Artificial Intelligence Research Laboratory: Courses in AI and related topics.

Graduate Courses in Artificial Intelligence

Principles of Artificial Intelligence. Com S 572. Dual-listed with 472; (3-1) Cr. 3. F. Prereq: 311, 331, Stat 330, Com S 342 or comparable programming experience. 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.
Additional information can be found on the course web page.
Machine Learning. Com S 573. (3-1) Cr. 3. S. Prereq: 311, 362, Stat 330. 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.
Additional information can be found on the course web page.
Intelligent Multiagent Systems. Com S 574. (3-0) Cr. 3. S. Prereq: Stat 330, Com S 331, Com S 572 or Com S 573 or Com S 472 or Com S 474. Specification, design, implementation, and applications of multi-agent systems. Intelligent agent architectures; infrastructures, languages and tools for design and implementation of distributed multi-agent systems; Multi-agent organizations, communication, interaction, cooperation, team formation, negotiation, competition, and learning. Selected topics in decision theory, game theory, contract theory, bargaining theory, auction theory, and organizational theory. Selected topics in knowledge representation and ontologies. Agent-based systems and the Semantic Web. Applications in distributed intelligent information networks for information retrieval, information integration, inference, and discovery from heterogeneous, autonomous, distributed, dynamic information sources.
Advanced Topics in Computational Models of Learning. Com S 672. (3-0) Cr. 3. Repeatable. Alt. S., offered 2008. Prereq: Com S 572 or 573 or 472 or 474. 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.
Advanced Topics in Computational Intelligence. (3-0) Cr. 3. Repeatable. Alt. S., offered 2008. Prereq: Com S 572 or 573 or 472 or 474. 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.
Artificial Intelligence Research Seminar Com S 610. Variable Credit. Topics vary. Student-led discussion of research articles of current interest. Additional details can be found on the AI Research Seminar web page

Undergraduate Courses in Artificial Intelligence

Principles of Artificial Intelligence. Com S 472. Dual-listed with 572; (3-1) Cr. 3. F. Prereq: 311, 331, Stat 330, Com S 342 or comparable programming experience. 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.
Additional information can be found on the course web page.
Elements of Neural Computation. Com S 474. (3-1) Cr. 3. S. Prereq: 311, 330 or Cpr E 310, Stat 330, Math 165, Engl 250, Sp Cm 212, Com S 342 or comparable programming experience. Introduction to theory and applications of neural computation and computational neuroscience. Computational models of neurons and networks of neurons. Neural architectures for associative memory, knowledge representation, inference, pattern classification, function approximation, stochastic search, decision making, and behavior. Neural architectures and algorithms for learning including perceptions, support vector machines, kernel methods, bayesian learning, instance based learning, reinforcement learning, unsupervised learning, and related techniques. Applications in Artificial Intelligence and cognitive and neural modeling. Hands-on experience is emphasized through the use of simulation tools and laboratory projects. Oral and written reports. Nonmajor Graduate Credit
Additional information can be found on the course web page

Recommended Courses in Bioinformatics

Bioinformatics I (Fundamentals of Genome Informatics). Com S 567. (Cross-listed with BCB, CPR E.) (3-0) Cr. 3. F. Prereq: Com S 208; Com S 330; Stat 341; credit or enrollment in Biol 315, Stat 401, and Stat 432. Biology as an information science. Review of algorithms and information processing. Generative models for sequences. String algorithms. Pairwise sequence alignment. Multiple sequence alignment. Searching sequence databases. Genome sequence assembly.
Bioinformatics II (Advanced Genome Informatics). Com S 568. (Cross-listed with BCB, GDCB, STAT.) (3-0) Cr. 3. S. Prereq: BCB 567, BBMB 301, Biol 315, Stat 401, Stat 432, credit or enrollment in Gen 411. Advanced sequence models. Basic methods in molecular phylogeny. Hidden Markov models. Genome annotation. DNA and protein motifs. Introduction to gene expression analysis.
Bioinformatics III (Structural Genome Informatics). Com S 569. (Cross-listed with BBMB, BCB, MATH.) (3-0) Cr. 3. F. Prereq: BCB 567, Gen 411, Stat 401, Stat 432. Algorithmic and statistical approaches in structural genomics including protein, DNA and RNA structure. Structure determination, refinement, representation, comparison, visualization, and modeling. Analysis and prediction of protein secondary and tertiary structure, disorder, protein cores and surfaces, protein-protein and protein-nucleic acid interactions, protein localization and function.
Bioinformatics IV (Computational Functional Genomics and Systems Biology. Com S 570. (Cross-listed with BCB, GDCB, STAT.) (3-0) Cr. 3. S. Prereq: BCB 567, Biol 315, Com S 363, Gen 411, Stat 401, Stat 432. Algorithmic and statistical approaches in computational functional genomics and systems biology. Biological Information Integration - knowledge (ontology) driven and statistical approaches. Qualitative, probabilistic, and dynamic network models. Modeling, analysis, simulation and inference of transcriptional regulatory modules and networks, protein-protein interaction networks. Metabolic networks; cells and systems.

Recommended Courses in Mathematics

Abstract Algebra I. Math 301. (3-0) Cr. 3. F.S. Prereq: 166 or 166H, 307 or 317, and 201. Theory of groups. Homomorphisms. Quotient groups. Introduction to rings. Emphasis on writing proofs.
Matrices and Linear Algebra. Math 307. (3-0) Cr. 3. F.S.SS. Prereq: 2 semesters of calculus. Systems of linear equations, determinants, vector spaces, linear transformations, orthogonality, least-squares methods, eigenvalues and eigenvectors. Emphasis on methods and techniques. Nonmajor Graduate Credit
Graphs and Networks. Math 331. (3-0) Cr. 3. S. Prereq: 166 or 166H; 201 or experience with proofs. Structure and extremal properties of graphs. Topics are selected from: trees, networks, colorings, paths and cycles, connectivity, planarity, Ramsey theory, forbidden structures, enumeration, applications.
Logic for Mathematics and Computer Science. Math 421. (Cross-listed with COM S.) (3-0) Cr. 3. S. Prereq: 301 or 307 or 317 or Com S 330. Propositional and predicate logic. Topics selected from Horn logic, equational logic, resolution and unification, foundations of logic programming, reasoning about programs, program specification and verification, model checking and binary decision diagrams.
Optimization. I E 312. (3-0) Cr. 3. F. Prereq: Math 267. Concepts, optimization and analysis techniques, and applications of operations research. Formulation of mathematical models for systems, concepts, and methods of improving search, linear programming and sensitivity analysis, network models, and integer programming. Nonmajor Graduate Credit

Recommended Courses in Statistics

Empirical Methods for Computer Science. Stat 430. (3-0) Cr. 3. S. Prereq: Stat 330 or an equivalent course. Programs and systems as objects of empirical studies; exploratory data analysis; analysis of designed experiments . analysis of variance, hypothesis testing, interaction among variables; linear regression, logistic regression, Poisson regression; parameter estimation, prediction, confidence regions, dimension reduction techniques, model diagnostics and sensitivity analysis; simulation techniques and bootstrap methods; applications to performance assessment . comparison of multiple systems; communicating results of empirical studies. Nonmajor graduate credit. Nonmajor Graduate Credit
Introduction to the Theory of Probability and Statistics I. Stat 341. (Cross-listed with MATH.) (3-0) Cr. 3. F.S. Prereq: Math 265 (or 265H). Probability; distribution functions and their properties; classical discrete and continuous distribution functions; moment generating functions, multivariate probability distributions and their properties; transformations of random variables; simulation of random variables and use of the R statistical package.
Introduction to the Theory of Probability and Statistics II. Stat 342. (Cross-listed with MATH.) (3-0) Cr. 3. S. Prereq: 341, Math 307 or 317. Sampling distributions; confidence intervals and hypothesis testing; theory of estimation and hypothesis tests; linear model theory, enumerative data.
Exploratory Methods and Data Mining. Stat 503. (2-2) Cr. 3. Alt. S., offered 2009. Prereq: 401, 341 or 447. Approaches to finding the unexpected in data; pattern recognition, classification, association rules, graphical methods, classical and computer-intensive statistical techniques, and problem solving. Emphasis is on data-centered, non-inferential statistics for large or high-dimensional data, topical problems, and building report writing skills.
Time Series Analysis. Stat 551. (3-0) Cr. 3. F. Prereq: 447 or 542. Concepts of trend and dependence in time series data; stationarity and basic model structures for dealing with temporal dependence; moving average and autoregressive error structures; analysis in the time domain and the frequency domain; parameter estimation, prediction and forecasting; identification of appropriate model structure for actual data and model assessment techniques. Possible extended topics include dynamic models and linear filters.

Recommended Courses in Philosophy

Philosophy of Science. (3-0) Phil 380. Cr. 3. F. Prereq: 201 or 6 credits in a science. Introduction to the philosophy of science. A variety of basic problems common to the natural and social sciences: the nature of explanation, the structure of theories, the unity of science, and the distinction between science and nonscience.
Brains, Minds, and Computers. Phil 465. (3-0) Cr. 3. F. Prereq: 201. Examination of concepts such as computability, intelligence, programming, and free will; and of arguments about whether any human capacity is forever beyond realization in a machine.

Recommended Courses in Psychology

Brain and Behavior. (3-0) Cr. 3. F.S. Prereq: 101, 155, or 211. Survey of basic concepts in the neurosciences with emphasis on brain mechanisms mediating sensory processes, arousal, motivation, learning, and abnormal behavior.
Sensation and Perception. Psych 312. (3-0) Cr. 3. F.S. Prereq: 101. Survey of the physiology and psychology of human sensory systems including vision, audition, smell, taste, the skin senses, and the vestibular senses.
Learning and Memory. Psych 313. (3-0) Cr. 3. F.S. Prereq: 101. Fundamental concepts and theories of learning and memory derived from human and animal research.
Cognitive Processes. Psych 316. (3-0) Cr. 3. F.S. Prereq: 101. The study of the human mind, addressing the processes by which people perceive the world, remember information, access and use knowledge, understand language, make decisions, reason, learn and solve problems.

Recommended Course in Neuroscience

Cellular, Molecular and Developmental Neuroscience. Neuro 556. (Cross-listed with GDCB.) Cr. R. F. Prereq: Biol 335 or Biol 436; physics recommended. Fundamental principles of neuroscience including cellular and molecular neuroscience, nervous system development, sensory, motor and regulatory systems.

Other Recommended Courses in Computer Science

Students specializing in AI might find it useful to expand their background in computer science by drawing on the broad range of courses offered in Computer Science:

Software Requirements Engineering. Com S 509; (Cross-listed with CPR E.) (3-0) Cr. 3. F. Prereq: 309. The requirements engineering process including identification of stakeholders requirements elicitation techniques such as interviews and prototyping, analysis fundamentals, requirements specification, and validation. Use of Models: State-oriented, Function-oriented, and Object-oriented. Documentation for Software Requirements. Informal, semi-formal, and formal representations. Structural, informational, and behavioral requirements. Non-functional requirements. Use of requirements repositories to manage and track requirements through the life cycle. Case studies, software projects, written reports, and oral presentations will be required.
Design and Analysis of Algorithms. Com S 511. (Cross-listed with CPR E.) (3-0) Cr. 3. F. Prereq: 311. 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.
Formal Methods in Software Engineering. Com S 512. (3-0) Cr. 3. S. Prereq: 311, 330. A study of formal techniques for specification and verification of software systems. Topics include temporal logic, propositional and predicate logic, model checking, process algebra, theorem proving. Tools providing automated support for these techniques will also be discussed.
Theory of Computation. Com S 531. (3-0) Cr. 3. S. Prereq: 331. 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.
Principles and Practice of Compiling. Dual-listed with 440; (3-1) Cr. 3. S. Prereq: 331, 342, Engl 250, Sp Cm 212. Theory of compiling and implementation issues of programming languages. Programming projects leading to the construction of a compiler. Projects with different difficulty levels will be given for 440 and 540. Topics: lexical, syntax and semantic analyses, syntax-directed translation, runtime environment and library support. Written reports.
Programming Languages. Com S 541. (3-1) Cr. 3. F. Prereq: 342 or 440. Survey of the goals and problems of language design. Formal and informal studies of a wide variety of programming language features including type systems. Creative use of functional and declarative programming paradigms.
Wireless Sensor Networks. Cpr E 546. (3-0) Cr. 3. Prereq: Cpr E 489 or 530. Selected topics from recent advances in wireless sensor networks, including data-centric routing, query, and storage; data fusion and aggregation; coverage, connectivity, and lifetime of wireless sensor networks; wireless sensor networks deployment and management; security issues; energy-efficiency issues; radio and link characterisitics in wireless sensor networks; medium access control protocols and link layer techniques; tracking and localization; geographical routing; robust routing; time synchronization; wireless sensor networks applications. Introduction to TinyOS and the nesC language. Hands-on experiments with Crossbow Motes.
Distributed Systems and Middleware. Cpr E 550; (3-0) Cr. 3. Prereq: 308 or Com S 352. Fundamentals of distributed computing, software agents, naming services, distributed transactions, security management, distributed object-based systems, middleware-based application design and development, case studies of middleware and internet applications.
Principles of Operating Systems. Com S 552. (3-0) Cr. 3. S. Prereq: 352. A comparative study of high-level language facilities for process synchronization and communication. Formal analysis of deadlock, concurrency control and recovery. Protection issues including capability-based systems, access and flow control, encryption, and authentication. Additional topics chosen from distributed operating systems, soft real-time operating systems, and advanced security issues.
Simulation: Algorithms and Implementation. Com S 555; (3-0) Cr. 3. F. Prereq: 311 and 330, Stat 330, Engl 150, Sp Cm 212. Introduction to discrete-event simulation with a focus on computer science applications, including performance evaluation of networks and distributed systems. Overview of algorithms and data structures necessary to implement simulation software. Discrete and continuous stochastic models, random number generation, elementary statistics, simulation of queuing and inventory systems, Monte Carlo simulation, point and interval parameter estimation. Graduate credit requires additional in-depth study of concepts. Oral and written reports.
Principles of Distributed and Network Programming. Com S 587. (3-0) Cr. 3. F. Prereq: 352 or Cpr E 489 or equivalent. Programming paradigms for building modern distributed applications, including multithreaded client-server programming, distributed object frameworks and programming languages. Directory services. Web-based computing. Mobile computing. Peer-to-Peer computing. Network multimedia applications. Reliability and manageability of networked systems, including aspects of distributed system security, verification of concurrent systems, and network management.
Principles of Database Systems. Com S 561. (3-0) Cr. 3. S. Prereq: 311, 363. Database models. Algebraic, first order, and user-oriented query languages. Database schema design. Physical storage, access methods, and query processing. Transaction management, concurrency control, and crash recovery. Database security. Parallel and distributed databases, and special purpose databases. Data warehousing and data mining.
Implementation of Database Systems. Com S 562. (3-0) Cr. 3. F. Prereq: 461 or 561. Implementation topics and projects are chosen from the following: Storage architecture, buffer management and caching, access methods, design, parsing and compilation of query languages and update operations, application programming interfaces (APIs), user interfaces, query optimization and processing, and transaction management for relational, object-oriented, semistructured (XML), and special purpose database models; client-server architectures, metadata and middleware for database integration, web databases.
Theory of Games, Knowledge and Uncertainty. (3-0) Com S 634. Cr. 3. Alt. S., offered 2008. Prereq: 330. Fundamentals of Game Theory: individual decision making, strategic and extensive games, mixed strategies, backward induction, Nash and other equilibrium concepts. Discussion of Auctions and Bargaining. Repeated, Bayesian and evolutionary games. Interactive Epistemology: reasoning about knowledge in multiagent environment, properties of knowledge, agreements, and common knowledge. Reasoning about and representing uncertainty, probabilities, and beliefs. Uncertainty in multiagent environments. Aspects and applications of game theory, knowledge, and uncertainty in other areas, especially Artificial Intelligence and Economics, will be discussed.
Advanced Topics in Computational Randomness. (3-0) Cr. 3. Repeatable. F. Prereq: 531. 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.