Computer Architecture

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  1. Lecture Notes # 1-2: ppt
  2. Lecture Notes # 3: ppt
  3. Lecture Notes # 4: pdf
  4. Lecture Notes # 5: ppt
  5. Lecture Notes # 6: ppt
  6. Lecture Notes # 6b: ppt
  7. Lecture Notes # 7: pdf
  8. Lecture Notes # 8 (updated 09/13/01): pdf
  9. Lecture Notes # 9: Mythsim Screenshots
  10. Lecture Notes # 10: pdf
  11. Lecture Notes # 11: ppt
  12. Lecture Notes # 12: ppt
  13. Lecture Notes # 13: pdf
  14. Lecture Notes # 14: pdf
  15. Lecture Notes # 15: pdf
  16. Lecture Notes # 16: pdf
  17. Lecture Notes # 14b : pdf

Computer Architecture PPT

Main Text: Patterson and Hennessy, Computer Organization and Design, Morgan

Kaufman Publisher
Reference: Hennessy and Patterson, Computer Architecture: A Quantitative Approach,
Morgan Kaufman Publisher

Course Syllabus:
  •  Advanced processor design: CPU pipelining, Datapath and Control Design, Data
     and Control Hazards: The topics will be covered from Chapter 6 of the text
  •  Instruction level parallelism, Dynamic scheduling of instructions, Branch
     Prediction and Speculation – From reference book and papers
  •  VLIW, Multithreading, and Network processor architectures – From papers
  •  Basic multiprocessor design: Shared memory and message passing; Network
     topologies. The topic will be covered from Chapter 9 of the text.

Download Here Lectures

Introduction to Computer Architecture Pdf Notes


Introduction to Computer Architecture
taught by Dr. Ken Williams
This course teaches techniques for design and optimization of combinatorial logic circuits, flip-flops, counter, registers and arithmetic concepts necessary to understand computer logic. Additional topics include assembly language programming, interrupt handling, and data representation.

Text Books :
The spring 2009 textbook for COMP370 Introduction to Computer Architecture is

Fundamentals of Computer Organization and Design, by Sivarama P. Dandamudi, Springer publishing, 2003, ISBN: 038795211X

Sylabus :-

Slides
Number bases

Integer representation

Floating Point representation

Character representation

Graphic representation

Logic gates

Boolean logic

Logic Simplification

Arithmetic

Transistors and VLSI

Combinatorial Logic

Logic Arrays

Finite State Automata

FSA Design

Assembler introduction

Assembler statements and ifs

Assembler pointers and arrays

Assembler function calls

Additional assembler information
FSA example: at least three 1’s

FSA example: two or more consecutive zeroes

FSA example: two consecutive zeroes but not three
Powers of 2

Computer Architecture and Organization

taught by Dr. Ken Williams
This course explores the design of computer systems and their architectures. Topics include central processing unit architecture, microcode, system interconnections, memory systems, input/output systems, interrupt handling, peripherals and communications networks

Textbook

The textbook for COMP375 Computer Architecture and Organization during the Fall 2008 semester is:
Computer Organization and Architecture: Designing for Performance, Seventh Edition,
by William Stallings, Pearson Prentice Hall, 2006, ISBN: 0131856448
Slides

History of Computers Additional information

Assembler Language Additional information

More assembler

Still more assembler

Assembler function calls

Machine language

Memory Access modes

Digital logic

VLSI transistors

Arithmetic

Interrupts

Buses

Memory Technology

Memory Organization

Cache Systems

Cache Mapping

Virtual Memory

Disk operation

RAID disks

I/O controllers

Backing up your data

Microcode 1

Microcode 2

Microcode 3

Microcode 4

Pipelining

Superscalar processors

RISC

Parallel Architectures

SMP Architectures

Benchmarking

Security
Combinatorial Logic

Sequential Logic

Number Representation




Computer Architecture PPT

Lecture Notes Download here

Computational Sciences and Engineering ppt

Course Description

Study of computer science techniques and tools that support computational sciences and engineering. Emphasis will be on visualization, performance evaluation, parallel computing, and distributed computing. Prerequisites: CS-115, CS/EE-380, and engineering standing.
(Picture courtesy of CSEP)

Requirements and Goals

Students need a knowledge of programming in a modern object oriented language and a basic knowledge of machine organization and architecture.

Students will learn about hardware and software support for high performance computing. They will learn to select algorithms and develop code for computing in a parallel (or distributed computing) environment. They will learn about benchmarking, optimization, and visualization. The course will include a hands on component utilizing a parallel computing environment.

Textbook and Course Outline

The course will follow, where still appropriate, the lecture notes of the Computational Science Educational Project (CSEP). The lectures will cover the following topics:

Computational Number Theory

Lecture 5 and 6:Chinese Remaindering[PDF][TeX]
Lecture 7:Towards Factorization over Finite Fields[PDF][TeX]
Lecture 8:More on Finite Fields[PDF][TeX]
Lecture 9:Uniqueness of F_q[PDF][TeX]
Lecture 10:Distinct Degree Factorization[PDF][TeX]
Lecture 11:Cantor-Zassenhaus Algorithm[PDF][TeX]
Lecture 12:Berlekamp's Algorithm[PDF][TeX]
(midsem 1 syllabus ends here)
Lecture 13:Codes: An Introduction[PDF][TeX]
Lecture 14:BCH Codes[PDF][TeX]


Midsem 1 (and solutions)[PDF]
Lecture 15 and 16:BCH Codes: Error correction[PDF][TeX]
Lecture 17:Primality is in NP and coNP[PDF][TeX]
Lecture 18:Quadratic Reciprocity[PDF][TeX]
Lecture 19:Quadratic Reciprocity (contd.)[PDF][TeX]
Lecture 20 and 21:Solovay-Strassen Primality Testing[PDF][TeX]
Lecture 22:A Discussion on ERH and Toward AKS[PDF][TeX]
Lecture 23 and 24:The Cyclotomic Polynomial[PDF][TeX]
Lecture 25:The AKS Primality Test[PDF][TeX]
Lecture 26:Hensel Lifting[PDF][TeX]
Lecture 27:Bivariate Factorization[PDF][TeX]

Computational Linguistics PPT PDF SLIDES

Instructor Ani Nenkova 

Course description:

This is an introductory course to computational linguistics, centered on the fundamental questions of how a machine can learn to analyze, understand and produce language. The topics covered include speech synthesis and recognition, syntactic parsing, semantic interpretation, discourse and pragmatic inference, and sentiment analysis. Students will get familiar with standard practical tools and resources for automatic linguistic analysis.
Prerequisites:
Undergraduate students should have completed CIS 121 before enrolling.
Textbooks:

  • [REQUIRED] Steven Bird, Ewan Klein, and Edward Loper, Natural Language Processing with Python --- Analyzing Text with the Natural Language Toolkit, O'Reilly Media, 2009. (Free Online)

  • [OPTIONAL] Daniel Jurafsky and James H. Martin Speech and language processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition, 2nd edition, Pearson Prentice Hall, 2008. (Available on Amazon)

  • [OPTIONAL] Chris Manning and Hinrich Shutze, Foundations of Statistical Natural Language Processing, MIT Press, 1999. (Free Online)
 Download slides here

  • Sep 7, Lecture 1: Welcome to CIS 430/530 (Slides: [PPTX] [PDF])
  • Sep 12, Lecture 2: From Frequency to Meaning: Vector Space Models of Semantics (Slides: [PPTX] [PDF]) [Related Reading]
  • Sep 14, Lecture 3: Introduction to Language Models (Slides: [PPT] [PDF])
  • Sep 19, Python/NLTK/matplotlib Tutorial, Part 1 (Slides: [PPT] [PDF])
  • Sep 21, Python/NLTK/matplotlib Tutorial, Part 2
  • Sep 26, Lecture 4: Language Models II (Slides: [PPT] [PDF])
  • Sep 28, Lecture 5: Morphology (Slides: [PPT] [PDF])
  • Oct 3, Lecture 6: Part of Speech Tagging (Slides: [PPT] [PDF])
  • Oct 5, Lecture 7: Hidden Markov Models (Slides: [PPT] [PDF])
  • Oct 12, Lecture 8: Dynamic programming algorithms (Slides: [PPT] [PDF]
  • Oct 17, Lecture 9: Formal Grammars (Slides: [PPT] [PDF])
  • Oct 19, Lecture 10: Parsing (Slides: [PPT] [PDF])
  • Oct 24, Lecture 11: Statistical parsing (Slides: [PPT] [PDF])
  • Oct 26, Lecture 12: Automatic summarization (Slides: [PPTX] [PDF])
  • Oct 31, Lecture 13: Content selection (Slides: [PPTX] [PDF])
  • Nov 2, Lecture 14: Evaluation (Slides: [PPTX] [PDF])
  • Nov 7, Lecture 15: Word sense disambiguation (Slides: [PPT] [PDF])
  • Nov 9, Lecture 16: Word similarity (Slides: [PPT] [PDF])
  • Nov 14, Lecture 17: Semantic Roles (Slides: [PPT] [PDF])
  • Nov 16, Lecture 18: Coreference resolution (Slides: [PPT] [PDF])
  • Nov 20, Lecture 19: Discourse coherence (Slides: [PPT] [PDF])
  • Nov 30, Lecture 20: Discourse coherence and readability (Slides: [PPT] [PDF])
  • Dec 5, Lecture 21, Guest lecture by Annie Louis: Twitter NLP (Slides: [PDF])

Computational Complexity

This course is taken by Manindra Agrawal. Half way down the course, he started on the PCP theorems and I thought it would be good to scribe the notes. I shall try to make it as detailed as possible.

Lecture 1:Derandomization: The Deathly Hallows[PDF][TeX]
Lecture 2:Expanders: Spectral and Vertex Expansion[PDF][TeX]
Lecture 3:Random Walks on Expanders[PDF][TeX]
Lecture 4:Constructing Expanders: The Zig-Zag Product[PDF][TeX]
Lecture 5:Towards the Proof of PCP Theorem[PDF][TeX]
Lecture 6:Gap Amplification[PDF][TeX]

Complexity Theory

Lectures on Pseudorandom Generators:
Lectures on Error Correcting Codes and Extractors: (haven't been TeX-ed yet)
Lecture notes on Expander Graphs: [TeX][PDF]
Lecture notes on the PCP Theorem:[TeX][PDF]
Lecture on Expander Codes: [TeX][PDF]
Lecture on Monotone Circuit Lower Bounds for Clique: [TeX][PDF]
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