![]() Introduction to computer image synthesis, modeling, and animation emphasizing the state-of-the-art algorithm applications. Unsupervised learning and clustering.ĬSE P 557 Current Trends in Computer Graphics Supervised learning and predictive modeling decision trees, rule induction, nearest neighbors, Bayesian methods, neural networks, support vector machines, and model ensembles. ![]() Methods for designing systems that learn from data and improve with experience. Prerequisites: none.Ĭourse video description from 2021 (same instructors) Programming language semantics and type theory. Our lectures are divided into 4 parts: genomics for drug discovery, sequence for drug discovery, graph for drug discovery and structure for drug discovery.Ī study of non-imperative programming paradigms such as functional, object-oriented, logic, and constraint programming. In this course, we will cover different parts of drug discovery that have benefited greatly from machine learning methods. Prerequisites: none.ĬSE P 590 section A Special Topics: AI for Medicineĭeveloping artificial intelligence methods for medicine and drug discovery is an emerging topic. Prerequisites: none.Įxamines the fundamental of computer security including: human factors attack detection, measurements, and models cryptography and communications security system design and implementation and side channels. Examples drawn from computational geometry, biology, scientific computation, image processing, combinatorial optimization, cryptography, and operations research. Principles of design of efficient algorithms with emphasis on algorithms with real world applications. ![]() Topics include mutation-based testing, constraint-based testing, automated test generation, and probabilistic debugging. Principles of effective software testing and debugging, emphasizing state-of-the-art approaches. CSE P 504 Advanced Topics in Software Systems: State of the Art Testing and Debugging ![]()
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