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Coursework
I had finished with my program of study.
In this semester, I am focused on finishing my dissertation
The following is a subset of my coursework (semester taken) at Iowa State University:
Machine Learning and Artificial Intelligence
COM S 572 Principles of Artificial Intelligence (Fall 2001)
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.
COM S 573 Machine Learning (Spring 2002)
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 S 672 Advanced Topics in Computational Models of Learning (Spring 2004)
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 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.
CPR E 594 Algorithms for the Internet (Spring 2003)
This course studies the new algorithms behind the current generation of internet applications. The algorithms include Algorithms for Web Search (Kleinberg's algorithm, Google's PageRank Algorithm), Algorithms for Massive Data Sets, Peer-to-peer systems (Napster, Gnutella, CAN, Chord, Tapestry), Web Page Caching (Bloom Filter, etc.), and other topics (Scalable High Speed IP Routing Lookups, The Impact of Internet Policy and Topology on Delayed Routing Convergence, Economics in Network Routing, Algorithms for Selfish Agents, Byzantine Agreement).
CPR E 587 Text Mining, Text Processing, and the Internet (Spring 2003)
Mining, retrieval, and other processing of text, including text and hypermedia on the world wide web. Human computer interaction in the context of text and hyper media. Topics of particular interest to enrolled students.
I E 583. Knowledge Discovery and Data Mining (Fall 2004)
Introduction to data warehouses and knowledge discovery. Techniques for data mining, including probabilistic and statistical methods, genetic algorithms and neural networks, visualization techniques, and mathematical programming. Relationship to enterprise computing. Advanced topics include web-mining and mining of multimedia data. Case studies from both manufacturing and service industries.
Bioinformatics and Computational Biology
COM S 549 Advanced Algorithms in Computational Biology (Spring 2002)
Design and analysis of algorithms for applications in computational biology, pairwise and multiple sequence alignments, approximation algorithms, string algorithms including in-depth coverage of suffix trees, semi-numerical string algorithms, algorithms for selected problems in fragment assembly, phylogenetic trees and protein folding. No background in biology is assumed. Also useful as an advanced algorithms course in string processing.
COM S 551 Computational Techniques for Genome Assembly and Analysis (Fall 2002)
Introduction to practical sequence assembly and comparison techniques. Topics include global alignment, local alignment, overlapping alignment, banded alignment, linear-space alignment, word hashing, DNA-protein alignment, DNA-cDNA alignment, comparison of two sets of sequences, construction of contigs, and generation of consensus sequences. Focus on development of sequence assembly and comparison programs.
BCB 495 Molecular Biology for Computational Scientists (Fall 2001)
Survey of molecular cell biology and molecular genetics for non-biologists, especially those interested in bioinformatics/computational biology. Basic cell structure and function; principles of molecular genetics; biosynthesis, structure, and function of DNA, RNA, and proteins; regulation of gene expression; selected topics.
General
COM S 511 Design and Analysis of Algorithms (Fall 2001)
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 531 Theory of Computation (Spring 2002)
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 554. Distributed and Network Operating Systems (Spring 2003)
Laboratory course dealing with practical issues of design and implementation of distributed and network operating systems and distributed computing environments (DCE). The client server paradigm, inter-process communications, layered communication protocols, synchronization and concurrency control, and distributed file systems. Graduate credit requires additional in-depth study of advanced operating systems. Written reports.
COM S 587. Principles of Distributed and Network Programming (Fall 2002)
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.
COM S 652. Topics in Distributed Operating Systems (Fall 2002)
Concepts and techniques for network and distributed operating systems: Communications protocols, processes and threads, name and object management, synchronization, consistency and replications for consistent distributed data, fault tolerance, protection and security, distributed file systems, design of reliable software, performance analysis.
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