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Email: email@example.comRoom Number: Peter Landin, CS 335
Artificial Intelligence in Games (Postgraduate)
This module covers a range of Artificial Intelligence techniques employed in games, and teaches how games are and can be used for research in Artificial Intelligence. This module has a strong programming component. The module explores algorithms for creating agents that play classical board games (such as chess or checkers) and real-time games (Mario or PacMan), including single agents able to play multiple games. The module gives an overview of multiple techniques, such as Monte Carlo Tree Search, Evolutionary Computation, Deep and Machine Learning applied to games.
Data Mining (Postgraduate)
Data that has relevance for decision-making 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 electronic patient records. Data mining is a rapidly growing field that is concerned with developing techniques to assist decision-makers to make intelligent use of these repositories. The field of data mining has evolved from the disciplines of statistics and artificial intelligence. This module will combine practical exploration of data mining techniques with a exploration of algorithms, including their limitations. Students taking this module should have an elementary understanding of probability concepts and some experience of programming.
Machine Learning (Postgraduate)
The aim of the module is to give students an understanding of machine learning methods, including pattern recognition, clustering and neural networks, and to allow them to apply such methods in a range of areas.
Object-Oriented Programming (Undergraduate)
Major topics include the concepts of class, object, method, subclass, inheritance and their use in programming. The relevance of the object oriented style with respect to concrete software problems will be stressed both in lectures and labs. There will be two hours of lectures per week, and each student will have a weekly timetabled lab session. In addition, you will be expected to spend further time outside scheduled lab periods in the lab (or at home machines if they are available), and to read textbooks and review notes.