Advanced computer programmer

Advanced computer programmer

This computer is concerned with the design of reconfigurable computing systems using hardware explanation languages. Topics covered include field programmable gate array architectures computer arithmetic, high speed digital logic, interfacing and case studies. Emphasis will be on how to design high performance digital systems at the algorithmic, system and logic level. Each student is required to implement and test a digital design of moderate complexity. Prerequisite: Fault-Tolerant Computing
Fault tolerance used to be a requirement of computer systems in specialized applications such as spacecraft control and telephone switching. With the advancement of hardware and software technology and the increasing complexity of computer systems, fault tolerance has become a necessity for a wide range of industrial, commercial and even personal applications. Models and methods are used in the analysis and design of faulttolerant and highly reliable computer systems. The topics to be covered by this course include fault/error modeling, reliability analysis, various redundancy techniques, fault-tolerant system design methods, case studies of faulttolerant systems, and current research issues. Structure of sequential machines, linear sequential machines, state machine testing, information losslessness of finite automata, state-identification and fault-detection experiments, finite-state recognizers, sequential circuit retiming techniques, synthesis for combinational and sequential circuits.

Boolean network synthesis, technology mapping, delay analysis, sequential logic synthesis, state minimization, retiming resynthesis, verification, advanced applications using Ordered Binary Decision Diagrams hardware fault testing, and notions of design for testability. Advanced Workshop in Computer Engineering
Advanced Topics in Database Systems :This course will introduce to students advanced topics in database systems including advanced data structures, concurrency control, deadlock resolutions, recovery schemes, distributed database systems, multimedia database indexing techniques, and data mining. This course is intended to provide for senior undergraduate and beginning graduate students a concise survey of the most important and fundamental work on semantics of programming languages of different paradigms, together with the necessary background material from logic, lambda calculus, type theory, domain theory, and category theory. All of the three major approaches to semantics - denotational, operational, and axiomatic - and the relations among them are discussed.
This course will introduce to students advanced topics in operating systems. The detailed contents may be changed from year to year depending on the current development and the teacher specialty.
This course aims to introduce the computational learning theory for applications to various areas of finance. This course consists of two parts. The first part gives an introduction of basic mathematical methods in finance. The second part deals with nonlinear computing models, Bayesian Ying-Yang unified learning theory, other computational learning techniques, and their applications to FOREX or stock forecasting, portfolio optimization and programmed trading.
Deterministic and non-deterministic Turing machine, Church's Thesis, uncomputability and intractability, NP-completeness, polynomial time hierarchy, probabilistic computation, predicate calculus and incompleteness.
Techniques for Data MiningData mining provides useful tools for the analysis, understanding and extraction of useful information from huge databases. These techniques are used in business, finance, medicine and engineering. This course will introduce the techniques used in data mining. Topics will include clustering, classification, estimation, forecasting, statistical analysis and visualization tools.
Artificial Intelligence ProgrammingThis course examines two representational paradigms of artificial intelligence programming. Topics in logic programming include unification, , SLD-resolution, Prolog, negation and control, trees, list, difference list, and programming techniques. Topics in functional programming include function definitions, recursive functions, scope, higher-order functions, programming techniques, and basic lambda calculus. Provide in-depth treatment of the following advanced computer graphics and visualization topics: radiosity rendering and global illumination, procedure texturing and modeling, imagebased rendering, stereo imaging, real-time
Advanced Topics in Multimedia Database:
This course aims at an in-depth study of various aspects in the frontier research of multimedia database systems. These include image processing methods, indexing methods, system design issues, and basis of multimedia data such as compression techniques and resource management. Image processing methods for shape, color, texture manipulation, etc., will be covered. Indexing methods of R-trees, VP-trees, X-tree, etc., will be introduced. This course will provide the students with a theoretical background as well as a hands-on experience in the design and implementation of a multimedia database system.
Advanced Topics in Compiler Construction :
Advanced topics in compiler construction, including code optimization, partial evaluation, super compilers, compilation techniques for multiparadigm languages, concurrent compilers.

Combinatorial Search and Optimization with ConstraintsStudents will be exposed to various constraint-based combinatorial search and optimization techniques that arise in artificial intelligence, operations research, etc. Topics include, but are not limited to, local propagation, consistency algorithms, Boolean constraint solving, numerical constraint solving, linear programming, search, and their applications.
Information Retrieval and Search Engines
This course surveys the current research in information retrieval for the Internet and related topics. This course will focus on the theoretical development of information retrieval systems for multimedia contents as well as practical design and implementation issues associated with Internet search engines. Topics include probabilistic retrieval, relevance feedback, indexing of multimedia data, and applications in e-commerce.
Brain Theory and Sensorimotor Processing :
The main focus of the class will be to explore various aspects of biological neural network modelling for visuomotor coordination. Topics such as visual perception of form, texture, color, depth and motion, motor movement generation, sensorimotor interaction, neural mechanisms for learning and memory, and applications to intelligent robots will be examined. This will be an interdisciplinary course combining engineering, cognitive science, and neuroscience approaches. Students are expected to have knowledge in linear algebra, calculus, probability theory and programming.
Principles of Computational Learning :This course aims at providing theoretical guides and useful tools for students working on neural networks, pattern recognition, computer vision, artificial intelligence or other topics involving learning and mathematical modelling. The first part of this course is an extensive introduction to learning theories and techniques for neural networks or intelligent computational machinery in general. The second half will cover the major techniques such as supervised learning, unsupervised learning and self-organization, as well as reinforcement learning. It would be helpful to have some previous knowledge on probability theory, statistics, neural networks and pattern recognition.
Image Processing and Computer Vision :
Image Processing: enhancement technique, image compression, segmentation, morphology, color image processing and restoration. Computer Vision: representation, decision models, structural methods and image understanding.
Theory of Neural Computation: This course introduces state-of-the-art neural network research. It covers the learning algorithms of various neural network paradigms such as the backpropagation network, the Boltzmann machine, the Hopfield network, bidirectional associative memory, adaptive resonance theory, the Kohonen network, and learning vector quantizer. Techniques in the theoretical analysis of their characteristics, limitations, storage capacity, stability and convergence are included.