Computer Architecture PPT

Lecture Notes Download here

• Lecture 1 (Introduction: Architecture Performance) (slides in ppt)
• Lecture 2 (MIPS Instruction Set and Pipelining) (slides in ppt)
• Lecture 3 (Pipeline Hazards) (slides in ppt)
• Lecture 4 (Hazards and Compiler Techniques) (slides in ppt)
• Lecture 5 (Branch Prediction and Scoreboarding) (slides in ppt)
• Lecture 6 (Score Boarding and Tomasulo) (slides in ppt)
• Lecture 7 (Tomasulo Algorithm) (slides in ppt)
• Lecture 8 (Hardware Speculation) (slides in ppt)
• Lecture 9 (Limits on ILP and Multithreading) (slides in ppt)
• Lecture 10 (Multithreaded and Multicore) (slides in ppt)
• Lecture 11 (Multicore and Network Processors) (slides in ppt)
• Lecture 12 (Cache Architectures) (slides in ppt)
• Lecture 13 (Cache Architectures Contd) (slides in ppt)
• Lecture 14 (Cache and Memory Architectures) (slides in pptx)
• Lecture 13 (Slides in ppt)
• Lecture 14 (Slides in ppt)
• Lecture 15 (Slides in ppt)
• Lecture 16 (Slides in ppt)
• Lecture 17 (Slides in ppt)
• Lecture 18 (Slides in ppt)

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:

• An Overview Of Computational Science (Douglas)
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• Scientific Visualization in High Performance Computing (Payne/Hsiao)
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• Networks (Snyder/Petty)
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• Computer Architecture (Petty/Snyder)
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• Cache Designs and Tricks (Douglas)
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• Tutorials for Network and Visualization Tools (Hsiao/Payne)
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• Tutorials for MPI (Satish/Douglas)
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  and OpenMP (Satish/Douglas)
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• Numerical Linear Algebra (Walters/Douglas)
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• Mathematical Optimization (Hsiao/Walters)
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• Ordinary Differential Equations (Carr/Douglas)
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• Some High Performance Computing Issues in PDEs (Douglas/Walters)
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• Random Number Generators (Snyder/Satish)
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• Monte Carlo Methods (Payne/Petty)
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• Case Studies
  • Ocean Models (Snyder)
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  • Chaos from Nonlinear Mappings (Carr)
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  • Mutational Meltdown of Endangered Species (Walters)
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  • Seismic Wave Propagation and Inversion (Petty)
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  • Direct and Inverse Bioelectric Fields Problems (Payne)
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  • Monte Carlo Surface to Surface Particle Transport (Satish/Hsiao) 4/27

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:

• Lecture 1 and 2: [TeX][PDF]
• Lectures 3 and part of 4: [TeX][PDF]
• Lectures 4(rest of it) and 5:[TeX][PDF]
• Lecture 6: [TeX][PDF]
• Lecture 7: [TeX][PDF]

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]

Compiler Design

Compiler Design

Course Information

Instructor:	Rich Maclin
Text:	Aho, Lam, Sethi, and Ullman, Compilers: Principles, Techniques, and Tools, 2nd Ed., Addison-Wesley, ISBN 0-321-48681-1

Click here to download the files:-

Useful software:

• gdb
• flex
• bison
• JavaCC
• NetBeans
• Eclipse

Class Materials (previous offerings)

• Introduction
  • Parts of a Compiler, Types of Programming Languages
• Lexical Analysis
  • Introduction, Finite State Machines
  • Regular Expressions, NFSMs, DFSMs, Translation
  • Scanner Implementation Sample code (gziped tar file)
• Parsing
  • Introduction, Context Free Grammars, Languages, Derivations
  • Ambiguity, Precedence, Associativity
  • Top Down Parsing, Recursive Descent, Predictive Parsing, LL(1), First and Follow sets
  • Bottom Up Parsing, Shift and Reduce, Conflicts
  • SLR Parsing, SLR States (PDF, 6 Per Page)
  • Constructing SLR States, LR(1) Parsing, LALR Parsing
• Semantic Analysis
  • Syntax-Directed Translation, Parse Trees, Abstract Syntax Trees
  • Implementation Example with Bison, LL(1) Grammar Code for a simple bison and corresponding LL(1) parser
  • Scoping
  • Parameter Passing
  • Symbol Table Implementation
  • Type Checking
• Interpreters
• Code Generation
  • Memory Management
  • Code Generation I (note that the class presentation of the remaining topics is different from these notes)
  • Code Generation II
  • Local Optimization

Project Parts:

1. Project Part 1: A String Table
2. Project Part 2: Two Scanners
3. Project Part 3: The Parser
4. Project Part 4: Symbol Table, Type Checking and Interpreter

Homework Assignments:

1. Homework 1
2. Homework 2
3. Homework 3
4. Homework 4
5. Homework 5

Sample Exam Questions:

• Sample Questions for Midterm 1
• Sample Questions for Midterm 2
• Sample Questions for the Final