Coursework 2003-2006

Fall '03

• Com S 572. Principles of Artificial Intelligence. Cr. 3. Specification, design, implementation, and selected applications of intelligent software agents and multi-agent systems. Computational models of intelligent behavior, including problem solving, knowledge representation, reasoning, planning, decision making, learning, perception, action, communication and interaction. Reactive, deliverative, rational, adaptive, learning and communicative agents. Artificial intelligence programming. Graduate credit requires a research project and a written report. Oral and written reports.
• Psych 516. Advanced Cognition. Cr. 3. Theoretical models and empirical research in human cognition including pattern recognition, attention, text processing, memory, problem solving, decision making, and language.

• Com S 573. Machine Learning. Cr. 3.  Algorithmic models of learning. Design, analysis, implementation and applications of learning algorithms. Learning of concepts, classification rules, functions, relations, grammars, probability distributions, value functions, models, skills, behaviors and programs. Agents that learn from observation, examples, instruction, induction, deduction, reinforcement and interaction. Computational learning theory. Data mining and knowledge discovery using artificial neural networks, support vector machines, decision trees, Bayesian networks, association rules, dimensionality reduction, feature selection and visualization. Learning from heterogeneous, distributed, dynamic data and knowledge sources. Learning in multi- agent systems. Selected applications in automated knowledge acquisition, pattern recognition, program synthesis, bioinformatics and Internet-based information systems. Oral and written reports.
• Com S 672. Advanced Topics in Computational Models of Learning.  Cr. 3. Selected topics in Computational Learning Theory (PAC learning, Sample complexity, VC Dimension, Occam Learning, Boosting, active learning, Kolomogorov Complexity, Learning under helpful distributions, Mistake Bound Analysis). Selected topics in Bayesian and Information Theoretic Models (ML, MAP, MDL, MML). Advanced statistical methods for machine learning. Selected topics in reinforcement learning.

• Com S 511. Design and Analysis of Algorithms.  Cr. 3. A study of basic algorithm design and analysis techniques. Advanced data structures, amortized analysis and randomized algorithms. Applications to sorting, graphs, and geometry. NP- completeness and approximation algorithms.
• Com S 548. Fundamental Algorithms in Computational Biology. Cr. 3. Introduction, design and analysis of fundamental algorithms and methods for molecular biology. Topics include pairwise sequence alignment, alignment heuristics, biological database and retrieval systems, multiple sequence alignment, phylogenetic trees, physical mapping, genome rearrangements, DNA-chips, fragment assembly, protein folding, and genetic networks.

• Com S 531. Theory of Computation. Cr. 3.  A systematic study of the fundamental models and analytical methods of theoretical computer science. Computability, the Church-Turing thesis, decidable and undecidable problems, and the elements of recursive function theory. Time complexity, logic, Boolean circuits, and NP-completeness. Role of randomness in computation.
• Com S 503. Complex Adaptive Systems Concepts and Techniques. Cr. 3. Prereq: Admission to CAS minor. Understanding of Computer Modeling of Complex Systems, Complex adaptive systems approach to the study of evolutionary computation, neural computation, cellular computation, computational models of immune systems, complexity theory, computational economics, and other fields of application.
• Com S 673. Advanced Topics in Computational Intelligence. Cr. 3.  Advanced applications of artificial intelligence in bioinformatics, distributed intelligent information networks and the Semantic Web. Selected topics in distributed learning, incremental learning, multi-task learning, multi-strategy learning; Graphical models, multi- relational learning, and causal inference; statistical natural language processing; modeling the internet and the web; automated scientific discovery; neural and cognitive modeling.

• Com S 610. Developmental Robotics Seminar. 
• Com S 633. Randomness in Computation. Cr. 3.  Advanced study of the role of randomness in computation. Randomized algorithms, derandomization, and probabilistic complexity classes. Kolmogorov complexity, algorithmic information theory, and algorithmic randomness. Applications chosen from cryptography, interactive proof systems, computational learning, lower bound arguments, mathematical logic, and the organization of complex systems.
• Stat 542. Theory of Probability and Statistics I. Cr. 4.  Sample spaces, probability, conditional probability; Random variables, univariate distributions, expectation, moment generating functions; Common theoretical distributions; Joint distributions, conditional distributions and independence, covariance; Probability laws and transformations; Introduction to the Multivariate Normal distribution; Sampling distributions, order statistics; Convergence concepts, the central limit theorem and delta method; Basics of stochastic simulation.

• Com S 512. Formal Methods in Software Engineering. Cr. 3.  A survey of formal methods relevant to the software life-cycle process including requirements, specifications, design, implementation, testing, and maintenance. Implications of formal results for software prototyping and automated testing.
• Com S 502. Complex Adaptive Systems Seminar.  Cr. 1.  Understanding core techniques in artificial life are based on basic readings in complex adaptive systems. Understand techniques of complex system analysis methods including: Evolutionary computation, Neural nets, Agent based simulations (Agent based Computational Economics). Large-scale simulations are to be emphasized, e.g. power grids, whole ecosystems.