Relevant Courses

Com Sci 672. Advanced Topics in Computational Models of Learning.
Instructors: Professor Jin Tian
Description
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.

Project

Com Sci 610. Machine Learning Seminar.
Instructors: Professor Vasant Honavar
Description

Com Sci 575. Computational perception.
Instructors: Professor Alexander Stoytchev
Description
This class covers statistical and algorithmic methods for sensing, recognizing, and interpreting the activities of people by a computer.

BCB 569. Bioinformatics III (Structural Genome Informatics).
Instructors: Professor Robert L. Jernigan, Professor Guang Song and Professor Zhijun Wu
Description
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.

Com Sci 511 - Design and Analysis of Algorithms.
Instructors: Professor David Fernández-Baca
Description
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 Sci 570 - Bioinformatics IV: Computational Functional Genomics and Systems Biology
Instructors: Professor Vasant Honavar, Professor Julie Dickerson
Description
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.

Com Sci 531 - Theory of computation
Instructor: Professor Jack Lutz
Description
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.

Stat 430 - Empirical Methods for Computer Science Research
Instructor: Professor Karin Dorman
Description
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.

Coms Sci 573 - Machine Learning
Instructor: Professor Vasant Honovar
Research Project
Description
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.

Com Sci 572 - Principles of Artificial Intelligence
Instructor: Professor Vasant Honovar
Research Project
Description
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, deliberative, rational, adaptive, learning and communicative agents and multiagent systems. Artificial intelligence programming.

BCB 544 - Introduction to Bioinformatics
Instructor: Professor Drena Dobbs
Research Project
Description
Broad overview of bioinformatics with a significant problem-solving component, including hands-on practice using computational tools to solve a variety of biological problems. Topics include: database searching, sequence alignment, gene prediction, RNA and protein structure prediction, construction of phylogenetic trees, comparative and functional genomics.