Formal Languages and Automata
 Instructor: T.K prasad Textbook:Thomas A. Sudkamp: Languages and Machines, 3rd Edition, Addison-Wesley, Download Slides from here
|
ARTIFICIAL INTELLIGENCE INSTRUCTOR: Geiger, Davi
Books: Artificial Intelligence: A Modern Approach. (Second Edition) by Stuart Russell and Peter Norvig
Syllabus:
1. Introduction to Artificial Intelligence.
Intelligent Agents (ppt)
2. Problem Solving: Search, Informed Search, Game Playing
Search (ppt)
Informed Search (ppt)
Adversarial Search (ppt)
3.Computer Vision and Inference
Introduction to Computer Vision and Inference (ppt)
4. Knowledge and Logic
Propositional or Boolean Logic (ppt)
First Order Logic (FOL) (ppt)
Inference in FOL (ppt)
The extra class is this Friday, April 11th, at 3 pm (*not 5 pm*) in
room 109, WWH. We will complete the inference in first order logic.
5. Uncertainty & Learning
Uncertainty and Probability (ppt)
Bayesian Probability (ppt)
Learning (ppt)
Evaluation: 60% final exam, 40% mid term exam. Hoemworks are provided
to help students learn the material, but no grading will be done.
Midterm Exam: Tuesday March 11th.
MidtermExam.Solution (pdf)
Final Exam: May 13th, 7pm to 9 pm
It covers all the material in the course, including chapter 18th, but not chapter 20th.
Last year, 2007, exam and solution is here (pdf)
Homeworks: Students are encouraged to work with others. There is no
grading. Solutions will be posted two weeks after the problems are
posted.
Homework1 (txt)
Posted January 30th.
Solution1 (pdf)
Posted February 20th.
Homework2 (txt)
Posted February 20th.
Solution2 (pdf)
Posted February 27th.
Homework3 (txt)
Posted April 1st.
Solution3 (pdf)
Posted April 20th.
Homework4 (txt)
Posted April 20th.
Solution4 (pdf)
Posted April 20th.
Atomic and Laser Physics
Course description
PHY332
covers the quantum theory of simple atoms and atomic spectra, and also
the basic principles of lasers. The first part of the course covers the
physics of atoms and atomic spectra, beginning with hydrogen and then
moving on to multi-electron atoms. The second part gives an introduction
to laser physics, with emphasis on the basic principles of
amplification by stimulated emission.
The aims of the course are as follows:
- to understand the quantum theory of the hydrogen atom and other simple atoms;
- to describe the main features of atomic spectra ;
- to understand the basic principles of laser operation;
Recommended books
- Demtroder W, "Atoms, molecules and photons" (Springer-Verlag)
- Haken H and Wolf H C, "The physics of atoms and quanta" (Springer-Verlag)
Other useful books include
- Eisberg R and Resnick R, "Quantum physics of atoms, molecules, solids, nuclei and particles " (Wiley)
- Wilson J and Hawkes J, "Optoelectronics: an introduction" (Prentice Hall)
- Smith F G and King T A, "Optics and Photonics " (Wiley)
- Silfvast, W T, "Laser fundamentals" (Cambridge)
Lecture notes
The lecture notes are given in pdf format.
They can be viewed and printed using Acrobat Reader. If you do not have
this programme on your computer, you can download it free of charge
from here: 
| Notes | Topic |
|---|---|
| Part I | Atomic Physics |
| Table of contents | |
| 1 | Introduction |
| 2 | Hydrogen |
| 3 | Radiative transitions |
| 4 | The shell model and alkali spectra |
| 5 | Fine structure |
| 6 | External fields |
| 7 | Helium and the exchange force |
| Part II | Laser Physics |
| 8 | Population inversion, stimulated emission and gain. Cavities. Gas & solid state lasers |
APPLIED PROBABILITY
Instructor: Tina Kapur and Rajeev Surati
Course Description
Focuses on modeling, quantification, and analysis of uncertainty by teaching random variables, simple random processes and their probability distributions, Markov processes, limit theorems, elements of statistical inference, and decision making under uncertainty. This course extends the discrete probability learned in the discrete math class. It focuses on actual applications, and places little emphasis on proofs. A problem set based on identifying tumors using MRI (Magnetic Resonance Imaging) is done using Matlab.Text: Fundamentals of Applied Probability Theory, Al Drake
Lecture Notes
lecture1.pptlecture2.ppt
lecture3.ppt
lecture4.ppt
Lecture_05.pdf
Lecture_05.ppt
Lecture Videos
07-02-01: Introduction, Algebra of Events, Conditional Probability07-03-01: Independence, Bayes Theorem, Probability Mass Functions
07-05-01: Conditional PMFs, Probability Density Functions
07-06-01: PDFs and Image Guided Surgery
07-09-01: Bayesian Segmentation of MRI Images
Problem Sets
ground_truth_test_imgMRI.tar
MRI_Link
mri_read.m
mri_test_img
Problem_Set_04.txt
Problem_Set_05.txt
pset01.txt
pset04.txt
SEG.tar
SEG_Link
Applications of the Internet
Instructor:
Prof. Shie-Yuan Wang
Textbook
· James
F. Kurose and Keith W. Ross, “Computer Networking: A Top-Down Approach
Featuring the Internet,” Addison Wesley, 2001, ISBN: 0-201-47711-4.
Course slides:
Analysis & Design of Accounting Information Systems
Jagdish S. Gangolly
Department of Accounting & Law
State University of New York at Albany
Department of Accounting & Law
State University of New York at Albany
I shall be adding to these notes as we go along. You can download the file and print the pages that you need. You will find the instructions for viewing postscript files on the course homepage at
http://www.albany.edu/faculty/gangolly/acc682/fall99/
- Contents
- Introduction to Systems I
- Introduction to Systems II
- The Functional Model
- About this document ...
ANALOG CIRCUITS AND SYSTEMS DESIGN
Instructor: Paulo F. Ribeiro
Summary:
Feedback
principles and electronic circuit theory and device
theory applied to multi-stage transistor amplifiers.
Detailed study of operational amplifier specs,
nonidealities, and compensation. Introduction to filter
theory and practical realizations. Power supply design: rectifier
circuits, linear and switching regulators. Nonlinear circuits:
comparators, multipliers, Schmitt trigger, S/H circuits,
multivibrators and oscillators. Introduction to noise
analysis and low noise design. Emphasis on realization of
designs using commercially available IC's. Design
experience emphasized in projects and the laboratory.
Lecture Notes
- Chapter 6: PowerPoint
Chapter 7: PowerPoint - Chapter 8: PowerPoint
- Chapter 9: PowerPoint
- Chapter 10: PowerPoint
- Chapter 11: PowerPoint
- Nyquist Pllots (Margains)Word
- Bode Plots (Margins) Word
- Class-B Output Satge Amplfier (PPT)
- Root-Locus Design GUI (Word)
- Simulink Tutorial
- The 741 OP-AMP (Tutorial 1)
- The 741 OP-AMP (Tutorial 2)
- The 741 OP-AMP (Tutorial 3) (Webpage)
- Phase-Locked Loop (Design Fundamentals - Web Link)
- OP-AMP History
- Cadence Tutorial
- Layout Tutorial
- RLC on Simulink
- Wien-Bridge Oscillator
- Wien-Bridge Oscilator (Additional Notes)
- Wien-Bridge Oscilator Design (1)
- Wien-Bridge Oscilator Design (2)
- PLC - Primer (PDF)
- Harmonic Distortion
- Output Stages - Classification and Application Notes
- Hardware Book (Pinouts)
An Engineering Approach to Computer Networking
by S. Keshav ISBN 0-201-63442-2 * Hardcover * 688 pages * 1997
Here are a set of slides (in Microsoft PowerPoint and HTML format) that cover the material in the book. The HTML was automatically generated by PowerPoint and needs a browser that supports frames. Feel free to copy and reuse this material (acknowledge me if you're feeling virtuous: I have not put my name on any of the slides).
Chapter 1: Atoms, Bits, and Networks
Chapter 2: The Telephone Network
Chapter 3: The Internet
Chapter 4: ATM Networks
Chapter 5: Protocol Layering
Chapter 6: System Design
Chapter 7: Multiple Access
Chapter 8: Switching
Chapter 9: Scheduling
Chapter 10: Naming and Addressing
Chapter 11: Routing
Chapter 12: Error Control
Chapter 13: Flow Control
Chapter 14: Traffic Management
Chapter 15: Common Protocols
Chapter 16: Protocol Implementation
The REAL network simulatorPowerPoint 95
PowerPoint 97
HTML
ALGORITHMS FOR BIOINFORMATICS
Lecture Slides
- Introduction to Molecular Biology Concepts and Tools - Text Overview Slides
- Introduction to Algorithm Analysis and Asymptotic- Slides (PPT)
- Lectures on Sequence Alignment - Slides (PPT)
- Lectures on Graph Theory and Sequence Assembly
- Graph Theory (PPT)
- Sequence Assembly (PPT)
- Lectures on Physical Mapping
- Dr. Raymer's Slides (PPT)
- Dr. Krane's Slides (PDF)
- Lectures on Proteins and Protein Folding
- Proteins (PPT)
- Protein Folding (PPT)
DESIGN & ANALYSIS OF ALGORITHMS
Instructor: David Luebke
Description:
This course will provide a rigorous introduction to the design and
analysis of algorithms. We will discuss classic problems (e.g., sorting,
traveling salesman problem), classic algorithm design strategies (e.g.,
divide-and-conquer, greedy approaches), and classic algorithms and data
structures (e.g., hash tables, Dijkstra's algorithm). We will also
analyze algorithm complexity throughout, and touch on issues of
tractibility such as "NP-Completeness". Texts:Required: Introduction to Algorithms (Second Edition) by Cormen, Leiserson, Rivest, and Stein, McGraw-Hill (2001).
This
book is similar to the first edition, so you could probably get by with
only the first edition. However, all homework problems assigned from
the book will be referenced from the second edition; it is your
responsibility to find a way to look them up. I strongly recommend that
you buy the text rather than borrow it; this is one of only two text
books that I still use on a regular basis. It is an indispensable
reference.
Lectures:
A tentative schedule of lecture topics is given below. The "CULTURE"
topics represent interesting but non-essential material from fields such
as computational geometry and computer graphics; they add some variety
to the schedule but also give us some slack if we get behind schedule.
If we cover a "culture" topic in class, you will be tested on it.
Number
|
Topic
|
Source
|
Text
|
1
|
--
| ||
2
|
3.1-3.2
| ||
3
|
4.1
| ||
4
|
4.3, 6.1-6.2
| ||
5
|
6, 7.1-7.3
| ||
6
|
7.4
| ||
7
|
5.1-5.3
| ||
8
|
5.4 last section
| ||
9
|
8.1-8.2
| ||
10
|
8.3-8.4
9.1-9.2 | ||
11
|
9.3
| ||
12
| |||
13
|
12.1-12.3
| ||
14
|
13.1-13.2
| ||
15
|
13.3-13.4
| ||
16
|
--
| ||
17
|
11.1-11.2
| ||
18
|
11.3-11.4
| ||
19
|
11.3-11.4
| ||
20
|
14.1-14.2
| ||
21
|
14.3
| ||
22
|
22.1-22.3
| ||
23
|
22.3
| ||
24
|
23.1
| ||
25
|
23.2
| ||
26
|
24.1-24.3
| ||
27
| |||
28
|
21.1-21.3, 23.2
| ||
29
|
17.1-17.2
| ||
30
|
17.3-17.4
| ||
31
|
15.1, 15.3
| ||
32
|
15.4
| ||
33
| |||
34
|
16.1-16.2
| ||
35
|
34.1-34.2
| ||
36
|
34.1-34.2
| ||
37
|
34.3-4
| ||
38
|
34.3-4
| ||
39
|
--
|
Data structures and Algorithms
Instructor: Rada Mihalcea
Textbook: Data Structures and Algorithm Analysis in C++ M.A.Weiss
Download slides from here
| Lecture | Reading material |
| Introduction. Course Overview. [ppt] | - |
| Algorithm Analysis I. [ppt] | Weiss, chap.2 |
| Algorithm Analysis II / ADT [ppt] | Weiss, chap.2 |
| Algorithm Analysis II [ppt] | Weiss, chap.2 |
| Arrays [ppt] | Weiss, chap.3 |
| Lists [ppt]. | Weiss, chap.3 |
| More Lists [ppt]. | Weiss, chap.3 |
| Stacks [ppt] | Weiss, chap.3 |
| Queues [ppt] | Weiss, chap.3 |
| Stack Applications [ppt] | - |
| Trees [ppt] | Weiss, chap.4 (4.1) |
| Trees [ppt] | Weiss, chap.4 |
| Binary search trees [ppt] | Weiss, chap.4 |
| Search Trees [ppt]. | Weiss, chap.4 |
| Search Trees [ppt]. | Weiss, chap.4 |
| Priority Queues. Heaps. [ppt]. | Weiss, chap.6 |
| Applications using Trees. [ppt] | Weiss, sec.10.1.2 |
| Dictionaries. Skip Lists. [ppt] | Weiss, chap.5, sec.10.4 |
| Hashing [ppt] | Weiss, chap.5, sec.10.4 |
| Sorting (I) [ppt]. | Weiss, chap.7 |
| Sorting (II) [ppt] | Weiss, chap.7 |
| Graphs (I) [ppt]. | Weiss, chap.9 |
ADVANCED PROGRAMMING
Advanced Computer Architecture Slides
Course Content
Instruction set, memory management and hierarchy, input/output and buses, pipelining techniques, branch prediction, RISC architectures, VLIW architectures and specific compiling techniques, superscalar architectures, out of order execution, parallel architectures and multiprocessors.Requirements
TSEA04 (Switching Theory and Logical Design),TSEA19/20 (Computer Hardware and Architecture)
Reading Instructions
Introduction:Outline, Basic computer architecture and organization, Basic functions of a computer and its main components, The von Neumann architecture, Historical perspective.
Some basic issues are recapitulated which are supposed to be known from previous courses.
(2.1, 2.2, 3.1, 3.2, 3.3, 3.4, 5.1, 5.3, 5.4, 6.1, 6.2, 6.3, 6.4, 6.5, 6.6, Chapter 9, 10.1, 10.3, 14.1, 14.2, 14.3, 15.1, 15.2)
The Memory System and its Organization:
Memory hierarchy, Organization of internal memories, Cache memories, Memory Management.
(4.1, 4.2, 4.3, 7.3)
Instruction Pipelining:
Organization of pipelined units, Pipeline hazards, Reducing branch penalties, Branch prediction strategies.
(11.1, 11.2, 11.3, 11.4, 12.5)
Reduced Instruction Set Computer (RISC) Architectures:
An analysis of instruction execution for code generated from high-level language programs, Compiling for RISC architectures, Main characteristics of RISC architectures, RISC-CISC trade-offs.
(12.1, 12.2, 12.4, 12.8)
Superscalar Architectures:
Instruction level parallelism and machine parallelism, Hardware techniques for performance enhancement, Data dependencies, Policies for parallel instruction execution, Limitations of the superscalar approach.
(13.1, 13.2)
Very Large Instruction Word (VLIW) Architectures:
The VLIW approach - advantages and limitations. Compiling for VLIW architectures. The Merced (Itanium) architecture.
(13.7)
Architectures for Parallel Computation:
Parallel programms, Performance of parallel computers, A classification of computer architectures, Array processors, Multiprocessors, Multicomputers, Vector processors. Cache Coherence and the MESI Protocol.
(16.1, 16.2, 16.3, 16.6)
Architectures for Low Power Consumption: The Crusoe Processors
The Technology Behind Crusoe Processors.
Lectures
- Lecture 1.
- Lecture 2.
- Lecture 3.
- Lecture 4.
- Lecture 5.
- Lecture 6.
- Lecture 7.
- Lecture 8.
- Lecture 9.
- Lecture 10.
- Lecture 11.
- Lecture 12.
- Exam questions and reading instructions: part 1 (slides) and part two.
Advanced Computer Architecture
Description and objectives
Studying the architecture, organization and use of the newest general
purpose (micro)processors currently on the market, and the latest
research developments in computer architecture.
Architectures exploiting instruction-level parallelism (ILP),
thread-level and task-level parallelism are treated. Starting from basic
architecture concepts we will end with discussing the latest commercial
processors (e.g., Pentium 4 multi-core, EPIC processors like Itanium,
and embedded processors such as the TriMedia), and academic processors
(like TRIPS).
This course also treats how processors can be combined in a
multiprocessing platform, e.g. by using a Network-on-Chip.
Interprocessor communication issues will be dealt with. Furthermore new
code generation techniques needed for exploiting ILP will be treated.
Special emphasis will be on quantifying design decisions in terms of
performance and cost.
Topics:
Basic principles (like instruction set design), pipelining and its consequences; VLIW (very long instruction word) architectures, Superpipelined, Superscalar, SIMD (single instruction, multiple data, used in vector and sub-wordparallel processors) and MIMD (multiple instruction, multiple data) architectures; SMT (Simultaneous Multi-Threading); Out-of-order and speculative execution; Branch prediction; Data (value) prediction; Design of advanced memory hierarchies; Memory coherency and consistency; Multi-threading; Exploiting task-level and instruction-level parallelism; Inter-processor communication models; Input and output; Network Communication Architecture; and Networks-on-Chip.Book and Handouts
 | Computer Architecture: A Quantitative Approach; 4th ed. John L. Hennessy and David A. Patterson Morgan Kaufmann Publishers ISBN 9780123704900 |
Slides
** will be added and updated during the course period **- Overview slides (including preliminary schedule)
- Topic 1: Computer Systems Overview
- Topic 2: Crash course on MIPS
- MIPS instruction set
- MIPS design 1: single cycle and multi-cycle implementations
- MIPS design 2: pipelined implementation
- Topic 3: Instruction-set design
- Topic 4: Instruction-Level Parallel (ILP) architectures
- The best of both worlds: EPIC or the Itanium architecture
- Topic 5: Exploiting ILP with Software approaches
- Topic 6: SMT: simultaneous multi-threading
- Guest lecture by Wouter van der Put: Time Predictability of a Computer System
- Topic 7: Multi-Processors
- part 1
- Including Synchronization, Memory Coherence, and Memory Consistency
- Topic 8 Caches and Memory Hierarchy;
ADVANCED ALGORITHMS
Course Description
Algorithm design and analysis is a fundamental and important part of computer science. This course introduces students to advanced techniques for the design and analysis of algorithms, and explores a variety of applications.
The topics and applications that we plan to cover include hashing, bloom filters, scheduling, network design, online load balancing, algorithms in machine learning, boosting (in the context of learning), Markov chains and the MCMC method, byzantine agreement, internet algorithms, and nearest neighbor algorithms. Enroute, we will encounter various useful ideas, including randomization, probabilistic analysis, amortized analysis, competitive analysis, eigenvalues, linear and semi-definite programming, high dimensional geometry, and random walks.
Reference books
There is no textbook required for the course. Lecture notes will be made available from the course web page. Please visit the course webpage frequently for extra reading material. Reference books for each topic are listed below. Some of these books (as specified below) have been placed on reserve in the Wendt library.
Approximation Algorithms
Algorithm design and analysis is a fundamental and important part of computer science. This course introduces students to advanced techniques for the design and analysis of algorithms, and explores a variety of applications.
The topics and applications that we plan to cover include hashing, bloom filters, scheduling, network design, online load balancing, algorithms in machine learning, boosting (in the context of learning), Markov chains and the MCMC method, byzantine agreement, internet algorithms, and nearest neighbor algorithms. Enroute, we will encounter various useful ideas, including randomization, probabilistic analysis, amortized analysis, competitive analysis, eigenvalues, linear and semi-definite programming, high dimensional geometry, and random walks.
Reference books
There is no textbook required for the course. Lecture notes will be made available from the course web page. Please visit the course webpage frequently for extra reading material. Reference books for each topic are listed below. Some of these books (as specified below) have been placed on reserve in the Wendt library.
Approximation Algorithms
- Vijay Vazirani, Approximation Algorithms, Springer, 2001. (on reserve)
- Dorit S. Hochbaum (ed.), Approximation Algorithms for NP-hard Problems, PWS Publishing, 1997. (on reserve)
- Rajeev Motwani and Prabhakar Raghavan, Randomized Algorithms, Cambridge University Press, 2000. (on reserve)
- Michael Mitzenmacher and Eli Upfal, Probability and Computing, Cambridge University Press, 2005.
- Allan Borodin and Ran El-Yaniv, Online Computation and Competitive Analysis, Cambridge University Press, 2005. (on reserve)
- Michael Kearns and Umesh Vazirani, An Introduction to Computational Learning Theory, The MIT Press, 1994. (on reserve)
- Jon Kleinber and Eva Tardos, Algorithm Design, Addison-Wesley, 2006. (on reserve)
- T. Cormen, C. Leiserson, R. Rivest, and C. Stein, Introduction to Algorithms, 2nd edition, 2001.
- 09/05 W [PDF] Intro, greedy algorithms: scheduling, MST. (K&T §4, §5)
- 09/07 F [PDF] Set cover, Divide & Conquer, Dynamic programming. (K&T §5, §6, §11.3)
- 09/10 M [PDF] Dynamic programming. (K&T §6)
- 09/12 W [PDF] Network flow. (K&T §7)
- 09/14 F [PDF] Network flow applications, matchings. (K&T §7)
- 09/17 M [PDF] Randomized algorithms, Karger's min-cut algorithm. (K&T §13)
Here are some lecture notes by Avrim Blum on how to speed-up Karger's algorithm. - 09/19 W [PDF] Randomized load balancing and hashing. (K&T §13.10, §13.6, M&R §8.4, §8.5)
- 09/21 F [PDF] Bloom filters, NP-completeness. (M&R §8.4, §8.5, M&U §5.5)
See also, this survey on the applications of bloom filters by Broder & Mitzenmacher. - 10/01 M [PDF] NP-completeness contd., Approximation algorithms. (K&T §8, Vaz. §1)
- 10/03 W [PDF] Approximation via local search.
- 10/08 M [PDF] Linear programming, LP rounding. (Vaz. §14)
- 10/10 W [PDF] Randomized rounding, concentration bounds. (M&R §3.2, §4.1, §4.2)
- 10/15 M [PDF] Randomized rounding (contd.), LP duality. (M&R §4.2, Vaz. §12)
- 10/17 W [PDF] LP duality, Primal-dual algorithms. (Vaz. §12, 15)
- 10/19 F [PDF] Primal-dual algorithms. (Vaz. §15)
- 10/22 M [PDF] Semi-definite Programming. (Vaz. §26)
- 10/24 W [PDF] SDP (contd.), Streaming algorithms.
- 10/26 F [PDF] Streaming algorithms (contd.).
See this survey by Muthu Muthukrishnan for some motivation behind, and math used in, streaming algorithms. - 10/29 M [PDF] Online algorithms & competitive analysis. (B&E-Y §1)
Here is a nice presentation by Pat Riley & Elly Winner about different approaches to evaluating online algorithms. - 10/31 W [PDF] Caching/Paging, k-server problem. (B&E-Y §3, §4, §10)
- 11/05 M [PDF] Caching lower bound based on Yao's principle, Work function algorithm. (B&E-Y §8.4, §10, §12)
For a complete analysis of the work function and other k-server algorithms, see these detailed lecture notes (lectures 5-9) by Yair Bartal. - 11/07 W [PDF] Work function (contd.), Online learning: regret minimization & the weighted majority algorithm.
- 11/12 M [PDF] Mistake bound model, winnow & perceptron algorithms.
- 11/14 W [PDF] MB model contd., PAC model. (K&V §1, §2)
- 11/16 F [PDF] PAC model, Occam's razor. (K&V §1, §2)
- 11/19 M [PDF] Boosting in the PAC framework. (K&V §4)
- 11/26 M [PDF] Random Walks & Markov chains. Cover time, hitting time. (M&R §6)
- 12/07 F [PDF] Random Walks & Markov chains: the resistance method, and mixing time. (M&R §6)
- 12/10 M Markov chains wrap-up and Q-A session.
No lecture notes are available for this last lecture, however, these notes contain all of what we covered, and extra.
Course Description Algorithm design and analysis is a fundamental and important part of computer science. This course introduces students to advanced techniques for the design and analysis of algorithms, and explores a variety of applications. Download all lectures notes in a single PDF file here.
- 09/05 W [PDF] Intro, greedy algorithms: scheduling, MST. (K&T §4, §5)
- 09/07 F [PDF] Set cover, Divide & Conquer, Dynamic programming. (K&T §5, §6, §11.3)
- 09/10 M [PDF] Dynamic programming. (K&T §6)
- 09/12 W [PDF] Network flow. (K&T §7)
- 09/14 F [PDF] Network flow applications, matchings. (K&T §7)
- 09/17 M [PDF] Randomized algorithms, Karger's min-cut algorithm. (K&T §13)
Here are some lecture notes by Avrim Blum on how to speed-up Karger's algorithm. - 09/19 W [PDF] Randomized load balancing and hashing. (K&T §13.10, §13.6, M&R §8.4, §8.5)
- 09/21 F [PDF] Bloom filters, NP-completeness. (M&R §8.4, §8.5, M&U §5.5)
See also, this survey on the applications of bloom filters by Broder & Mitzenmacher. - 10/01 M [PDF] NP-completeness contd., Approximation algorithms. (K&T §8, Vaz. §1)
- 10/03 W [PDF] Approximation via local search.
- 10/08 M [PDF] Linear programming, LP rounding. (Vaz. §14)
- 10/10 W [PDF] Randomized rounding, concentration bounds. (M&R §3.2, §4.1, §4.2)
- 10/15 M [PDF] Randomized rounding (contd.), LP duality. (M&R §4.2, Vaz. §12)
- 10/17 W [PDF] LP duality, Primal-dual algorithms. (Vaz. §12, 15)
- 10/19 F [PDF] Primal-dual algorithms. (Vaz. §15)
- 10/22 M [PDF] Semi-definite Programming. (Vaz. §26)
- 10/24 W [PDF] SDP (contd.), Streaming algorithms.
- 10/26 F [PDF] Streaming algorithms (contd.).
See this survey by Muthu Muthukrishnan for some motivation behind, and math used in, streaming algorithms. - 10/29 M [PDF] Online algorithms & competitive analysis. (B&E-Y §1)
Here is a nice presentation by Pat Riley & Elly Winner about different approaches to evaluating online algorithms. - 10/31 W [PDF] Caching/Paging, k-server problem. (B&E-Y §3, §4, §10)
- 11/05 M [PDF] Caching lower bound based on Yao's principle, Work function algorithm. (B&E-Y §8.4, §10, §12)
For a complete analysis of the work function and other k-server algorithms, see these detailed lecture notes (lectures 5-9) by Yair Bartal. - 11/07 W [PDF] Work function (contd.), Online learning: regret minimization & the weighted majority algorithm.
- 11/12 M [PDF] Mistake bound model, winnow & perceptron algorithms.
- 11/14 W [PDF] MB model contd., PAC model. (K&V §1, §2)
- 11/16 F [PDF] PAC model, Occam's razor. (K&V §1, §2)
- 11/19 M [PDF] Boosting in the PAC framework. (K&V §4)
- 11/26 M [PDF] Random Walks & Markov chains. Cover time, hitting time. (M&R §6)
- 12/07 F [PDF] Random Walks & Markov chains: the resistance method, and mixing time. (M&R §6)
- 12/10 M Markov chains wrap-up and Q-A session.
No lecture notes are available for this last lecture, however, these notes contain all of what we covered, and extra.
ADVANCE COMPILER Instructor: Sorin Lerner
Overview
In this course, we will explore the basic techniques that are the cornerstone of a variety of program analysis tools, including optimizing compilers, just-in-time compilers, program verifiers, bug fingers, code refactoring tools, garbage collectors, and runtime monitoring systems. These techniques may come in handy, no matter what field of CS you end up working in.At the same time, you will get a feeling for what research is like in the area of program analysis and compilers by reading research papers, and getting your feet wet in a small research project. If you haven't picked an area of research to work in, being exposed to some research will help you make a better decision. If you have already picked an area of research to work in, seeing what research is like in other fields of CS will broaden your perspective.
Schedule (ever evolving)
Using MS PowerPoint to view the slides will give you the best experience. If you don't have MS PowerPoint, Open Office works too, except that in some versions of Open Office, the digital ink doesn't display correctly. You can also use Acrobat Reader to view the slides in pdf format. The pdf files display the ink properly, but they are some artifacts here and there, mostly related to animations.| Week 0 | Th 09/24 | |
| Week 1 | Tu 09/29 | |
| Th 10/01 | ||
| Week 2 | Tu 10/7 | |
| Th 10/9 | ||
| Week 3 | Tu 10/13 | |
| Th 10/15 | ||
| Week 4 | Tu 10/20 |
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| Th 10/22 | ||
| Week 5 | Tu 10/27 | |
| Th 10/29 | ||
| Week 6 | Tu 11/03 |
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| Th 11/05 |
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| Week 7 | Tu 11/10 | |
| Th 11/12 | ||
| Week 8 | Tu 11/17 | |
| Th 11/19 |
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| Week 9 | Tu 11/24 | |
| Th 11/26 |
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| Week 10 | Tu 12/01 | |
| Th 12/03 |
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Accounting Information Systems Ninth Edition
Marshall B. RomneyPaul John Steinbart
| Study Guide |
- Chapter 1: Accounting Information Systems: An . . .
- Chapter 2: Overview of Business Processes
- Chapter 3: Introduction to E-Business
- Chapter 4: Relational Databases
- Chapter 5: Data Modeling and Database Design
- Chapter 6: Systems Development and Documentation . . .
- Chapter 7: Control and Accounting Information . . .
- Chapter 8: Computer-Based Information Systems . . .
- Chapter 9: Computer Fraud and Security
- Chapter 10: Auditing of Computer-Based Information . . .
- Chapter 11: The Revenue Cycle: Sales and Cash . . .
- Chapter 12: The Expenditure Cycle: Purchasing and . . .
- Chapter 13: The Production Cycle
- Chapter 14: The Human Resources Management and . . .
- Chapter 15: General Ledger and Reporting System
- Chapter 16: Introduction to Systems Development and
- Chapter 17: AIS Development Strategies
- Chapter 18: Systems Design, Implementation, and . . .
| PowerPoint Slides |
To view these files you will need Microsoft® PowerPoint® or the Microsoft® PowerPoint® Viewer.
| Chapter 1 | Chapter 10 |
| Chapter 2 | Chapter 11 |
| Chapter 3 | Chapter 12 |
| Chapter 4 | Chapter 13 |
| Chapter 5 | Chapter 14 |
| Chapter 6 | Chapter 15 |
| Chapter 7 | Chapter 16 |
| Chapter 8 | Chapter 17 |
| Chapter 9 | Chapter 18 |
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