Course Description for Spring 2015

Course Description for Spring 2015 Course Name Advances in Data Mining Advances in Robotics & Control Artificial Neural Networks Cognitive Neuroscience Computational Linguistics 2 Computer Aided Drug Design Computer Vision Database Systems Design for Testability Distributed Systems e-governance Electromagnetism & Optics Error Correcting Codes General & Structural Chemistry Indian Grammatical Tradition Information Retrieval and Extraction Information Security Audit and Assurance Internals of Application Servers Intro to Neural & Cognitive Modeling Intro to Robotics: Mechanics & Control Intro to Systems Biology Intro. to Biology Linear Algebra Linguistic Data 2: Collection & Modeling Medical Image Processing Modeling and Simulations NLP Applications Optical Communication & Networks Optimization Methods Principles of Information Security Readings from Hindi Literature Remote Sensing Science Lab II Select topics in Physical Chemistry Signal Detection and Estimation Theory Software Engineering Software Foundations Speech Systems System and Network Security Topics in Natural Language Semantics Faculty name Kamal Karlapalem Rambabu Kalla Yegnanarayana Kavitha Vemuri Radhika M+Soma Paul Deva Priyakumar Anoop + PJ Naryanan P.Krishna Reddy Shubhajit Roy Vivekanand Vellanki Harish P Iyer+RK Bagga Prabhakar B Prasad Krishnan Deva Priyakumar +Abdul Rehman Dipti M Sharma Vasudeva Varma Shatrunjay Rawat +Mulualem Teku Ramesh Loganathan S.Bapi Raju + Dipanjan Roy Suril V Shah V.Shridevi +Vinod Palakkad A.Rameshwar Rajat Tandon TBD Jayanthi Sivaswamy M.Krishnan +Abdul Rehman Manish Shrivastava Jayashree Ratnam Kannan Srinathan Ashok Kumar Das Harjinder Singh RC Prasad Tapan Kumar Sau +M.Krishnan Tapan Kumar Sau +Prabhakar Bhimalapuram Sachin Chaudhari Raghu Reddy Suresh Purini +Venkatesh Ch + K.Viswanath Anil Kumar V Sanjay Rawat Soma Paul

Topics in Speech Processing: Audio Topology Usability Engineering VLSI Architectures Suryakanth VG Geeta Priyanka Srivastava Rahul Shrestha : Advances in Data Mining Course Code : Note: Please use course code for previously existing course CREDITS : 3-0-0-4 TYPE-WHEN : CSE elective FACULTY NAME : Kamal Karlapalem PRE-REQUISITE : Data Warehousing and Data MIning OBJECTIVE : To learn about latest techniques and applications of data mining. COURSE TOPICS : 1. Understanding data and feature determination 2. Advanced techniques in Data Clustering and Outlier detection 3. Graph mining 4. Text Analytics 5. Time Series mining Han & Kamber Data MIning Zaki, Data Mining & survey papers and other research papers *PROJECT: A semester long term paper. GRADING: 10 quizzes or assignments 40% marks. Term paper 60% marks. Must get at least 40/60 in Term paper to pass the course. To get 40/60 it should be of a novel, full-fledged work that addresses a significant problem, with algorithms, implementation and results. The term paper must be submitted by Midterm 2 exams time frame. By the end of 10 th week of classes. OUTCOME: Exposure to advanced data mining techniques and training to do research to address a problem. : Advances in Robotics and Control Course Code : Note : Please use course code for previously existing course CREDITS : 4 TYPE-WHEN : Level-2 Elective, Spring FACULTY NAME : Rambabu Kalla PRE-REQUISITE: Linear Control Systems OBJECTIVE : With the advent of novel mechanisms in robotics such as quad copters, fixed wing aircrafts, Google car, humanoid robots and a renewed interest in manipulators due to emergence of personal robotics the need to model and control such robot mechanisms cannot be articulated better. This course strives to fill up the vacuum existing in the robotic curricula by addressing issues related to above. Simultaneously, the course objective is also to bring to the table advanced concepts in control systems and their application to the popular domain of robotics COURSE TOPICS: Actuators in Robotics: Different types of Actuators, Dynamic Model of DC Motor from First Principle, Transfer function of DC Motor, Speed Control of DC Motor, Position Control of DC Motor, Servos. State Space Analysis: State Space (SS) Models, Linear Transformation of SS Models, Eign Values and Eign Vectors, Computation of State Transition Matrix, Transfer Function and Transfer Matrix, Linearization of Non-linear System, State Controllability, State Observability, Canonical Form, Discrete Time State Space Model, Design of Control Systems in State Space. Review of Mobile Robot Kinematics: Kinematics of differential drives, Ackerman Steer and Omni Drives. Kinodynamic Planning, Non Holonomic is planning. Control of Non Holonomic Systems: Introduction to Non Holonomic (NH) Systems, Modeling Examples NH Systems, Control Properties of NH Systems, NH Motion Planning, Feedback Control of NH Systems, Asymptotic Tracking. Review of Kinematics and Dynamics of Manipulators: DH Parameter, Forward Kinematics

Trajectory Tracking of Manipulators: Feedback Linearization, Trajectory Generation, Tracking and Control. Quad-rotor Modelling and Control: Introduction to Quad-rotors (QR), First Principles Model of QR, Nonlinear Model Simulation of QR, Model Linearization, Control Strategies for QRs, Static Position Reference Controller, Dynamic Position and Velocity Reference Controllers, Linear State Feedback Control of QR, Optimal Control of QR. 1. Modern Control Theory, Katsuhiko Ogata, Pearson Education, 2002 2. Modern Control Theory, Richard C. Dorf and Robert H. Bishop, Pearson Education, 2011. 3. Robot Modeling and Control Mark W. Spong, Seth Hutchinson, and M. Vidyasagar, John Wiley & Sons, 2006. 4. Nonholonomic Mechanics and Control, A. M. Bloch, Springer Science, 2003. 5. Modeling and Control of Mini-Flying Machines, Pedro Castillo, Rogelio Lozano, Alejandro E. Dzul, Springer- Verlag, 2005. 6. Modeling and Control Simulation for Autonomous Quadrotor, Idris Eko Putro, Lambert Academic Pub., 2011 *PROJECT: GRADING: 2 Mid Semester Exams, 6 Assignments, and End Semester Exam OUTCOME: Students on successful completion of the course get acquainted with the control schemes applied to the field of Robotics. : Artificial Neural Networks CREDITS : 3-1-0-4 TYPE-WHEN : Elective to be offered in the next semester FACULTY NAME : B. Yegnanarayana PRE-REQUISITE: Meant only for 6th semester and above. Preferably PG and research scholars OBJECTIVE : The course is a first course on the subject, highlighting the basics of ANN COURSE TOPICS: Background to ANN Developments of computer technology and Artificial Intelligence, need for new models of computing, biological motivation, PDP models. Basics of ANN -- ANN terminology, models of neurons, topology, basic learning laws. Activation and synaptic dynamics Activation dynamics models, synaptic dynamics models, stability and convergence, and recall in neural networks. Feed forward neural networks Analysis of pattern association, pattern classification and pattern mapping networks, perception learning and convergence theorem, back propagation learning. Feedback neural networks Analysis of pattern storage networks (Hopfield model), Stochastic networks and simulated annealing, Boltzmann machine. Copetitive learning neural networks Components of CLNN, Analysis of pattern clustering networks, analysis of feature mapping networks. Applications of ANN Optimization, traveling salesman problem, speech and speaker recognition B.Yegnanarayana, Artificial Neural Networks, Prentice-Hall India, 1999 J.M.Zurada, Introduction to Artificial Neural Systems, 1992 Simon Haykin, Neural Networks A comprehensive foundation, Pearson and Prentice-Hall, 1999 *PROJECT: GRADING: Midsem exam 1: 20%, Midsem exam 2: 20%, Assignments: 10% Final Exam (oral or written): 50% OUTCOME: I hope they will get some idea of basics of ANN, and the need of ANN for solving pattern recognition problems Title: Cognitive Neuroscience Type When: Spring 2014 Faculty Name: Kavita Vemuri Joint course: IIITH and University of Hyderabad. The course will examine how modern cognitive neuroscientists explore the neural underpinnings of sensory information vision, sound, and touch leading to visual/auditory attention, language processing, memory, empathy/emotion and other higher-order cognitive processes. Investigates the different techniques applied to uncover observations of clinical populations & non-clinical human populations and also some specimens from the insect/ animal kingdom. Data collected from powerful methods like functional magnetic resonance imaging (fmri) and electroencephalogram (EEG) will be analyzed to examine functional brain connectivity. Equal emphasis is on understanding analytical methods and the limitations of each. The third part of the course will cover a part of

computational neurosciences, which involves building computer simulation on models of neurons and dynamic neural circuits Lectures: 70% Lab work: 30% The lab work will cover analysis of fmri, Difusion Tomography imaging, EEG data from research studies designed to investigate the neural responses to a visual, auditory or task stimuli. Textbooks: 1. Cognitive Neuroscience by Gazaniga (copy available in ITH library) 2. Fundamentals of Computational Neuroscience by Thomas Trapenberg. 3. Required research papers. Evaluation: Assignments (6):20% Class presentation (1): 10% Lab work: 30% Mid-sem I: 20% Final Sem: 20% *** : Computational Linguistics 2 CREDITS : 3-0-0-4 TYPE-WHEN : FACULTY NAME : Radhika M + Soma Paul PRE-REQUISITE: NLP-1 or CL-1 OBJECTIVE: To introduce the students to the basic concepts of structure of texts, meaning in text and contextual interpretation of text. COURSE TOPICS: SEMANTICS -Background for studying word meaning and sentence meaning, Sentence meaning and propositional content, Reference and Sense, Entailment, Contrariety, Contradiction, Transitivity, Symmetry, Reflexivity. -Word meaning and sentence meaning, content word and grammatical word, Contextual variation. -Semantic classes for categorizing words: Verb alternation, Accomplishment, Achievement, Activity, Noun alternation, Mass vs Count, Adjective alternation; -Lexical semantic relations - Synonymy, Antonymy, Hyponymy and lexical inheritance, Meronymy; Lexical ambiguity. -Formal representation of natural language. PRAGMATICS AND DISCOURSE: Pragmatics and Discourse analysis as a study of context dependent aspects of meaning context, text and relevance. Discourse analysis: Structure of text and coherence; exchange structure and conversational analysis; turn taking; deixis; anaphora; discourse connectives and relations. Pragmatics: Meaning beyond textual context; entailment and inference; conversational implicative, conventional implicative and presupposition; co operative interaction and Gricean maxims; speech act theory; language as action, performatives, direct and indirect speech acts and felicity conditions; Reference; SEMINARS: Students will be expected to read research papers on various topics and present in class. PROJECT: Students will do one term project which will include issues related to semantics, pragmatics and discourse. Alan Cruse (2004). Meaning in Language. John Lyons (1995). Linguistic Semantics. Cruse Alan (2004). Meaning in Language: An Introduction to Semantics and Pragmatics. Part 2 and Part 4. Levinson, Stephen C. (1983). Pragmatics. Brown, G and Yule, G. (1983). Discourse Analysis. Cutting Joan (2002). Pragmatics and Discourse: A resource book for students. GRADING: HA10, Seminar 10, Mid Sem 25, Project 20, End Sem 35 OUTCOME: Students will have a good understanding of semantic and contextual analysis of texts which will enable them in building text processing tools and systems. : Computer Aided Drug Design (CADD) CREDITS : 4

TYPE-WHEN : Elective FACULTY NAME : Dr. U. Deva Priyakumar PRE-REQUISITE: Molecular architecture or advanced biomolecular architecture or equivalent. Those who are interested in taking the course and do not have one of these prerequisites; please talk to the course instructor. OBJECTIVE: This course aims to introduce principal concepts in drug design, and specifically the role of computational models. Various methods that are used in computer aided drug design would be discussed, and the basic principles to understand these methods will also be taught. COURSE TOPICS: Course overview and Introduction (What to expect for the next 15 weeks) Introduction to few computational methods - Potential energy surface - Force Field parameters - Conformational analysis - Energy calculations using molecular mechanics - Free energy of binding - Basics of chemical bonding Drug design - Drug - Traditional drug design - Drug-receptor interactions - Biological activity and various measures - Importance of computational methods in drug design Databases - biomolecules & small molecules Ligand based drug design - Structure activity relationship - Quantitative structure activity relationship (2D & 3D methods) - QSAR parameters - QSAR validation - Pharmacophore based models Structure based drug design - Docking - Discussion of various common algorithms - Protein flexibility in docking - Scoring functions - De novo drug design Molecular similarity analysis ADME-T prediction Advanced topics in drug design (Student Seminars) Textbook of drug design & discovery (Ed. Krogsgaard-Larsen & Lilijefors and Madsen) 2001, CRC; Pharmacophore perception, development, and use in drug design (Ed. Gunter) 2000, IUL; Molecular modeling: principles and applications (Leach) 2001, Prentice Hall; Molecular modeling and simulations (Schlick) 2002, Springer. Material will be given from time to time *PROJECT: GRADING: Exams - 40% (10 + 10 + 20); Assignments - 40%; Lab Assignment - 10%; Seminars/Quiz - 10% OUTCOME: Understanding of the basic principles involved in computer aided drug design and, to be able to appreciate the utility of the techniques in pharmaceutical industry. : Computer Vision CREDITS : 3-1-0-4 TYPE-WHEN : FACULTY NAME : Dr.Anoop & Prof.P J Narayanan PRE-REQUISITE : Computer Graphics or Image processing OBJECTIVE : COURSE TOPICS : Relationship between computer vision, graphics and Image processing. Camera model: Imaging process 3D to 2D projection and loss of information, calibrated and un calibrated vision systems. Limitations of popular cameras and methods to overcome them. Multiple view geometry and imaging systems. Algebraic constraints, reconstruction, view synthesis. Recognition of objects from appearance, shape, partial view, occlusion, etc., Analysis of video, motion and recognizing dynamic activities.

Forsytn and Ponce Computer Vision: a modern approach, Pearsen Education Inc. : CSC 441- Database Systems CREDITS : 4 TYPE-WHEN : Second-level course in database systems FACULTY NAME : P. Krishna Reddy PRE-REQUISITE : Students should have knowledge of SQL, database design and operating systems, programming language, algorithms. OBJECTIVE : Databases have become essential part of every business. A database system can be used to manage large amounts of data in a persistent manner. The objective of this course is to study the methods that have been evolved over several decades to build database systems or database management systems software in a focused manner which include storage management, index management, query processing, recovery management and transaction management. COURSE TOPICS Introduction (3 hours); Data storage ( 3 hours); Representing data elements (3 hour s); Index structures (3 hours); Multidimensional indexes (6 hours); Query execution (6 hours); The query compiler (6 hours); Coping with system failures (3 hours); Concurrency control (6 hours); More about transaction management (6 hours). 1. Database System Implementation, Hector Garcia-Molina, Jeffrey D. Ullman and Jennifer Widon, Pearson Education, 2003 OTHER TEXT BOOKS: 2. Elmasri & Navathe, Fundamentals of Database Systems, Pearson Education, 5th Education. 3. Raghu Ramakrishnan and Johannes Gehrke, Database Management Systems, Third edition, Mc Graw Hill, 2003. 4. Abraham Silberschatz, Henry F.Korth, S.Sudarshan, Database system concepts, fifth edition, Mc Graw Hill, 2006. PROJECT: A practical project on indexing, query optimization, and transaction management will be given. The project will be evaluated. GRADING: PROJECT and Assignments: 30%; MIDSEM: 30%; ENDSEM: 40% OUTCOME: The course will help the students in understanding the fundamental concepts of several database management systems like ORACLE, DB2, SYBASE and so on. Also, the students will understand the solutions/options to interesting problems which have been encountered by the designers of preceding DBMSs. Most important, the students will be exposed to internal design of DBMSs and able to tune the DBMSs to meet the performance demands of diverse applications. *** : Design for Testability CREDITS : 3-1-0-4 TYPE-WHEN : Spring 2012 FACULTY NAME : Shubhajit Roy PRE-REQUISITE : A course on Digital Circuits (or) B.Tech OBJECTIVE : To expose the students to the various techniques adopted to make the testing (complicated) of manufactured ICs. To make the students to take care of the testing aspects into acount at the design stage itself. COURSE TOPICS : 1) Introduction: Testing of electronic gadgets, various types of tests, VLSI design flow, role of modeling and simulation in testing. 2) Faults and fault modeling, detection of faults, fault simulation and its applications, functional testing, exhaustive and non-exhaustive testing, automatic testing procedures. 3) Design for testabilty: Various features are to be incorporated for carying out testing from input & output pins, scan architecture, board level testing, signature analysis and testing. 4) Built in Self Test (BIST), BIST concepts, text patern generation, BIST architectures. 5) Testing of Analog and mixed signal ICs, testing of system on chip.

1) Miron Abramolici, Melin A Breur, Arthur D. Friedman, Digital systems, testing and testable design, Jaico publishing house, 2001 2) Stanley L. Hurst, VLSI Testing, Digital and Mixed Analog / Digital Techniques, Institution of Electrical Engineers, 1998, London, United Kingdom. 3) Michael L. Bushnel, Vishwani D. Agarwal, Esentials of Electronic Testing for Digital & Mixed Signal FLSI Circuits, Springer 2000 1. VLSI Test Principles and Architectures: Design for Testabilty,Laung-Terng Wang, Cheng-Wen Wu, Xiaoqing Wen 2. VLSI Testing, Stanley Leonard Hurst 3. Electronic Design Automation, Laung-Terng Wang, Yao-Wen Chang, Kwang-Ting (Tim) Cheng 4. System-on-Chip Test Architectures: Nanometer Design for Testabilty, Laung-Terng Wang, Charles 5. E. Stroud, Nur A. Touba 6. Testing of Digital Systems, Jha and Gupta *PROJECT: GRADING: 2 Mid Sem Exams 2 x 20 40 2 Surprise Tests 10 Final Examination 50 ----- Total Marks 100 ----- A > 80 B 70 79 C 60 69 D 50 59 E < 50 OUTCOME: ** : Distributed Systems CREDITS : 4 FACULTY NAME : Vivekanand Vellanki Foundations: Characterizations of Distributed Systems System Models Networking and Internetworking Inter-process Communication Logical Time: A framework for a system of logical clocks Scalar time, vector time and efficient implementation of vector clocks Synchronization of physical clocks. NTP Global state and snapshot recording algorithms: System model and definition Snapshot algorithms for FIFO channels Middleware: Distributed objects and RMI Termination Detection: Termination detection using distributed snapshots A spanning-tree-based termination detection algorithms Distributed mutual exclusion algorithms: Lamport s algorithm, Ricart-Agarwala Algoritm Sughal s dynamic information Structure Algorithm Quorum-based mutual exclusion Alogorithm Maekawa s Algorithm Deadlock detection in Distributed Systems: Models of deadlocks, Knapp s classification of distributed deadlock detection algorithms. Mitchell and Merrit s algorithm for single resource model Consensus and agreement algorithm: Problem definition. Agreement in a failure-free system (synchronous or as ynchronous). Agreement in (message - passing) synchronous system with failures. Agreement in asynchronous message passing systems with failures. Reference Books 1) Ajay D. Kshemkalyani and Mukesh Singhal, Distributed Computing Principles, Algorithms and System, Cambridge University Press 2008. 2) Sukumar Ghosh, Distributed Systems An Algorithmic Approach, Chapman & Hall ICRC, 2007. 3) M. L. Liu, Distributed Computing Principles and Applications, Pearson, 2004. 4) George Coulouris, Jean Dollimore, Tim Kindberg and Gordon Blair, Distributed Systems Concepts and Design, Fifth Edition, Pearson 2011.

5) Mukesh Singhal and Niranjan G. Shivaratri, Advanced Concepts in Operating Systems, TMH, 1994, 2010. *** : e-governance CREDITS : 3-1-0-4 TYPE-WHEN : Monsoon 2013 - Being offered for the second time for PG and 3 rd/4th UG Students. Max number of students to be restricted to 35. Course wil not be ofered, if students number is les than 10. FACULTY NAME : Dr R K BAGGA, Harish P Iyer PRE-REQUISITE : None at present OBJECTIVE : 1. Covers basic concepts of e-government with scope of applications of Information & Communication Technologies (ICT) in Government sector.2. The role of ICT as enabler for government proces reengineering (GPR) for citizen services wil be highlighted. 3. Propose to cover implementation isues of e-governance initiatives in India, Change management, transparency, coruptions, Cyber laws and e-security. COURSE TOPICS : MDULE I.ICT FOR DEVELOPMENT (ICT4D) I. Exposure to emerging trends in ICT for development. I. Understanding of design and implementation of e-government projects II. Systems Approach and MIS Framework in egoverment IV. National e-governance Plan(NeGP) for India V. Technical Paper(individual work) MODULE I.e-GOVERNMENT (in Learning By Doing Methodology) 1. Need for Government Proces Reengineering(GPR), 2. SMART Governments & Thumb Rules 3. Architecture and models of e-governance, including Public Private Partnership (PPP). 4. Focusing on Indian initiatives and their impact on citizens. 5. Need for Innovation and Change Management in egovernance MODULE II ISSUES & CASE STUDIES 1. Critical Suces Factors for egovernance 2. Major isue including coruption, innovations, e-security and Cyber laws 3. Sharing of case studies to highlight best practices in managing e-governance projects in Indian context 4. Mini Projects by students in Groups as team work 5. Presentations by Groups 1.e-Government - The Science of the Posible, J Satyanarayana, Prentice Hal, India (2004)/latest edition 2. Enablers of Change: Selected e-governance Initiatives in India Piyush Gupta,R K Bagga and Ayaluri Sridevi (2010) under Print ICFAI Pres 1. Implementing and managing egovernment, an International Text; by Richard Heeks, Vistaar Publications, India (2006). 2. E-Government: From vision to Implementation; by Subhash Bhatnagar; Sage Publications India Pvt Ltd. (2004). 3. State, IT and Development: by Kenneth Kenniston, RK Bagga and Rohit Raj Mathur, Sage Publications India Pvt Ltd. (2005). 4. e-governance Case Studies; Ashok Agarwal, University Pres India, (2006) 5. Compendium of e-governance Initiatives in India; Piyush Gupta, R K Bagga, University Pres India, (2007). 6. Transforming Government: RK Bagga and Piyush Gupta (2008) 7. Fostering e-governance: Piyush Gupta, R K Bagga and A Sridevi (2009) TUTORIALS: Field visits to some typical e-governance applications in and around Hyderabad e e- Seva/e-Sagu/EMRI/ Aarogyasri/INCOIS wil be aranged. LABORATORY: Yes; Module I wil be using Internet and involve Lab work in ISWOP Cel. It is planned to folow Learning By Doing Methodology for Module I

* MINI PROJECT: Participants on this course would be aloted some e-governance projects analysis work and would be asked to write a paper and make formal presentations. GRADING: QUIZZES : 10% TECH PAPER/HA : 10% MIDTERM EXAMS : 30% (15% EACH) MINI PROJECT : 20% FINAL EXAM : 30% OUTCOME: After taking this course, the student wil be exposed to the major national initiatives in National e- Governance Plan (NeGP) for India.They wil be able to analyze the essential parameters/critical suces factor s of e- Government projects. They wil also be able to carry out independent study and undertake critical analysis and evaluation of ongoing E-GOV projects. ** : Electromagnetism and Optics Course Code :??? CREDITS : 4 (3-1-0-0) TYPE-WHEN : Spring 2014 FACULTY NAME : Prabhakar Bhimalapuram PRE-REQUISITE : None OBJECTIVE : Understand Maxwell Equations as the central unifying ``principle'' of electromagnetic phenomena. And provide introduction to modern optics. COURSE TOPICS : One half of the course (~12 lectures and about 5 tutorials) will directly deal with Electromagnetism, starting with Maxwell Equations. The second half (~12 lectures and 5 tutorials) will deal with optics; after a short overview of geometric optics. Following are the topics in their rough chronological order: A-1. Maxwell Laws of electromagnetism; Electrostatics and Magnetostatics; scalar and vector potentials A-2. Poisson & Laplace equations; named laws in electromagnetism. A-3. Dielectrics and their polarisation; use in capacitors. A-4. Electromagnetic radiation: Light A-5. Plane wave solutions to Maxwell Equations in vacuum; A-6. Light polarization. Other effects on radiation propagating in medium. ---- B-1. Various physical properties of light B-2. Phenomena of diffraction: models B-3. Scalar theory of light: Helmholtz equation. Kirchoff formulation. Rayliegh-Somerfield formulation. Huygens- Fresnel Principle. B-4. Holography. B-5. Introduction to non-imaging optics, statistical optics. B-6. Theory of lasers B-6. Overview of working of a few optical instruments (telescope, microscope, camera, Atomic Force Microscopes, compound lenses etc) 1) Feynman Lecture notes on Physics, Volume II. (for electromagnetism) 2) Feyman Lecture notes on Physics, Volume I (for optics) 3) Optics, by Ajoy Ghatak. (for Wave optics) *PROJECT: GRADING: Every week will have one homework, but need not be submitted. Every week, the tutorial will start with a quiz (30 min), so approximately 10 quizzes will be conducted; of these ~10 quizzes, marks from top 8 quizzes will be considered for grading purposes. 1) Quizzes: ~ 10 quizzes of which top 8 will be considered : 30% 2) In class work: small problems, participation : 10% 3) Exams: Midsem-1 and Midsem-2, 15% each : 30%

4) Endsem exam : 30% OUTCOME: The student after taking this course will be able to appreciate and use Maxwell Equations as the central tool for estimating and computing various optical phenomena. ** : ERROR CORRECTING CODES CREDITS : 4 credits TYPE-WHEN : Monsoon 2011 FACULTY NAME : Prasad Krishnan PRE-REQUISITE : Basic course on Communication theory OBJECTIVE : To provide mathematical approach to the theory Of Error Correction Coding COURSE TOPICS : MINIMAL SYLLABUS: (1) Basic Models of Communication System : Information Theory (2) Common Sense Approach to the Theory of Block Codes (3) Mathematical Preliminaries: Groups and Vector Spaces (4) Linear Block Codes (5) Cyclic Codes, Rings and Polynomials ADDITIONAL CONTENT ( TIME PERMITTING ): (6) Rudiments of Number Theory and Algebra (7) BCH and Reed Solomon Codes TEXT BOOK: "ERROR CORRECTION CODING: Mathematical Methods and Algorithms" by TODD K. MOON (Wiley Inter science publishers) (1) S.Lin abd D.J.Costello, Error Control Coding : Fundamentals and Applications, (2) R.E. Blahut, Theory and Practise of Error Control Codes, Addison Wesley Or the more recent title Algebraic Codes for Data Transmission *PROJECT: Programming Project will be given GRADING: 1. Mid Terms : 40% marks 2. Homeworks : 20 % Marks 3. Final Exam : 40 % marks OUTCOME: Good understanding of mathematical concepts utilized in the design of error control codes. Application of the concepts in designing block codes. ** : General and Structural Chemistry CREDITS : 3-1-0-4 TYPE-WHEN : Core for CND /Open elective for others: Spring 2013 FACULTY NAME : Deva Priyakumar + Abdul Rehman PRE-REQUISITE: None OBJECTIVE : Help students to understand basic principles of chemistry from a cross disciplinary point of view. COURSE TOPICS: 1. Atomic Structure and Periodicity: The importance of chemical principles, introduction to atomic structure and need for quantum mechanics, periodic classification of elements, outer electronic configuration, periodicity in properties, classification into metals, non-metals and insulators. 2. Chemical Bonding and Shapes of Compounds: Structure and bonding, VSEPR theory, molecular orbital theory, shapes of molecules, hybridization, dipole moment, ionic solids and lattice energy.

3. Classification of elements: Main Group Elements (s and p blocks): Chemistry with emphasis on group relationship and gradation in properties; structure of electron deficient compounds of main group elements and application of main group elements. 4. Transition Metals (d block): Characteristics of 3d elements and coordination complexes, color and magnetic properties of metal complexes. 5. Rare gas: Structure and bonding in rare gas compounds. 6. Acid-base equilibrium: Hard-Soft Acid Bases (HSAB theory), Chemical and biological buffers. 7. Basic Concepts in Organic Chemistry and Stereochemistry: Electronic (resonance and inductive) effects, Optical isomerism in compounds containing one and two asymmetric centers, designation of absolute configuration, conformations of cyclohexanes, aromaticity and Huckel s rule.\ 8. Equilibria, rates and mechanism of chemical reactions: Control of equilibria and rate of reactions, enthalpy and entropy, intermediates and transition states, role of solvent and catalyst, how mechanism of reactions are discovered. 9. Coordination chemistry: Nomenclature, Isomerism in coordination compounds, splitting of orbitals in various ligand fields, Crystal field and ligand field theories, MO theory of coordination compounds. 10. Laws of thermodynamics: Enthalpy and thermochemistry, Entropy and free energy, criterion of spontaneity for equilibrium processes. 11. Physical and chemical equilibrium: Solutions and phase equilibria: Colligative properties, Electrolytes and non-electrolytes, Ideal and non-ideal solutions, colloids; Chemical equilibrium in the gas phase equilibrium constants and their relation to free energy temperature dependence; Equilibrium in the aqueous phase ph, buffers and indicators complex ions-3; Heterogeneous equilibria adsorption-1. 12. Electrochemistry voltage and free energy standard potentials. Ralph H. Petrucci, General Chemistry: Principles & Modern Applications, 8th Edition, Addison Wesley Longman (2003) 1. J. D. Lee, Concise Inorganic Chemistry, 5th Edition, Wiley-Blackwell 2. J. E. Huheey, R. L. Keiter and E. A. Keiter and O. K. Medhi, Inorganic Chemistry: Principles of Structure and Reactivity, 4th Edition, Pearson Education (2008) 3. J. Clayden, N. Greeves, S. Warren, P. Wothers, Organic Chemistry, Oxford University Press 4. T. E. Brown, H. E. LeMay, B. E. Bursten, C. Murphy, Chemistry: The Central Science, 11th Edition, Prentice Hall 5. P W Atkins, Elements of Physical Chemistry, 5/E, Oxford University Press (2010) GRADING: Assignments, Project* and Quizzes - 60% Exams - 40% [2 midterm (10 + 10) + Endsem (20)] Total - 100% *To be decided OUTCOME: Students would be chemenabled to appreciate current research in natural (physical and biological) sciences. For CND students this will be a core prerequisite course and hence, need to be fine tuned after assessing the abilities and the potentials of the CND students. The grading plan may accordingly be modified, after a couple of weeks, to accommodate a project. Non-CND students will be selected on the basis of their interest in CNS as a domain in general, and in chemistry in particular. A personal interview will be conducted before accepting the enrollment of non-cnd students. Course Name : Indian Grammatical Tradition Applied to NLP Objective : To explore the application of Indian grammatical traditions to modern languages. A major goal of the course would be to study how language conveys meaning. Faculty Name : Dipiti M Sharma Evaluation : HA 15 Seminar 15 Project 35 Endsem 35 Topics to be Covered : The course will be discussion oriented a) The relation between phrase structure grammar and dependency grammar. b) English from Paninian grammatical viewpoint c) Concepts to be discusssed :

sphota, pravitti nimitta prakritti pratyaya vibhag karaka, vibhakti, samas, samarthya tatparya, vivakshaa aakaamkshaa, yogyataa, sannidhi d) Tools to implement the theoretical framework Reading List On the Architecture of Panini's Grammar. Three lectures delivered at the Hyderabad Conference on the Architecture of Grammar, Jan. 2002, and at UCLA, March 2002. Panini, His Work and its Traditions by George Cardona Motilal Banarsidass 1988 Panini : Re-interpreted by Charu Deva Shastri Motilal Banarsidass 1990 Bhartrihari : A Study of the Vakyapadiya in the light of the Ancient Commentaries by K.A> Subramania Iyer, Deccan College, Poona 1969 : Information Retrieval and Extraction CREDITS : 3-1-0-4 TYPE-WHEN : FACULTY NAME : Vasudeva Varma PRE-REQUISITE: OBJECTIVE : COURSE TOPICS: Search, Information Retrieval, Information Extraction - An Introducion (Function of an IR system, Kinds of IR systems, Components of an IR system, Problems in designing an IR system., The nature of unstructured and semi-structured text). Role of Language Processing in Search, IR and IE, Role of Machine Learning in Search, IR and IE, Modeling documents for IR purpose - Vector model, term weighing, Similarity measures, text collections and issues, Text processing and Indexing Techniques (Preliminary stages of text analysis and document processing, tokenization, stemming, lemmatization, stop words, phrases), Data Structures for IR and IE, distributed and Parallel IR (Advanced Indexing, query expansion, Postings size estimation, merge sort, dynamic indexing, positional indexes, n-gram indexes, Index compression, Web Based Search, Page Ranking, LSI, Evaluation of IR and IE Systems, Ontologies and Categorization, Named Entity Recognition, Personalization, Question Answering, Summarization Cross Lingual Information Retrieval, Other applications and Conclusions, 1. Modern Information Retrieval, by R. Baeza-Yates and B. Ribeiro-Neto. 2. Information Retrieval: Algorithms and Heuristics by D. Grossman and O. Frieder *PROJECT: There are no home assignments. This is a project Intensive course. Groups will have project deliverables every alternate week. Project Deliverable: Finalize the project, Preliminary study and requirements Specification document, Architecture and D GRADING: Project - 80% (Evaluated every alternate week) Take Home Exam - 20% OUTCOME: : Information Security Audit and Assurance CREDITS : 3-0-0-4 TYPE-WHEN : Spring FACULTY NAME : Shatrunjay Rawat & Mulualem Teku PRE-REQUISITE : Basic understanding of Computer Networks and Operating Systems OBJECTIVE : To learn how to evaluate and enhance information security of IT infrastructure and organizations COURSE TOPICS : (1) Introduction to Information Security (2) Security weaknesses in various networking protocols IP, TCP, UDP, SMTP, RIP, OSPF, etc. (3) Network Security Products Firewall, IDS/IPS, VPN Devices, Content Screening Gateways, etc. (4) Physical Security Access Control Systems, Video Surveillance, etc. (5) Security Features of Operating Systems (6) PKI

(7) Security Standards ISO 27001, Indian IT Act, IPR Laws (8) Security Audit procedures (9) Developing Security Policies (10) Disaster Recovery, Disaster Management (11) Business Continuity Management (12) Security considerations while developing software The course will be primarily driven by class room discussions and assignments. No single text book. Required study material will be identified as course progresses. REFERENCE BOOKS: RFCs; Various Acts/Laws and Standards; Security Guideline documents of Operating Systems PROJECT: TBD GRADING: Based on class participation, presentations, assignments, Mid/End Sem exams, Viva, etc. OUTCOME: Understanding of security needs and issues of IT infrastructure. Have basic skills on security audit of networks, operating systems and application software. : Internals of Application Servers CREDITS : 3-1-0-4 TYPE-WHEN : FACULTY NAME : Dr. Ramesh Loganathan PRE-REQUISITE: OBJECTIVE : COURSE TOPICS: Understand essence of middlewares and distributed object technology. J2EE Technology and Architecture overview. J2EE App Server architecture. Lifecycle of a J2Eeapplication-deployment thru running and unemployment. Web Container internals. EJB Container internals. Essentials of Clustering architecture, Project problems Discussions *PROJECT: GRADING: OUTCOME: : Introduction to Neural and Cognitive Modeling Course Code : To be given. CREDITS : 4 TYPE-WHEN : Spring semester (Dec 27- May 5) FACULTY NAME: Professor S. Bapi Raju, Dr. Dipanjan Roy, and Dr. John Eric Steephen PRE-REQUISITE: Interest in Neuroscience and Cognitive Science, Basic background in Calculus, Probability and Statistics, Linear Algebra, Ordinary Differential Equations and aptitude for programming. OBJECTIVE: This is an introductory course on computational models used in Neuroscience and Cognitive Science. The emphasis is on multiple scales (three levels) of modeling Single Neuron-level, Network-level and Abstract (Connectionist and Bayesian) models. The course emphasizes the need for and role of theory and computation in Neuroscience and Cognitive Science. COURSE TOPICS: Part I: Introduction to Neuroscience; Compartmental models of neuron; Spiking Neuron models. Part II: Neural population codes; information representation; neural encoding and decoding; hierarchy and organization of sensory systems; Spiking Network models of sensory systems; Neuroplasticity and learning. Part III: Introduction to Hebbian, Competitive and Error-driven learning rules; Neural Network models of Perception, Attention, Memory, Language and Executive Function. REFERENCE BOOKS:

1) J. M. Bower and D. Beeman (2003). The Book of GENESIS: Exploring Realistic Neural Models with the GEneral NEural SImulation System, Internet Edition. 2) Peter Dayan and L. F. Abbott (2005). Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems. MIT press. 3) R. O'Reilly & Y. Munakata (2000). Computational Explorations in Cognitive Neuroscience: Understanding the Mind by Simulating the Brain. MIT Press. PROJECT: (see below) GRADING: Mid-term Exam(s): 30% Final Exam: 40% Quiz / Assignment, Project: 30% OUTCOME: At the end of the course, students will have an appreciation of models used in Neuroscience at multiple levels of resolution and would acquire familiarity with programming environments that implement them. Although the course stands independently by itself, it adds computational perspective to courses such as Introduction to Cognitive Science and Introduction to Cognitive Neuroscience. : Introduction to Robotics: Mechanics & Control Credits : 3-1-0-4 FACULTY NAME : Suril V Shah Requisite : A course in linear control systems and the like Course Description: Robotics is an inter-disciplinary subject concerning areas of mechanics, electronics, information theory, control and automation. This course provides an introduction to robotics and covers fundamental aspects of modeling and control of robot manipulators. Topics include history and application of robotics in industry, rigid body kinematics, manipulator forward and inverse kinematic solution methods, Jacobians, singularities, redundancies, serial link manipulator dynamics, trajectory generation, sensors and actuators, position control and interaction force control. Syllabus & Timetable: Overview [w 1] Introduction to Robotics Manipulators [w 1] Rigid Motions: Spatial Descriptions and Transformations [w 1-3] Forward and Inverse Kinematics, Workspace, and Redundancies [w 3-4] Differential Kinematics and Statics [w 5-6] Dynamics [w 7-8] Position Control [w 8-11] Force Control [w 11] Trajectory Generation [w12] Actuators and Sensors [w 7-12] (Time Permitting) Text Book: "Introduction to Robotics: Mechanics and Control," by John J. Craig, 3rd edition, Pearson Prentice-Hall, 2005. (Several copies Available in the Library) Additional References: Robotics : Fundamental Concepts and Analysis, by Ashitava Ghosal,Oxford University Press.(Available in the Library) Lab Experiments: Students will have the opportunity to build robot models with CAD softwares like Solidworks and MSC Visual Nastran and also integrate them using MATLAB and SIMULINK. Grading Scheme: Assignments 15% Laboratories 10% Mid-Term 30% Final 45% : INTRODUCTION TO SYSTEMS BIOLOGY Course Code : CREDITS : 4 TYPE-WHEN : SPRING

FACULTY NAME : Dr. V. SHRIDEVI + VINOD PALAKKAD PRE-REQUISITE: OBJECTIVE : This course provides an overview of systems biology approaches and tools, and will enable students to integrate concepts from multiple disciplines and understand how advances in biochemistry, cell and molecular biology, genomics, proteomics, computation, and bioinformatics support novel insights into biological complexity. COURSE TOPICS: UNIT -1 SYSTEMS LEVEL REASONING: o Bottom-Up and Top-Down Approaches for Complex Systems OVERVIEW OF CELL PHYSIOLOGY: o Cell growth, division, motility, differentiation, death, homeostasis, excitability CELL SIGNALING PATHWAYS: o From molecules to Pathways SIGNAL FLOW: From Pathways to Networks o Types of networks in cellular systems biology: protein-protein, metabolic, mirna, gene regulatory networks UNIT 2 MATHEMATICAL REPRESENTATIONS OF CELL BIOLOGICAL SYSTEMS o Input/output relationships o Enzyme Kinetics o Design principles of biological systems o Deterministic and stochastic modelling o Parameter estimation and sensitivity analysis o Spatial modelling o Modelling of signaling pathways o Biological Switches and Clocks o Metabolic networks and flux balance analysis o Advantages and limitations of various modelling techniques SIMULATIONS OF CELL BIOLOGICAL SYSTEMS MODELLING STANDARDS AND TOOLS UNIT-3 NETWORK BIOLOGY: o Graph theoretic description of network o Scale free networks in biology o Motifs, modules and hierarchical networks o Network Robustness o Bayesian networks NETWORK INFERENCE AND VISUALIZATION: o Introduction to high throughput data analysis o Genes2networks, lists2networks, from expression patterns to regulatory networks o Tracing pathways with chip enrichment analysis and kinases enrichment analysis o Visualization of networks using pajek, cytoscape UNIT-4 UNIT- 5 o Visualizing large scale dynamics: Grid analysis of time series expression ANALYZING LARGE DATA SETS: o Statistical tests to identify differentially expressed genes o Programs for analyzing genomic datasets: cufflink, cuffdiff o Annotating differentially expressed genes and proteins for sub cellular functions o Gene set enrichment analysis NETWORK ANALYSIS: o Analysis of network from differentially expressed genes o Analysis of protein-protein interaction networks o Combining annotation of nodes with network topology APPLICATIONS OF SYSTEMS BIOLOGY: o Systems Biotechnology o Systems and Synthetic Biology o Systems Analysis of Complex Diseases o Systems Pharmacology: Understanding Drug Action from a Systems Perspective

1. Systems Biology: A Textbook answers to problems By Edda Klipp, Wolfram Liebermeister, Christoph Wierling, Axel Kowald, Hans Lehrach, Ralf Herwig Wiley-VCH, 1st edition (August 2009). 2. Systems Biology: Properties of Reconstructed Networks By Bernhard O. Palsson Cambridge University Press; 1st edition (January 2006). 3. An Introduction to Systems Biology: Design Principles of Biological Circuits by Uri Alon, Chapman & Hall, 1st edition (July 2006). GRADING: Mid semester exam 1 20% Mid semester exam 2 20% End semester exam 40% Assignments 10% OUTCOME: After successfully completing the course, students are expected to have a good understanding of the basic concepts, challenges, current research topics, and trends in selected topics of computational systems biology. : Introduction to Biology CREDITS : 3-1-0-4 TYPE-WHEN : FACULTY NAME : Dr. A. Rameshwar PRE-REQUISITE: OBJECTIVE : COURSE TOPICS: Characterization of life, Atoms and molecules in living organisms, Organic Molecules-Monomers, Macromolecules, cells, Chemical reactions, Energy transformations, Photosynthesis, respiration, Heredity, Genes, Flow of Genetic Information, Origin of life, Evolution, Systematics, Growth & Development. Homeostasis, Disease and defense mechanisms, Aging, cell death. Voet,Voet & Pratt, Fundamentals of Biochemistry, Wiley pp931, MBV Roberts Biology: A Functional Approach., ELBS pp693 *PROJECT: GRADING: OUTCOME: Title : Linear Algebra Credits : 4 (three hours per week plus tutorial) When : Spring Faculty Name : Rajat Tandon Prerequisites : Basic 12th class algebra including basic operations on matrices and the definition of a Group. Objective: to give a theoretical justification for matrix theory. Course Topics: Basic Groups, Rings and Fields. Vector spaces, linear span, linear independence, existence of basis. Linear transformations. Solutions of linear equations, row reduced echelon form, complete echelon form, rank. Minimal polynomial of a linear transformation. Jordan canonical form. Determinants. Characteristic polynomial, eigenvalues and eigenvectors. Inner product space. Gram Schmidt orthoganalization. Unitary and Hermitian transformations. Diagonalization of Hermitian transformations. : Linguistics Data 2: Collection and Modeling CREDITS : 4 TYPE-WHEN : Spring FACULTY NAME: TBD PRE-REQUISITE: Linguistic Data I

OBJECTIVE: The objective of Linguistic Data II course is to introduce the students to the necessary concepts and the methods for analysing linguistic data at different levels of language organization. They will also be given practical training in analyzing data, storing and modeling it for NLP applications. COURSE TOPICS: 1. Discourse structure theory - Informational structure - Attentional structure - Intentional structure 2. Collection and formatting of data from various web resources 3. Various discourse annotation schema - Penn Discourse Tree Bank (PDTB) - Hindi Discourse Relation Bank (HDRB) - Rhetorical Structure Theory (RST) - Indian grammatical tradition 4. Annotation of collected data 5. Discourse modelling GRADING: HA10, Seminar 10, Project 30, MidSem 20, End Semester 30 Reference: - PDTB Guidelines - HDRB Guidelines - RST Manual Grosz and Sidner. 1986. Attention, intentions, and the structure of discourse. Amba kulkarni and Monali Das. 2012. Discourse Analysis of Sanskrit Texts. : Medical Image processing CREDITS : 3-1-0-4 TYPE-WHEN : FACULTY NAME : Dr. Jayanthi Sivaswamy PRE-REQUISITE : Digital image processing (preferred) OBJECTIVE : Medical images are a vital and widely used source of diagnostic information. From simple X-rays to SPECT and FMRI such images provide a window into the functioning of human bodies and other organisms. Processing of medical images is needed for various purposes ranging from providing high quality information for visual inspection and guidance for surgeries, to extracting higher order information about the condition of different tissues/organs/structures. This course will provide an hands-on introduction to the exciting area of medical image processing, an area of focus for several major international conferences. COURSE TOPICS : 1. Physics of medical imaging Optical, X-ray, acoustic, magnetic and nuclear 2. Fundamentals Types of images, data formats, tools for medical image processing (ITK, VTK) 3. 3D and nd image processing 4. Problems in med IP Image conditioning illumination/geometric correction, denoising Segmentation Geometric and other methods Rigid and non-rigid image registration and Fusion Reconstruction 5. Validation of results Signal detection theoretic issues Fundamentals of medical imaging by Paul Seutens, Cambridge University Press. Handbook of Medical Imaging, Vol 2: Medical image processing and analysis, by Jacob Beutel and Milan Sonka. Geometric methods in medical image processing by Ravikanth Malladi. Medical image processing- reconstruction and restoration by Jiri Jan. Medical Imaging Systems, by Albert Macouski, Prentice Hall, New Jersey. *PROJECT: GRADING: 2 midsem exams (40%) + 1 final project (30%)+ assignments using ITK (National Library of Medicine Insight Toolkit) an open source software library (30%). OUTCOME: : Modeling and Simulations CREDITS : 3-0-1-4 TYPE-WHEN : Bouquet core & Open elective, Spring 2011-12 FACULTY NAME : M.Krishnan + Abdul Rehman PRE-REQUISITE: None

OBJECTIVE : To introduce the fundamental concepts of molecular modeling and simulation to students (mainly for computational natural sciences and bioinformatics students) and motivate/train them to apply these concepts/techniques to solve interesting research problems. COURSE TOPICS: 1 Basic Maths: coordinate systems, vector algebra, differential equations, matrices, Taylor expansion (1 lecture) 2 Molecular Mechanics: Molecular force fields, energy minimization (2 lectures) (3) Molecular Dynamics: Equations of motion, phase space distribution functions, sampling, integrators, boundary conditions, electrostatics, molecular constraints (5 lectures) (4) Free energy calculations: Umbrella sampling, thermodynamic integration, replica exchange method (2 lectures) (5) Monte Carlo methods: Pi-value computation, important sampling, Metropolis algorithm, applications (1 lecture) (6) Non-equilibrium molecular dynamics: Jarzynski equality, steered molecular dynamics, shear flow (2 lectures) (7) solvent models: Implicit models, explicit models (1 lectures) (8) Quantum Chemistry: Operators, wavefunctions, postulates, probability density, time-dependent Schrodinger equation (2 lectures) (9) Translational, rotational, vibrational dynamics of simple quantum systems, hydrogen atom (3 lectures) (10) Molecular quantum mechanics: Born-Oppenheimer approximation, LCAO, Variation theorem, perturbation theory, Huckel theory, HF, semi-empirical methods, electron correlation, CI (4 lectures) (11) DFT (1 lecture) (12) Force field parameterization using quantum mechanical methods (1 lecture) (13) Students presentations (3 lectures) 1. Computer Simulation of Liquids, by M.P. Allen and D.J. Tildesley 2. Understanding Molecular Simulation: From Algorithms to Applications, by D. Frenkel and B. Smit 3. Molecular Quantum Mechanics by Atkins *PROJECT: GRADING: Will be decided later after discussing with students OUTCOME: : NLP Applications CREDITS : 3-0-1-4 TYPE-WHEN : FACULTY NAME : Manish Shrivastava PRE-REQUISITE : Intro to NLP OBJECTIVE : This is the advanced course in Natural Language Processing intended for honors, dual degree, BTP, MTech and PhD students. COURSE TOPICS : In this course, students get an overview of various areas in NLP and the current research trends in each of them. The topics covered include machine translation (rule based & statistical), discourse, statistical parsing, word sense disambiguation, natural language generation, co reference resolution, semantic role labeling etc.. The course also covers two of the most popular machine learning methods (Expectation-Maximization and Maximum Entropy Models) for NLP. Students would be introduced to tools such as NLTK, CoreNLP to aid them in their research. *PROJECT: There will be a mini project and research readings once every alternate week. : Optical Communication and Networks CREDITS : 3-1-0-4 TYPE-WHEN : Spring Semester