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
1. Maximum-Likelihood Estimation
4.
5.
6.
7.
8.
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
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
Non-Metric Methods
1. Recognition with Strings
2. String Matching
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
Unsupervised Learning and Clustering
1. Unsupervised Learning and Clustering
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
"
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:
o
Selected sections of Chapters 24 and 25.
Week 11: Mar 25, 27
"
Topics:
o
Maximum Flow Problem
"
Reading Assignment:
o
"
Week
"
o
"
o
Week
"
"
o
Week
"
"
o
Week
"
Chapter 26.
Midterm II: Thu. March 27
12: Apr 1, 3
Topics:
Same as the last week.
Reading Assignments:
Same as the last week.
13: Apr 8, 10
Topics: NP-Completeness
Reading Assignment:
CLRS: Chapter 34
14: Apr 15, 17
Topics: Approximation Algorithms
Reading Assignment:
CLRS: Chapter 35
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: 9780136006633 Publisher : prentice-hall Inc.
Date (Week
of)
8/29
9/5
Topics Covered
General Introduction; Course Outline; Computer
hardware review; Operating Systems (OS) fundamentals.
Different OS structures.
Introduction to Nachos operating systems. Project 1
discussion: System calls and Exception handling
Reading
Material
Ch.1, Ch.2
Project 1
handout
9/14
Different OS structures. Process model; Process
description and control; examples from Nachos.
Ch.3
9/19
The Thread Model; Implementing concurrency;
multithreaded programs; Nachos threads.
Ch.4, Nachos
9/26
Process communication and synchronization:
semaphores, monitors, messages, and locks.
Ch. 6
10/3
Classical IPC Problems and solutions. Exam Review.
Project 2 discussion.
Ch.6
Project 2 discussion; Exam 1: 10/12
10/10
Project 2
handout; Exam
on Ch.1- 4 and
6.
10/17
CPU scheduling: long term, short term and real-time
scheduling.
Ch. 5
10/24
Deadlock detection and resolution; avoidance and
prevention.
Ch.7
10/31
11/7
11/14,21
11/28
12/5
Important
Dates
9/27
10/25
12/6
10/12
Finals Week
Memory management. Virtual memory and demand
paging. Project 3 discussion
Ch. 8 and
Ch.9; Project 3
handout
File system design and implementation; IO subsystem
and mass storage
Ch. 10, Ch.11,
and Ch.12
Security and protection; authorization and authentication;
Trusted systems. 11/23,25: Thanks Giving Break
Ch. 14 and 15
Sample operating systems: Windows XP, .net, and Linux
Ch.19 and Ch.
21
Review for the final exam. Final exam during final exam
week.
Due Date
Project 1
Project 2
Project 3
Exam 1
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.
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
1
Aug 29
Introduction and Overview of course
Aug 31
Probability, Entropy, and Inf.Th. (1)
Sep 05
Probability, Entropy, and Inf.Th. (2)
Assignment 1 out: Statistical Foundations
Sep 07
Probability, Entropy, and Inf.Th. (3)
Sep 12
Decision Trees (1)
Sep 14
Decision Trees (2)
Sep 19
no class
Sep 21
Latent Variable Models for Clustering & DimReduct (1)
Assignment 1 due
Assignment 2 out: Decision Trees
Sep 26
Latent Variable Models for Clustering & DimReduct (2)
Sep 28
Neural Networks (1)
Oct 03
Neural Networks (2)
Oct 05
Neural Networks (3)
Assignment 2 due
Oct 10
Midterm Review
Oct 12
class cancelled
2
3
4
5
6
7
Topic, Assignments
8
9
10
11
12
13
14
15
16
17
Oct 17
class cancelled
Oct 19
Midterm Exam
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
Oct 31
Graphical Models (1)
Nov 02
Graphical Models (2)
Nov 07
Time Series Models (1)
Assignment 4 out: Neural Networks
Nov 09
Time Series Models (2)
Assignment 3 due
Nov 14
Reinforcement Learning
Nov 16
Capacity of the single neuron / VC theory
Assignment 5 out: EM & Graphical Models
Project component begins
Nov 21
Support Vector Machines (1)
Assignment 4 due
Nov 23
no lecture: Fall Recess
Nov 28
Support Vector Machines (2)
Nov 30
Maximum-Entropy and Conditional Random Fields
Dec 05
Project open discussion session
Assignment 5 due
Dec 07
Course Syllabus Review (LAST LECTURE)
Exam room allocation announced
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 .
Dec 18
MONDAY Project examinations. Times TBA.
[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
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