Gang Fang (3481652) PhD Student (advisor: Dr. Vipin Kumar).doc

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Gang Fang (3481652) PhD Student (advisor: Dr. Vipin Kumar).doc

  1. 1. Gang Fang (3481652) PhD Student (advisor: Dr. Vipin Kumar) (I) Petition of Courses Transfer from M.S. at CSE@SUNY-Buffalo (II) Petition of substituting advanced courses in place of Breadth courses (To take, CSci8701: Overview of Database Research (Systems)) (To take, CSCI 8363 Numerical Linear Algebra in Data Exploration (Theory)) Courses Taken at CSE@SUNY-Buffalo [1] CSE 555 Pattern Recognition (Grade: A) (Petition for General Requirement) [2] CSE 531 Algorithms Analysis and Design (Grade: A) (Petition for Breadth (Theory) Requirement) [3] CSE 521 Introduction to Operating System (Grade: A) (Petition for Breadth (System) Requirement) [4] CSE 560 Data Models and Query Languages (Grade: A) (Petition for General Requirement) [5] CSE 574 Machine Learning (Grade: B+) (Petition for Breadth (Theory) Requirement) [6] CSE 589 Modern Networking Concepts (Grade: B+) (Petition for Breadth (System) Requirement) p.s. Courses Taken at CS@UMN CSci5523: Data Mining (Fall 2007) (Grade: A) (for Breath (Application) Req.) CSci5481: Computational Techniques for Genomics (Fall 2007) (Grade: A) (for Breath (Application) Req.) [1] Pattern Recognition (Grade: A) http://www.cedar.buffalo.edu/~srihari/CSE555/index.html Reference Textbooks: (i) Pattern Classification (2nd. Edition) by R. O. Duda, P. E. Hart and D. Stork, Wiley 2002, (ii) Pattern Recognition and Machine Learning by C. Bishop, Springer 2006, and (iii) Statistics and the Evaluation of Evidence for Forensic Scientists by C. Aitken and F. Taroni, Wiley, 2004. 1. Introduction 2. Bayes Decision Theory 1. Bayes Decision Rule 2. Minimum Error Rate Classification 3. Normal Density and Discriminant Functions 4. Error Integrals and Bounds 5. Bayesian Networks, Compound Decision Theory 3. Generative Methods 1. Maximum-Likelihood and Bayesian Parameter Estimation
  2. 2. 1. Maximum-Likelihood Estimation 2. Bayesian Parameter Estimation 3. Sufficient Statistics 4. Some Common Statistical Distributions 5. Dimensionality and Computational Complexity 6. Principal Components Analysis 7. Fisher Linear Discriminant 8. Expectation Maximization 9. Sequential Data and Hidden Markov Models 2. Nonparametric Techniques 1. Density Estimation 4. Discriminative Methods 1. Distance-based Methods 1. Nearest neighbor Classification 2. Metrics and Tangent Distance 3. Fuzzy Classification 4. Linear Discriminant Functions 1. Hyper plane Geometry 2. Gradient Descent and Perceptrons 3. Minimum Squared Error Procedures 4. Support Vector Machines 5. Artificial Neural Networks 1. Biological Motivation and Back-Propagation 5. Non-Metric Methods 1. Recognition with Strings 2. String Matching 6. Algorithm-Independent Machine Learning 1. No-Free Lunch Theorem 2. Bias and Variance 3. Resampling for Estimation 4. Bagging and Boosting 5. Estimation of Misclassification 6. Classifier Combination 7. Unsupervised Learning and Clustering 1. Unsupervised Learning and Clustering 8. Appendix 1. Normal Distribution 2. Statistical Tests [2] Algorithms Analysis and Design (Grade: A) http://www.cse.buffalo.edu/~xinhe/cse531/ http://www.cse.buffalo.edu/~xinhe/cse531/syllabus.html Textbook: Introduction to Algorithms (2e), Thomas H. Cormen, Charles E. Leiserson, Ronald Rivest, and Clifford Stein, MIT Press, 2001 Week 1: Jan. 15 & 17
  3. 3. " Topics: Asymptotic Notations and Analysis, Solving Recurrences, Master Theorem " Reading Assignment: o CLRS: Chapter 3 & 4 " Supplementary Reading: o CLRS: Chapter 1 & 2 Week 2: Jan. 22, 24 " Topics: Asymptotic Notations and Analysis, Solving Recurrences, Master Theorem " Reading Assignment: o Same as Week 1. Week 3: Jan 29, 31 " Topics: Divide and Conquer, including Selection, Matrix Multiplication, closest pair of points " Reading Assignment: o CLRS: Chapters 9, Sections 28.1, 28.2, 33.4 Week 4: Feb 5, 7 " Topics: Dynamic Programming with Knapsack problem, Longest Common Subsequence, Matrix-chain multiplication, " Reading Assignment: o CLRS: 15 Week 5: Feb 12, 14 " Topics:... continue with dynamic programming. Application of dynamic programming in bioinformatics problems. Week 6: Feb 19, 21 " Topics: Greedy Algorithms. " Reading Assignment: o CLRS: Section 16.1, 16.2. " Midterm I: Tue. Feb 19 Week 7 Feb 26, 28 " Topics: o Introductory Graph Algorithms, Minimum Spanning Tree problem " Reading Assignment: o CLRS: Chapters 22, 23 Week 8: Mar 4, 6 " Topics: o Breadth-First-Search, Depth-First-Search, and applications " Reading Assignment: o Selected sections of Chapters 22. Week 9: Mar 10-15 " Topics: o Spring Recess " Reading Assignment: o Week 10: Mar 18, 20 " Topics: o Single-Source Shortest Path and All-Pairs Shortest Path Problems " Reading Assignment:
  4. 4. o Selected sections of Chapters 24 and 25. Week 11: Mar 25, 27 " Topics: o Maximum Flow Problem " Reading Assignment: o Chapter 26. " Midterm II: Thu. March 27 Week 12: Apr 1, 3 " Topics: o Same as the last week. " Reading Assignments: o Same as the last week. Week 13: Apr 8, 10 " Topics: NP-Completeness " Reading Assignment: o CLRS: Chapter 34 Week 14: Apr 15, 17 " Topics: Approximation Algorithms " Reading Assignment: o CLRS: Chapter 35 Week 15: Apr 22, 24 " Topics and Readings: Same as last week. [3] Introduction to Operating System (Grade: A) http://www.cse.buffalo.edu/~bina/cse421/fall2005/index.html Textbook: Modern Operating Systems, 3/E Andrew S. Tanenbaum ISBN-13: 978-0136006633 Publisher : prentice-hall Inc. Date (Week Reading Topics Covered of) Material General Introduction; Course Outline; Computer 8/29 hardware review; Operating Systems (OS) fundamentals. Ch.1, Ch.2 Different OS structures. Introduction to Nachos operating systems. Project 1 Project 1 9/5 discussion: System calls and Exception handling handout Different OS structures. Process model; Process 9/14 description and control; examples from Nachos. Ch.3 The Thread Model; Implementing concurrency; 9/19 multithreaded programs; Nachos threads. Ch.4, Nachos Process communication and synchronization: 9/26 semaphores, monitors, messages, and locks. Ch. 6 Classical IPC Problems and solutions. Exam Review. 10/3 Project 2 discussion. Ch.6 Project 2 discussion; Exam 1: 10/12 Project 2 handout; Exam 10/10 on Ch.1- 4 and 6.
  5. 5. CPU scheduling: long term, short term and real-time 10/17 scheduling. Ch. 5 Deadlock detection and resolution; avoidance and 10/24 prevention. Ch.7 Memory management. Virtual memory and demand Ch. 8 and 10/31 paging. Project 3 discussion Ch.9; Project 3 handout File system design and implementation; IO subsystem Ch. 10, Ch.11, 11/7 and mass storage and Ch.12 Security and protection; authorization and authentication; 11/14,21 Trusted systems. 11/23,25: Thanks Giving Break Ch. 14 and 15 Sample operating systems: Windows XP, .net, and Linux Ch.19 and Ch. 11/28 21 Review for the final exam. Final exam during final exam 12/5 week. Important Dates Due Date 9/27 Project 1 10/25 Project 2 12/6 Project 3 10/12 Exam 1 Finals Week Final Exam [4] CSE 560 Data Models and Query Languages (Grade: A) [4] CSE 560 Data Models and Query Languages (Grade: A) http://www.cse.buffalo.edu/~chomicki/cse560-s06.html Texts: # A. Siberschatz, H. Korth, S. Sudarshan. Database System Concepts, 5th edition, McGrawHill, 2006. # G. Koch, K. Loney. Oracle 9i: The Complete Reference Osborne McGraw-Hill, 2002. Course summary: 1. Conceptual database design. ER (Entity-Relationship) data model: basic concepts, design principles; mapping ER schemas to relational schemas. 2. Logical database design. Relational data model: basic concepts; relational database design: functional and multi-valued dependencies, normal forms. 3. Relational query languages. Relational algebra and calculus; SQL2; JDBC.
  6. 6. 4. Object data models. Basic object-oriented concepts; object-relational model; object-oriented constructs and recursion in SQL:1999 and commercial systems. 5. XML databases. XML data model; Document Type Definitions; XML Schema; XML query languages: XPath, XQuery; representing XML documents in relational databases. 6. Integrity constraints and triggers. 7. Indexing and query optimization from a user's point of view. Index definition and use; statistics; query optimizers; query plans. 8. Data integration. [5] CSE 574 Machine Learning (Grade: B+) http://www.cse.buffalo.edu/faculty/mbeal/cse574/ Textbook 1) T. M. Mitchell (1997) Machine Learning, McGraw Hill. 2) D. J. C. MacKay (2003) Information Theory, Inference, and Learning Algorithms, Cambridge University Press (I highly recommend this book; it is also very cheap and available free for online viewing). Wk Date Topic, Assignments 1 Aug 29 Introduction and Overview of course Aug 31 Probability, Entropy, and Inf.Th. (1) 2 Sep 05 Probability, Entropy, and Inf.Th. (2) Assignment 1 out: Statistical Foundations Sep 07 Probability, Entropy, and Inf.Th. (3) 3 Sep 12 Decision Trees (1) Sep 14 Decision Trees (2) 4 Sep 19 no class Sep 21 Latent Variable Models for Clustering & DimReduct (1) Assignment 1 due Assignment 2 out: Decision Trees 5 Sep 26 Latent Variable Models for Clustering & DimReduct (2) Sep 28 Neural Networks (1)
  7. 7. 6 Oct 03 Neural Networks (2) Oct 05 Neural Networks (3) Assignment 2 due 7 Oct 10 Midterm Review Oct 12 class cancelled 8 Oct 17 class cancelled Oct 19 Midterm Exam 9 Oct 24 Expectation-Maximization (EM) for hidden variables (1) Oct 26 Expectation-Maximization (EM) for hidden variables (2) Assignment 3 out: Mixture models & Dimensionality Reduction 10 Oct 31 Graphical Models (1) Nov 02 Graphical Models (2) 11 Nov 07 Time Series Models (1) Assignment 4 out: Neural Networks Nov 09 Time Series Models (2) Assignment 3 due 12 Nov 14 Reinforcement Learning Nov 16 Capacity of the single neuron / VC theory Assignment 5 out: EM & Graphical Models Project component begins 13 Nov 21 Support Vector Machines (1) Assignment 4 due Nov 23 no lecture: Fall Recess 14 Nov 28 Support Vector Machines (2) Nov 30 Maximum-Entropy and Conditional Random Fields 15 Dec 05 Project open discussion session Assignment 5 due Dec 07 Course Syllabus Review (LAST LECTURE) Exam room allocation announced 16 Dec 11 MONDAY Final Exam 03:30 PM - 06:30 PM ALUMNI 97 This examination dominates Prof. Scott's exam at the same time. It is your responsibility to arrange an alternative time for Prof. Scott's examination. Please arrive 10 minutes early to peruse the examination paper. Dec 14 THURSDAY Projects electronically submitted by end of day . 17 Dec 18 MONDAY Project examinations. Times TBA.
  8. 8. [6] CSE 589 Modern Networking Concepts (Grade: B+) Textbook: Computer Networking: A Top Down Approach Featuring the Internet, 3rd edition by Jim Kurose, Keith Ross Addison-Wesley. Syllabus Chapter 1: roadmap 1.1 What is the Internet? 1.2 Network edge 1.3 Network core 1.4 Network access and physical media 1.5 Internet structure and ISPs 1.6 Delay & loss in packet-switched networks 1.7 Protocol layers, service models 1.8 History Chapter 2 2.1 Principles of app layer protocols ❍ processes ❍ app requirements 2.2 HTTP 2.3 FTP 2.4 Electronic Mail SMTP, POP3, IMAP 2.5 DNS 2.6 P2P and CDN 2.7 Socket programming with TCP (RIY) 2.8 Socket programming with UDP (RIY) 2.9 Building a Web server (RIY) Chapter 3 outline 3.1 Transport-layer services 3.2 Multiplexing and de-multiplexing 3.3 Connectionless transport: UDP 3.4 Principles of reliable data transfer 3.5 Connection-oriented transport: TCP segment structure, reliable data transfer, flow control, connection management 3.6 Principles of congestion control 3.7 TCP congestion control Chapter 4: Network Layer 4. 1 Introduction 4.2 Virtual circuit and datagram networks 4.3 What’s inside a router 4.4 IP: Internet Protocol, Datagram format, IPv4 addressing, ICMP, IPv6 4.5 Routing algorithms, Link state, Distance Vector, Hierarchical routing 4.6 Routing in the Internet, RIP, OSPF, BGP
  9. 9. 4.7 Broadcast and multicast routing Chapter 5: Link Layer 5.1 Introduction and services 5.2 Error detection and correction 5.3 Multiple access protocols 5.4 Link-Layer Addressing 5.5 Ethernet 5.6 Hubs and switches 5.7 PPP 5.8 Link Virtualization: ATM and MPLS Chapter 6 outline 6.1 Introduction Wireless 6.2 Wireless links, characteristics– CDMA 6.3 IEEE 802.11, wireless LANs (“wi-fi”) 6.4 Cellular Internet Access, – architecture – standards (e.g., GSM) Mobility 6.5 Principles: addressing and routing to mobile users 6.6 Mobile IP 6.7 Handling mobility in cellular networks 6.8 Mobility and higher-layer protocols 6.9 Summary Chapter 7 outline 7.1 Multimedia Networking Applications 7.2 Streaming stored audio and video 7.3 Real-time Multimedia: Internet Phone study 7.4 Protocols for Real-Time Interactive Applications – RTP,RTCP,SIP 7.5 Distributing Multimedia: content distribution networks 7.6 Beyond Best Effort 7.7 Scheduling and Policing Mechanisms 7.8 Integrated Services and Differentiated Services 7.9 RSVP Chapter 8 roadmap 8.1 What is network security? 8.2 Principles of cryptography 8.3 Authentication 8.4 Integrity 8.5 Key Distribution and certification 8.6 Access control: firewalls 8.7 Attacks and counter measures 8.8 Security in many layers

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