Computer Science

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.

This course examines the http protocol and its add ons. It covers relevant technologies and programming languages such as (X)HTML, CSS, JavaScript and framework; the development of the world wide web from a historical context; and technical aspects of the world wide web.

This course examines the intricacies of Android Mobile Application development. Throughout the course students must program a number of smaller tutorial applications, concurrently with a larger course long programming project, an application of their own design. In parallel with these programming assignments, students learn details of Android application design through tutorials and lectures.

The course provides a background on the fundamentals of cryptography for those interested in carrying out cryptographic research or employing cryptographic security. Course topics include classical cryptography, Shannon's theory of secrecy, shift register theory and stream ciphers, public key cryptography, authentication codes, and secret sharing. Students must also complete a course project.