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Course Work in CS and Math

Curriculum Vita

  

		Course Work in computer science and math
		My background is Bioengineering, and I put my past researches in research page. One of my main purposes to enroll the computer science graduate school at the University of Illinois at Chicago (UIC) is to build a basis of mathematical and computational science, so I only put course works in computer science and mathematic here.
		Applied Graph Theory at UIC Instructor: Prof. Tadao Murata Textbook: GRAPH THEORY with Applications to Engineering and Computer Science, Narsingh Deo, Prentice-Hall Original Handouts Topics: Introduction: historical notes, basic terminology, graph synthesis examples Paths and circuits: Hamiltonian paths, Euler graphs, operations on graphs Trees: spanning trees, their properties, theorems and proofs; distance, eccentricity, radius, diameter; f-circuits, elementary tree transformation, finding all spanning trees Cutsets: f-cutsets, measures of connectivity, separable graphs, Menger's theorem, maximum-flow/minimum-cut theorem, network flows, terminal capacity matrix, synthesis of communication nets Planar and dual graphs: Euler's formula, geometric and combinatorial duals, theorems of Kuratowski, Whitney and MacLane Vector space of a graph: circuit and cutset subspaces, orthogonality, other subspaces Matrix representation of graphs: relationships and properties of incidence-, cutset-, circuit- and path-matrices Synthesis of (Boolean) switching networks, odd-ring sum condition, Tutte realizability Coloring, dominating sets, minimum covers, partitioning, maximum matching Directed graphs, strongly connected graphs, out-trees, in-trees, matrices for directed graphs, Binet-Cauchy theorem, Kirchohoff matrix, properties of acyclic graphs NP-complete graph and network problems	Fall 2005	A
		Data Mining and Text Mining at UIC Instructor: Prof. Bing Liu Textbook: Original Handouts Topics: Data pre-processing Paths and circuits: Hamiltonian paths, Euler graphs, operations on graphs Association rule mining Supervised learning (Classification) Unsupervised learning (Clustering) Post-processing: Are all the data mining results interesting? Text mining Partially supervised learning Introduction to Web mining:	Fall 2005	A
		Matrices and Their Applications at UIC Instructor: Prof. Shmuel Friedland Textbook: Matrix Analysis, R.A. Horn and C.R. J, Cambridge University Press Original Handouts Topics: Jordan Canonical form and spectral theory for matrices over complex numbers with applications to control theory. Spectral decomposition of normal operators in finite dimensional inner product spaces, i.e. diagonalization of normal matrices by a unitary matrix. Mini-max characterizations of eigenvalues of hermitian matrices Singular Value Decomposition and Generalized Inverses, with applications image processing and microarrays in DNA Inverse eigenvalue problems with applications in engineering and DNA. Tensor (Kronecker) products of vector spaces and matrices with applications to SVD decomposition of triple tensors Perron-Frobenius theorem for nonnegative matrices with applications to probability Random matrices	Fall 2005	A
		I attended the Machine Learning Summer School, Chicago 2005.
		Machine Learning Summer School at the Toyota Technological Institute at Chicago, Chicago University Topics: Bayesian Learning Information Geometry Boosting Kernel Methods Decision Trees for Classification and Regression Learning and Game Theory Empirical Comparisons of Learning Methods Learning on Structured Data Energy-based Models and Learning for Invariant Image Recognition Machine Learning Reductions Estimates for Learning Algorithms Online Learning for Kernels: from Binary Classification to Sequence Prediction Evidence Integration in Bioinformatics Parsing Generalization Bounds Semi-supervised Learning and Manifold Methods	May 16-27 2005	40 hours of tutorial lectures
		Mathematical Theory of Artificial Intelligence at UIC Instructor: Prof. Gyorgy Turan Textbook: Machine Learning, Tom M. Michell, WCB/McGraw Hill An Introduction to Computational Learning Theory, Michael J. Kearns, Umesh V. Vazirani, The MIT Press Original Handouts Topics: Concept Learning Decision Tree Learning Artificial Neural Network Probably Approximately Correct (PAC) Learning Occam's Razor Learning Decision List Vapnik-Chervonekis (VC) Dimension Support Vector Machine (SVM) Learning Finite Automata Relational Reinforcement Learning Weighted Majority Algorithm On-Line Learing Boosting	Spring 2005	A
		Probability on Graphs at UIC Instructor: Prof. Shmuel Friedland Textbook: Finite Markov Chains and Algorithmic Applications, Olle Haggstrom, London Mathematical Society Original Handouts Topics: Graphs: undirected, directed, weighted, decompositions of graphs into connected components (reduced graph), isomorphisms, invariants. Spectral theory of matrices with real and complex entries Markov Chains: irreducible homogeneous Markov chains, the steady state distribution, the rate of convergence to the steady state, reducible homogeneous Markov chains, nonhomogeneous Markov chains. Random Walks on Graphs: the space of walks on a given graph, invariant probability measures, first passage time, the Kolmogorov-Sinai entropy of an invariant measure, Parry measures having the maximal entropy, the topological entropy as the exponential growth rate of the number of walks as the functions of length. MCMC-Markov Chain Monte Carlo: Metropolis algorithm and Metropolis schemes. Gibbs Samplers.	Spring 2005	Grade: A
		Introduction to Networking at UIC Instructor: Prof. Patrick Troy Textbook: A Top-Down Approach Featuring The Internet Computer Networking 3rd, James F. Kurose, Keith W. Ross, Pearson Addison Wesley Topics: Application Layer Protocols: HTTP, FTP, SMTP, POP, MIME, DNS... Transport Layer Protocols: TCP, UDP, Flow and Congestion Control, Go-Back-N, Selective Repeat... Network Layer: Forwarding and Routing, Virtual-Circuit Network, Datagram Network, Switching, IPv4 and 6, NAT, Subnet, Internet Control Message Protocol, Autonomous System Routing... Link Layer and Local Area Network: Channel Partitioning, Random Access, Taking-Turns Protocols, LAN, MAC address, Address Resolution Protocol, CSMA/CD Ethernet Multiple Access Protocol, ARP, Hub, Switch, Point-to-Point Protocol...	Spring 2005	Grade: A
		Introduction to Software Engineering at UIC Instructor: Prof. Ugo Buy Textbook: Object-Oriented and Classical Software Engineering 6th, Stephen R. Schach, McGill Hill Original Handouts Topics: Software Life-Cycle Model Software Process Capability Maturity Models Unified Modeling Language (UML) Requirements: Use Case, Scenario, Glossary, Rapid Prototype... Classical Analysis: Operation Oriented Design, Data Flow Analysis, Transaction Analysis, Petri Net.... Object-Oriented Analysis: Functional Modeling, Entity-Class Modeling, Dynamic Modeling, Class diagram, Collaboration Diagram, Sequence Diagram Design: Design Patterns, Object-Oriented Design, Design Workflow, Metrics for Design ... Implementation: Integration, Coding Standard, Testing, CASE tools... Postdelivery Maintenance Cohesion and Coupling Abstract Data Type	Spring 2005	Grade: A
		Data Structure and Discrete Mathematics at UIC Instructor: Prof. Florin Balasa Textbook: Data Structures and Algorithms in JAVA, Michael T. Goodsirh, Roberto Tamassia, Wiley Discrete Mathematics with Applications 3rd, Susanna S. Epp, Thomson Brooks/Cole Topics: Stack, Queues, and Recursion Vector, Lists, and Sequences Trees Priority Queues Maps and Dictionaries Search Trees: Binary Search Tree, AVL Tree, and Splay Trees Sorting Graphs	Fall 2004	Grade: A
		Languages and Automata at UIC Instructor: Prof. Ajay Kshemkalyani Textbook: Introduction to the Theory of Computation, Michael Sipser, PWS Publishing Company Topics: Regular Languages Finite Automata Nondeterminism Regular Expression Context-Free Grammar Pushdown Automata Turing Machines Decidability Reducibility Time Complexity NP-completeness	Fall 2004	Grade: A
		Computer Algorithm I at UIC Instructor: Prof. Richard G. Larson Textbook: Introduction to Algorithm 2nd, Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein, McGraw Hill Topics: Recurrences Probabilistic Analysis and Randomized Algorithm Heap, Quick, Counting, and other sorting Algorithms Dynamic Programming Greedy Algorithm Breath First Search Depth First Search Topological Sort Minimum Spanning Trees Shortest Path: The Bellman-Ford Algorithm, Dijkstra Algorithm, shortest path in directed acyclic graphs NP-Completeness NP-completeness and reducibility	Fall 2004	Grade: A
		Following are the classes I attended when I was in Berkeley. I tried to follow every assignment.
		Statistical Learning Theory at UC Berkeley Instructor: Michael Jordan Textbook: M. I. Jordan, An Introduction to Probabilistic Graphical Models, in preparation Topics: This course covers a thorough grounding in probabilistic and computational methods for the statistical modeling of complex, multivariate data. The emphasis is on the unifying framework provided by graphical models, a formalism that merges aspects of graph theory and probability theory.	Fall 2002	N/A
		Convex optimization and applications at UC Berkeley Instructor: Laurent El Ghaoui Textbook: Convex Optimization, Vandengerphe Boyd, in preparation Original Handouts Topics: This course is on mathematical programming, with emphasis on convex optimization and problems with uncertain data. The course covers some convex optimization theory and algorithms, and describes various applications, with some emphasis on problems with incomplete/unknown data. A large number of examples arising in a variety of fields will be given, covering analysis, design and control of complex systems, and in various identification, data fitting and estimation problems.	Spring 2003	N/A

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	Makio Tamura