Project Category Author Description
Data Mining Computer Science Robin Murray Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations such as the internet, e-commerce, electronic banking, point-of-sale devices, bar-code readers, and intelligent machines. Such data is often stored in data warehouses and data marts specifically intended for management decision support. Data mining is a rapidly growing field that is concerned with developing techniques to assist managers to make intelligent use of these repositories.
Knowledge-Based Applications Systems Computer Science Andrew Garza This course covers the development of programs containing a significant amount of knowledge about their application domain. The course includes a brief review of relevant AI techniques; case studies from a number of application domains, chosen to illustrate principles of system development; a discussion of technical issues encountered in building a system, including selection of knowledge representation, knowledge acquisition, etc.; and a discussion of current and future research.
Quantum Computation Computer Science Jason Arnold This course provides an introduction to the theory and practice of quantum computation. Topics covered include: physics of information processing, quantum logic, quantum algorithms including Shor's factoring algorithm and Grover's search algorithm, quantum error correction, quantum communication, and cryptography.
Common Sense Reasoning for Interactive Applications Computer Science Dorothy Garcia This course will explore the state of the art in common sense knowledge, and class projects will design and build interfaces that can exploit this knowledge to make more usable and helpful interfaces.
Ambient Intelligence Computer Science Dorothy Garcia This course will provide an overview of a new vision for Human-Computer Interaction (HCI) in which people are surrounded by intelligent and intuitive interfaces embedded in the everyday objects around them.
Statistical Learning Theory and Applications Computer Science Annie Martin Focuses on the problem of supervised learning from the perspective of modern statistical learning theory starting with the theory of multivariate function approximation from sparse data. Develops basic tools such as Regularization including Support Vector Machines for regression and classification.
Advanced Natural Language Processing Computer Science Raymond Ruiz This is a laboratory-oriented course on the theory and practice of building computer systems for human language processing, with an emphasis on the linguistic, cognitive, and engineering foundations for understanding their design.
Techniques in Artificial Intelligence Computer Science Raymond Ruiz This is a graduate-level introduction to artificial intelligence. Topics covered include: representation and inference in first-order logic, modern deterministic and decision-theoretic planning techniques, basic supervised learning methods, and Bayesian network inference and learning.
Machine Vision Computer Science Ryzal Yusoff Machine Vision provides an intensive introduction to the process of generating a symbolic description of an environment from an image. Lectures describe the physics of image formation, motion vision, and recovering shapes from shading. Binary image processing and filtering are presented as preprocessing steps. Further topics include photogrammetry, object representation alignment, analog VLSI and computational vision. Applications to robotics and intelligent machine interaction are discussed.
Agile & Test Driven Development Computer Science Ryzal Yusoff Theory – Learning about agile principles – Understanding how organizaFonal factors affect the development process – Looking at evidence for the efficacy of agile and testdriven development • PracFce – Applying agile principles in team project – Using test-driven development and scrum – Developing an agile mindset
Pattern Based Software Development Accounting and Finance Ryzal Yusoff The course aim is to introduce three types of software pattern:process patterns, analysis patterns and design patterns and to provide a systematic understanding of these patterns through the lectures and coursework.
Semi Structured Data and The Web Computer Science Ryzal Yusoff Semi-structured data is a form of structured data that does not conform with the formal structure of data models associated with relational databases or other forms of data tables, but nonetheless contains tags or other markers to separate semantic elements and enforce hierarchies of records and fields within the data. Therefore, it is also known as self-describing structure. In semi-structured data, the entities belonging to the same class may have different attributes even though they are grouped together, and the attributes' order is not important.

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