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Fall 2006: Currently I am finished with my program
of study. I am focused on taking research credits and finishing
my dissertation
The following is a subset of my coursework (semester taken) at Iowa State University:
General:
COM S 511 Data Structure and Analysis of Algorithms (Fall 2000)
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 611 Advanced Topics of Analysis of Algorithms (Spring 2001)
Advanced algorithm analysis and design techniques. Topics include graph algorithms, algebraic algorithms, number-theoretic algorithms, randomized and parallel algorithms. Intractable problems and NP-completeness. Advanced data structures.
COM S 633 Randomness in Computation (Fall 2000)
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.
COM S 531 Theory of Computation (Spring 2001)
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.
Machine Learning and Artificial Intelligence:
COM S 572 Principles of Artificial Intelligence (Fall 2000)
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.
COM S 573 Machine Learning (Spring 2001)
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 673 Advanced Topics of Artificial Intelligence (Spring 2003)
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.
Bioinformatics and Computational Biology:
GEN 594 Computational Molecular Biology (Spring 2001)
State-of-the-art introduction to bioinformatics with emphasis on concepts and principles, combined with hands-on (keyboard) applications. Topics typically include: molecular databases, score-based sequence analysis, amino acid substitution scoring matrices, query search problems, dynamic programming and other methods for pairwise sequence alignment, motif identification, multiple sequence alignment, construction of phylogenetic trees from sequence data, gene structure prediction, protein structure prediction.
COM S 597 Computational Structure of Biology (Spring 2002)
State-of-the-art introduction to the modeling and prediction of biological structures. Topics include dna/rna structures, protein structures, and protein/protein interactions.
COM S 518 Computational Geometry (Spring 2003)
Introduction to data structures, algorithms, and analysis techniques for computational problems that involve geometry. Line segment intersection, polygon triangulation and visibility problems, range queries, point location, arrangements and duality, Voronoi diagrams and Delaunay triangulation, convex hulls. Other selected topics. Programming assignments. A scholarly report must be submitted for graduate credit.
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