Operations Management

Operations Management

Course description: This operations management course is intended to be a survey of the operating practices and procedures found in both manufacturing and service delivery firms. We will focus our attention on those business processes and procedures used to transform inputs into finished goods and services.
Operations management focuses on the systematic planning, design, and operation of all processes required for the production of goods and the delivery of services. Thus, operations management spans almost all the real value-added activities of an organization including product and process design, customer order management, production, and service delivery. Operations management also includes many supporting value-added activities such as purchasing, material requirements planning, inventory management, project management, and process improvement. These and related topics will be covered.


Lectures by chapter or topic

 Group Dynamics

 Chapter 1 Introduction to Operations Management

 Chapter 2 Quality Management

 Chapter 3 Statistical Quality Control

 Chapter 4 Product Design

 Chapter 5 Service Design

 Chapter 6 Processes and Technology

 Chapter 7 Capacity and Facilities

 Chapter S7 Facility Location Models

 Chapter 8 Human Resources

 Chapter 9 Project Management

 Introduction to Project Management - Basics

 Chapter 10 Supply Chain Management

 Chapter 11 Global Supply Chain Procurement

 Chapter 12 Forecasting

 Chapter 13 Inventory Management

 Chapter S13 Simulation

 Chapter 14 Sales and Operational Planning

 Chapter 15 Resource Planning

 Chapter 17 Scheduling

Fault-tolerant Digital Systems

Course Synopsis

Our daily lives are becoming increasingly dependent on computer systems, from small, embedded computers to large-scale data centers. Any disruption in or malfunctioning of these systems can lead to devastating consequences for society as a whole. The reliability and availability of these systems is thus essential for our quality of life and for the smooth functioning of society. Therefore, it is important to build computer systems that operate correctly in the face of errors and failures.

This course focuses on the design of fault-tolerant and reliable computer systems. In particular, we will attempt to understand the root causes of faults in computer systems and their impact. We will study both traditional and cutting-edge techniques to provide fault-tolerance and error resilience. Finally, we will explore the practical applications of the techniques in the context of real systems.

An important thread that runs through the course is the evaluation of fault-tolerant systems. To this end, we will study techniques ranging from analytical modeling to empirical validation. The assignments will give you hands-on exposure to cutting edge tools and techniques for dependability evaluation, and will prepare you for the final project. You are encouraged (but not required) to work on a project related to your research interests. The final project constitutes a significant part of the grade.

Topics Covered

This is a tentative list of topics to be covered in the course. Note that these topics are subject to change.

Some slides are based on Prof. Saurabh Bagchi’s slides for “Fault Tolerant Computer System Design” (ECE 695B) at Purdue University. Used with permission.

Topic	Lectures	Sub-topics
Introduction and Overview	3	Introduction to the course, Basic concepts, Sources of faults in computer systems
Modeling and Evaluation -1	2	Probability review and discrete probability, Continuous probability and TMR
Hardware fault-tolerance	2	Architectural techniques
Modeling and Evaluation -2	2	Markov processes, Stochastic Activity Networks
Software fault-tolerance	3	N-version programming, recovery blocks, robust data structures and process pairs
Modeling and Evaluation – 3	2	Fault-injection: techniques and tools, Formal methods
Parallel and Distributed systems	4	Check-pointing and recovery, Byzantine fault-tolerance and paxos
Case Studies	2	Stratus and AT&T systems

Textbooks

There is NO required textbook. However, the following books are recommended:

1. D. P. Siewiorek and R. S. Swarz, Reliable Computer Systems – Design and Evaluation, 3rd edition, 1999, A.K. Peters, Limited.
2. K. Trivedi, Probability and Statistics with Reliability, Queuing and Computer Science Applications, 2nd edition, 2001, John Wiley & Sons.

Data Structures and Algorithms

Data Structures and Algorithms

Course Contents

This is an introductory level course in Data structures and algorithms, offered by CSE dept, to students of other departments who have been permitted to register for a 'minor'. The course is meant for non-CSE, UG students. CSE/PG students should take one of the other algorithms courses being offered.The syllabus definition given for this course is available from the ASC website. It is copied here for information:
Introduction to data structures, abstract data types, analysis of algorithms. Creation and manipulation of data structures: arrays, lists, stacks, queues, trees, heaps, hash tables, balanced trees, tries, graphs. Algorithms for sorting and searching, order statistics, depth-first and breadth-first search, shortest paths and minimum spanning tree.
The course will not go into much excruciating details of various C++ implementation issues of data structures and algorithms; It will not be too top-level either. The course will focus on concepts that are 'broadly useful', not only in CSE but also other disciplines. Mostly we will study "What are some useful data structures and algorithms?”.
We will do in-class group discussions to design data structures for simpler versions of real-life applications amd programming problems. We will focus on "Why are the data structures and algorithms designed in a given way?". We will study pros-and-cons of various solutions to a given problem (Why is a particular data structure or algorithm "better" than some other?). We will also discuss "How to implement these data structures and algorithms?" in a systematic manner.

Lecture Schedule

The course is in Slot 5. There will be two lectures per week, each of 90 minutes duration.Most of the readings correspond to the sections in the following Textbooks:
[GTM]: Data Structures and Algorithms in C++ , by M. Goodrich, R. Tamassia and D. Mount, Seventh Edition.
[RS]: Algorithms in C++ , by R. Sedgewick, Third Edition.

No	Date	Topic	Reading/Slides	Remarks
-	05 Jan	No class	-	Registration still ongoing
-	07 Jan	No class	-	Institute: Techfest 2011
1	12 Jan	Introduction	-	Need for data structures, their relation with algorithms, ideas of correctnesss, complexity
2	14 Jan	C++ recap	[GTM Ch.1]	Complete this topic as self study
3	19 Jan	Arrays and Linked Lists	[RS Ch.3]	-
4	21 Jan	List processing	[RS Ch.3]	-
-	26 Jan	No class	-	Republic Day
5	28 Jan	String processing	[RS Ch.3]	-
6	02 Feb	Complexity analysis	[GTM Ch.3]	-
7	04 Feb	Quiz 1	-	10% weightage; One A4 sheet of handwritten notes allowed
8	09 Feb	Stack applications	[RS Ch.4]	Slides for Stack-Queue
9	11 Feb	Queue and Deque	[RS Ch.4]	Activity: Implementing a Queue using Stacks
-	16 Feb	No class	-	Institute Holiday
10	18 Feb	Tutorial	-	Problem-solving on topics covered so far
-	23 Feb	No class	-	Midsem week
11	25 Feb	Midsem: All topics so far	-	30% weightage; One A4 sheet of handwritten notes is allowed. Links to:Some Animations, and More animations.
12	02 Mar	Trees	[GTM Ch. 6]	-
13	04 Mar	Tree traversal	[GTM Ch. 6]	Homework 1 to be assigned
14	09 Mar	Quicksort and Mergesort	[RS Ch. 7, 8]	Elementary sorting [RS Ch. 6] - self study
15	11 Mar	Priority Queue	[RS Ch. 9]	Homework 1 is due on 14th March
16	16 Mar	Heapsort	[RS Ch. 9]	Slides for Priority Queues and Heapsort
17	18 Mar	Binary Search Trees	[GTM Ch. 9]	Homework 2 to be assigned
18	23 Mar	AVL Trees	[GTM Ch. 9]	-
19	25 Mar	Hashing	[RS Ch. 14]	Homework 2 is due on 28th March
20	30 Mar	Quiz 2: All topics after Midsem	-	10% weightage; One A4 sheet of handwritten notes is allowed.
21	01 Apr	Graphs: Data Structures	[GTM Ch. 12]	Homework 3 to be assigned
22	06 Apr	Graphs: Traversal	[GTM Ch. 12]	-
23	08 Apr	Shortest Path, MST	[GTM Ch. 12]	Homework 3 is due on 15th April
24	13 Apr	Tutorial	-	Problem-solving and Practice Test
25	15 Apr	Closure	-	More problem-solving; Last day of classes
26	28 Apr	End-sem: All topics	-	40% weightage; One A4 sheet of handwritten notes is allowed.

Speech and Natural Language Processing and the Web/Topics in Artificial Intelligence Programming

Speech and Natural Language Processing and the Web/Topics in Artificial Intelligence Programming

Course contents

• Sound : Biology of Speech Processing; Place and Manner of Articulation; Word Boundary Detection; Argmax based computations; HMM and Speech Recognition
• Words and Word Forms : Morphology fundamentals; Morphological Diversity of Indian Languages; Morphology Paradigms; Finite State Machine Based Morphology; Automatic Morphology Learning; Shallow Parsing; Named Entities; Maximum Entropy Models; Random Fields
• Structures : Theories of Parsing, Parsing Algorithms; Robust and Scalable Parsing on Noisy Text as in Web documents; Hybrid of Rule Based and Probabilistic Parsing; Scope Ambiguity and Attachment Ambiguity resolution
• Meaning : Lexical Knowledge Networks, Wordnet Theory; Indian Language Wordnets and Multilingual Dictionaries; Semantic Roles; Word Sense Disambiguation; WSD and Multilinguality; Metaphors; Coreferences
• Web 2.0 Applications : Sentiment Analysis; Text Entailment; Robust and Scalable Machine Translation; Question Answering in Multilingual Setting; Cross Lingual Information Retrieval (CLIR)

Texts and References

1. Allen, James, Natural Language Understanding, Second Edition, Benjamin/Cumming, 1995.
2. Charniack, Eugene, Statistical Language Learning, MIT Press, 1993.
3. Jurafsky, Dan and Martin, James, Speech and Language Processing, Second Edition, Prentice Hall, 2008.
4. Manning, Christopher and Heinrich, Schutze, Foundations of Statistical Natural Language Processing, MIT Press, 1999.
5. Radford, Andrew et. al., Linguistics, An Introduction, Cambridge University Press, 1999.

• Journals : Computational Linguistics, Natural Language Engineering, Machine Learning, Machine Translation, Artificial Intelligence
• Conferences : Annual Meeting of the Association of Computational Linguistics (ACL), Computational Linguistics (COLING), European ACL (EACL), Empirical Methods in NLP (EMNLP), Annual Meeting of the Special Interest Group in Information Retrieval (SIGIR), Human Language Technology (HLT).

Lecture Slides

• 23/7/09 Lecture 1 : Introduction
• 27/7/09 Lecture 2 : Machine Learning and NLP
• 28/7/09 Lecture 3 : ArgMax Computation
• 30/7/09 Lecture 4 : WSD : WordNet
• 10/8/09 Lecture 5 : Wordnet; Application in Query Expansion
• 11/8/09 Lecture 6 : Wiktionary; semantic relatedness; how toread research papers
• 19/8/09 Lecture 7 : Measures of WordNet Similarity
• 19/8/09 Lecture 8 : Quiz 1
• 25/8/09 Lecture 9 : Resnick's Measure of WordNet Similarity
• 27/8/09 Lecture 10 : Parsing Algorithms
• 31/8/09 Lecture 11 : Evidence for Deeper Structure; Top Down Parsing Algorithms
• 31/8/09 Lecture 12 : Noun Structure; Top Down Parsing Algorithms- contd.
• 01/9/09 Lecture 13 : Non-noun Structure Parsing Algorithms
• 03/9/09 Lecture 14 : Probabilistic parsing; sequence labeling, PCFG [ pdf ]
• 07/9/09 Lecture 15 : Probabilistic parsing; PCFG (contd.) [ pdf ]
• 08/9/09 Lecture 16 : Probabilistic parsing; computing probability of a sentence [pdf ]
• 10/9/09 Lecture 17 : PCFG; Arguments and Adjuncts [ pdf ]
• 22/9/09 Lecture 18 : Probabilistic parsing; inside-outside probabilities [ pdf ]
• 24/9/09 Lecture 19 : Speech : Phonetics
• 24/9/09 Lecture 20 : HMM - Combined Lecture with CS 621
• 29/9/09 Lecture 21 : Morphology - Guest Lecture by Mughdha Bapat [ pdf ]
• 30/9/09 Lecture 22 : Graphical Models for Sequence Labelling in NLP. - Guest Lecture by Anup Kulkarni
• 01/10/09 Lecture 22 : Graphical Models for Sequence Labelling in NLP (contd.).- Guest Lecture by Anup Kulkarni
• 05/10/09 Lecture 23 : Phonetics (contd.).
• 06/10/09 Lecture 24 : Consonants (place and manner of articulation) and Vowels.
• 08/10/09 Lecture 25 : Vowels (contd.) and a “grand” assignment.
• 09/10/09 Lecture 26 : Forward Backward probability; Viterbi Algorithm - Combined Lecture with CS 621
• 15/10/09 Lecture 27 : Phonology.
• 20/10/09 Lecture 28a : Sentiment Analysis and Opinions on the Web. - Guest Lecture by Aaditya Joshi.
• 20/10/09 Lecture 28b : Machine Translation and MT Tools - GIZA++ and Moses. - Guest Lecture by Nirdesh Chauhan [pdf ]
• 22/10/09 Lecture 29 : Text Entailment. - Guest Lecture by Prasad Pradip Joshi
• 26/10/09 Lecture 30 : POS Tagging.
• 27/10/09 Lecture 31 : Phonology; ASR, Speech Synthesis.
• 29/10/09 Lecture 32-33 : HMM and Viterbi.
• 02/11/09 Lecture 34 : Precision, Recall, F-score, Map.
• 03/11/09 Lecture 35 : Semantic Relations; UNL; Towards Dependency Parsing.
• 05/11/09 Lecture 36 : UNL.
• 11/11/09 Lecture 37 : Semantic Role Extraction.
• 11/11/09 Lecture 38-39 : Baum Welch Algorithm; HMM training.

Lecture slides can also be found here

Natural Language Processing

Course description:

This course will cover traditional material, as well as recent advances in the theory and practice of natural language processing (NLP) - the creation of computer programs that can understand, generate, and learn natural language. The course will introduce both knowledge-based and statistical approaches to NLP, illustrate the use of NLP techniques and tools in a variety of application areas, and provide insight into many open research problems.

class notes

Lecture	Reading material
Course overview [ppt]	-
Short Perl tutorial (I) [ppt]	One of the tutorials below [see the "Links" section]
Short Perl tutorial (II) [ppt]	One of the tutorials below [see the "Links" section]
Linguistics Essentials [ppt]  Guest lecture by Ben Leong	Chap.3 [Manning & Schutze] or any book on English grammar
Language Models [ppt]	Chap.4 [Jurafsky & Martin]
Language Models [ppt]	Chap.4 [Jurafsky & Martin]
Language Models [ppt]	Chap.4 [Jurafsky & Martin]
Collocations [ppt]	Chap.5 [Manning & Schutze] [a prefinal version of this chapter is available online]
Word classes and part of speech tagging ppt]	Chap.5 [Jurafsky & Martin]
Word classes and part of speech tagging ppt]	Chap.5 [Jurafsky & Martin]
HMM Tagging. Viterbi Algorithm. [ppt]	Chap.6 [Jurafsky & Martin]
Context Free Grammars [ppt]	Chap.12-13 [Jurafsky & Martin]
Parsing with Context Free Grammars [ppt]	Chap.12-13 [Jurafsky & Martin]
Probabilistic Parsing [ppt]  Guest lecture by Michael Mohler.	Chap.14 [Jurafsky & Martin]
Presentation and Workshop: "New Google Tools for Research" Mano Marks, Google  Willis 136, first floor of Library 11:30am-2pm	-
Exam preparation.	All the material studied so far.
Exam I.	All the material studied so far.
Word Sense Disambiguation [ppt]	Chap. 19, 20 [Jurafsky & Martin]
Word Sense Disambiguation [ppt]	Chap.19, 20 [Jurafsky & Martin]
Word Sense Disambiguation [ppt]	Chap.19, 20 [Jurafsky & Martin]
No class.	-
Word Sense Disambiguation	-
Special topics: Subjectivity and sentiment analysis [ppt]	-
Special topics: Subjectivity and sentiment analysis [ppt]	-
Special topics: Text semantic similarity [ppt]	-
Special topics: Text classification [ppt]	-
Thanksgiving. No class.	-
Wrap-up. Project discussions. Exam II preparation.	-
Exam II.	-
Project presentations.	-
Project presentations.	-