS within the end-user computing environment are also discussed. The course covers more recent technologies, including Data Warehouse / Data Mining, and OLAP technologies. The course is design-oriented and emphasizes conceptual foundations of DSS and EIS, but DSS software reviews, demonstrations, laboratory lessons, and case examples are also included.

As data are accumulating at an incredible rate due to a host of technological advances, electronic data capture has become inexpensive and ubiquitous. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these data. A number of successful applications have been reported in many areas. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This course examines several methods that have emerged from both fields and proven to be of value in recognizing patterns and making predictions from an application's perspective. We survey applications and provide an opportunity for hands-on experimentation with algorithms for data mining using easy-to- use software and cases.