Course Contents for the new Elective courses
offered by the
Department of Computer Science and Engineering
· Stream Analytics
· Machine Learning with Big Data
· Algorithms for Big Data
· Computer Vision
· Digital Image Analysis
· Edge and Fog Computing
· Natural Language Processing
· GPU Programming
· Data Visualization
· Introduction to Augmented Reality and Virtual Reality
· Advanced Computer Graphics
· Embedded Systems
· Bioimage computing
· Neuromorphic Computing and Design
· Dependable AI
· Resource Constrained AI
· Ad hoc Wireless Networks
· Vehicular Adhoc Networks (VANETs)
· Selected Topics in Computer Science  I
· Selected Topics in Computer Science  II
· Selected Topics in Computer Science  III
· Selected Topics in Artificial Intelligence  I
· Selected Topics in Artificial Intelligence  II
· Selected Topics in Artificial Intelligence  III
· Graph Theory and Applications
· Social Network Analysis
· Blockchain
· Computational Optimization
Title 
Stream Analytics 
Number 
CSL8XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
B.Tech. 3^{rd} and 4^{th} Year, M.Tech. 1^{st} and 2^{nd} Year 
Type 
Elective 
Prerequisite 
Machine Learning1 


Objectives The Instructor will: 1. Provide background on some of the important models, algorithms, and applications related to stream data
Learning Outcomes The students are expected to have the ability to: 1. Understand and apply the practical and algorithmic aspects related to various topics of data streams
Contents Introduction: Stream and mining algorithms (2 lectures) Clustering massive data streams: Microclustering based stream mining, Clustering evolving data streams, Online Microcluster maintenance, Highdimensional projected stream clustering, Classification of data streams using microclustering, Ondemand stream classification, Applications of microclustering (7 lectures) Classification methods in data streams: Ensemble based classification, Very fast decision trees, On demand classification, Online Information Network, LWClass algorithm, ANNCAD algorithm, ALLOP algorithm (5 lectures) Distributed mining of data streams: Outlier and anomaly detection, Clustering, Frequent itemset mining, Classification, Summarization, Mining distributed data streams in resource constrained environments (5 lectures) Change diagnosis algorithms in evolving data streams: Velocity density method, Use of clustering for characterizing stream evolution (5 lectures) Multidimensional analysis of data streams using stream cubes: Architecture for online analysis of data streams, Stream data cube computation, Performance study (4 lectures) Indexing and querying data streams (3 lectures) Dimensionality reduction and forecasting on streams: Principal Component Analysis, Autoregressive models and recursive least squares, MUSCLE, Tracking correlations and hidden variables (6 lectures) Distributed data stream mining: Local algorithm, Bayesian network learning (5 lectures)
Textbook 1.Aggarwal,C.C., (2007), Data Streams: Models and Algorithms, 1^{st} Edition, Kluwar Academic Publishers
Reference Books 1. Research literature
Self Learning Material 
Title 
Machine Learning with Big Data 
Number 
CSL8XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
B.Tech. 4^{th} Year, M.Tech. 2^{nd} Year, Ph.D. 2^{nd} Year 
Type 
Elective 
Prerequisite 
Artificial Intelligence1 and Machine Learning1 


Objectives The Instructor will: 1. Provide an understanding of the role of big data in the realworld scenarios 2. Provide technical details about various algorithms and software/hardware tools/platforms related to big data
Learning Outcomes The students are expected to have the ability to: 1. Develop an understanding of big data in the modern context, and independently work on problems relating to bigdata 2. Design and program efficient algorithms for big data from the perspective of a project
Contents Introduction: What is big data, Unreasonable effectiveness of data (1 lecture) Streaming algorithms: Streaming Naive Bayes, Stream and sort (2 lectures)
Platforms for learning from big data MapReduce, New Software Stack, Large Scale File System Organization (5)
Nearest Neighbour Search, Jaccardi Similarity of Sets, Similarity of Documents, Locality Sensitive Hashing, The Stream Data Model (4)
Randomized methods: Clustering, Hashing, Sketching, Scalable stochastic gradient descent (3 lectures)
Frequent Itemsets: The Market Basket Model, APriori Algorithm, Handling larger datasets in Main Memory, LimitedPass Algorithms, Counting Frequent Items in a Stream (6)
Parameter Servers: Introduction, Abstraction, Parameter Cache Synchronization, Asynchronous execution, Model Parallel Examples (3 lectures)
Graphbased methods: Link Analysis Page Rank, Topic Sensitive Page Rank, Approaches to Page Rank iteration, Link Spam, Semisupervised learning, Scalable link analysis, Models for Recommendation Systems, Social Networks as Graphs (9 lectures)
Largescale Machine Learning with CPUs and GPUs (3 lectures)
Textbook 1.Leskovec,J., Rajaraman,A., Ullman,J., (2014), Mining of Massive Datasets, 2^{nd} Edition, Cambridge University Press 2. Bekkerman,R., Bilenko,M., Langford,J., (2011), Scaling Up Machine Learning, Cambridge University Press
Reference Books 1. Research literature
Self Learning Material 1. Department of Machine Learning, Carnegie Mellon University, Machine Learning with Large Datasets Course 2. Department of Computer Science, University of California, Berkeley, 3. ETH Zurich, Data Mining: Machine Learning from Large Datasets 
Title 
Computer Vision 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
B.Tech. 4^{th} Year, M.Tech. 1^{st} and 2^{nd} Year, Ph.D. 2^{nd} Year 
Type 
Elective 
Prerequisite 



Objectives The Instructor will: 1. Provide insights into fundamental concepts and algorithms behind some of the remarkable success of Computer Vision 2. Impart working expertise by means of programming assignments and a project
Learning Outcomes The students are expected to have the ability to: 1. Learn and appreciate the usage and implications of various Computer Vision techniques in realworld scenarios 2. Design and implement basic applications of Computer Vision
Contents Introduction: The Three R’s  Recognition, Reconstruction, Reorganization (1 lecture) Perspective: Static Perspective, Transformations, Dynamic perspective (5 lectures) Fundamentals of Image formation and processing: Radiometry of image formation, Basic image processing, Biological visual processing (5 lectures) Recognition: Object recognition case study  identifying digits with multiple approaches, Visual grouping, Convolutional Neural Network (ConvNet) based approaches to visual recognition of objects and scenes, Deformable Parts Model (DPM), Attributes, pose and actions (8 lectures) Analysis: Binocular Stereopsis, Markov Random Fields in Computer Vision, Solving for stereo correspondence, Optical flow, Review of differential geometry (12 lectures) Detection and Segmentation: Contour detection, Bottomup segmentation, Gestalt grouping heuristics, Semantic segmentation  instance segmentation and pixel classification, Pose and keypoint estimation (5 lectures) Image understanding: Scene understanding from RGBD images, 3D perception from a single image, Face recognition (6 lectures)
Textbook 1.Hartley,R. Zisserman,A., (2004), Multiple View Geometry in Computer Vision, 2^{nd} Edition, Cambridge University Press 2. Szeliski,R., (2010), Computer Vision: Algorithms and Applications, SpringerVerlag London
Reference Books 1. Research literature

Title 
Digital Image Analysis 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 

Type 
Elective 
Prerequisite 
None 


Objectives The Instructor will: 1. Introduce the origin and formation of digital imaging. 2. Develop the understanding of different types of image processing and analysis for different purposes. 3. Show how to develop modular systems for image analysis through handson application development. Learning Outcomes The students are expected to have the ability to: 1. Enhance image in spatial and frequency domain. 2. Implement various aspects of image segmentation, compression, and content analysis.
Contents Digital Image Fundamentals: Image modeling, Sampling and Quantization, Imaging Geometry,Digital Geometry, Image Acquisition Systems, Different types of digital images. (4) Bilevel Image Processing: Basic concepts of digital distances, distance transform, medial axis transform, component labeling, Histogram of grey level images, Optimal thresholding. (5) Images Enhancement: Point processing, enhancement in spatial domain, enhancement in frequency domain. (5) Detection of edges and lines in 2D images: First order and second order edge operators,multiscale edge detection, Canny's edge detection algorithm, Hough transform for detecting lines and curves. (5) Color Image Processing: Color Representation, Laws of color matching, chromaticity diagram, color enhancement, color image segmentation, color edge detection. (5) Image compression: Lossy and lossless compression schemes, prediction based compression schemes, vector quantization, subband encoding schemes, JPEG compression standard. (6) Segmentation: Segmentation of grey level images, Watershed algorithm for segmenting grey level image. (5) Morphology: Dilation, erosion, opening, closing, hit and miss transform, thinning, extension to grey scale morphology. (4) Feature Detection: Fourier descriptors, shape features, object matching/features(3)
Textbook 1. Gonzalez and Woods, Digital Image Processing, PrenticeHall. 2. Fundamentals of Digital Image Processing by Anil K. Jain.
SelfLearning Material NPTEL: Digital Image Processing https://nptel.ac.in/courses/117105079/ 
Title 
Edge & Fog Computing 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
M.Tech., Ph.D 
Type 
Elective 
Prerequisite 



Objectives The Instructor will: 1. Introduce research, frameworks, and applications in Edge Computing to the audience
Learning Outcomes The students are expected to have the ability to: 1. Understand various edge devices and their ecosystems 2. Develop edgebased distributed computing platforms and applications
Contents CSL7XX1 Introduction to IoT 100 [1] (fractal 1) Introduction to IoT: Internet of things as an interdisciplinary domain, IoT as a data centric technology, Data, information, knowledge and wisdom (DIKW) relationship (3 lectures) Analytics in IoT: Analytics as a knowledge extraction technique, Role of Statistical analysis, Machine learning, Deep learning and Artificial Intelligence in the emergence of Internet of things, IoT Semantics and Streaming data analysis (4 lectures) IoT Endpoint  architecture, design, and performance: IoT Endpoint architecture, Design and development of IoT endpoints (4 lectures) IoT Gateways: Roles of Gateway in IoT networks– Field Gateway, stateoftheart solutions (3 lectures) CSL7XX2 Communication in IoT 100 [1] (fractal 2) Communication in IoT: Fundamentals of data communication, Network architecture and reference models (OSI – TCP/IP), Communication technologies standards – Wired & Wireless data link layer standards (Bluetooth/WiFi/Zigbee/802.15.4/LoRa/Sigfox), Application layer protocols – HTTP, MQTT, CoAP, AMQP (11 lectures) Sensor Networks: Algorithmic Models for Sensor Networks, Aggregation service for adhoc sensor networks, GossipBased Computation of Aggregate Information, Optimal aggregation algorithms for middleware, Efficient topK query calculation in distributed networks (3 lectures) CSL7XX3 Cloud and IoT 100 [1] (fractal 3) Cloud and IoT (Fog & Edge): Cloud and IoT services stack, End to End solutions development and design of cloudbased IoT services, device integration, stream analytics, Analytics use cases: Smart building, smart cities, wearable, smart retail and smart workspaces, Leveraging cloud hosted application to build monitoring and control solutions integrated with field devices, Integration of user devices with cloud hosted applications/services to enable users to interact with gateways and end points (14 lectures)
Textbook 1. Buyya R, Srirama S. N., (2019), Fog and Edge Computing: Principles and Paradigms, 1^{st} edition, Wiley
Self Learning Material 1. Cao J.,, Zhang Q, Shi W., (2018), Edge Computing: A Primer, Springer
Preparatory Course Material 1. Edge Computing (EDGE) Conference Series, Springer

Title 
Natural Language Processing 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
M.Tech. 2^{nd} Year 
Type 
Elective 
Prerequisite 



Objectives The Instructor will: 1. Provide background to understand various modern techniques for natural language processing, understanding, and synthesis
Learning Outcomes The students are expected to have the ability to: 1. Explain various NLP algorithms 2. Implement NLP Systems for English Language
Contents Introduction: NLP tasks in syntax, semantics, and pragmatics, Applications such as information extraction, question answering, and machine translation, The problem of ambiguity, The role of machine learning. Brief history of the field (4 lectures) Language Models: The role of language models, Simple Ngram models, Estimating parameters and smoothing, Evaluating language models, Part of Speech Tagging (10 lectures) Sentences: Basic ideas in compositional semantics, Classical Parsing (Bottom up, top down, Dynamic Programming: CYK parser) (7 lectures) Syntactic parsing: Grammar formalisms and treebanks, Efficient parsing for contextfree grammars (CFGs), Statistical parsing and probabilistic CFGs (PCFGs), Lexicalized PCFGs, Neural shiftreduce dependency parsing (7 lectures) Semantic Analysis: Lexical semantics and wordsense disambiguation, Compositional semantics, Semantic Role Labeling and Semantic Parsing (3 lectures) Information Extraction: Named entity recognition and relation extraction, IE using sequence labeling (3 lectures) Machine Translation: Basic issues in MT. Statistical translation, word alignment, phrasebased translation, and synchronous grammars (8 lectures)
Textbook 1. 1. Jurafsky,D. and Martin,J.H., (2003), Speech And Language Processing, 2nd Edition, Pearson Education India 2. SelfLearning Material 1. Goldberg,Y. and Hirst,G., (2017), Neural Network Methods for Natural Language Processing, Morgan & Claypool Publishers 2. Clark,A., Fox,C. and Lappin,S., (2010), The Handbook of Computational Linguistics and Natural Language Processing, WileyBlackwell
Preparatory Course Material 1. Natural Language Processing with Python, Steven Bird, Ewan Klein and Edward Loper, O’Reilly, http://www.nltk.org/book/ 2. Natural Language Pocessing, NPTEL Lectures, https://www.youtube.com/watch?v=aeOLjFe256E&list=PLD392E2ACAEF0C689

Title 
GPU Programming 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
M.Tech. 2^{nd} Year 
Type 
Elective 
Prerequisite 



Objectives The Instructor will: 1. Provide background to understand various aspects of Graphics Processing Unit (GPU) 2. Introduce parallel programming using GPUs.
Learning Outcomes The students are expected to have the ability to: 1. Explain various concepts involving GPU Programming 2. Implement programs of GPU 3. Debug and profile parallel programs.
Contents Introduction: History, graphics processors, graphics processing units, GPGPUs. Clock speeds, CPU / GPU comparisons, heterogeneity. Accelerators, parallel programming, CUDA / OpenCL / OpenACC (2 lectures) Hello World Computation: Kernels, launch parameters, thread hierarchy, warps/wavefronts, thread blocks/workgroups, streaming multiprocessors, 1D / 2D / 3D thread mapping, device properties, simple programs (8 lectures) Support: Debugging GPU programs. Profiling, profile tools, performance aspects (2 lectures) Memory: Memory hierarchy, DRAM / global, local / shared, private/local, textures, constant memory, Pointers, parameter passing, arrays and dynamic memory, multidimensional arrays, Memory allocation, memory copying across devices, Programs with matrices, performance evaluation with different memories (5 lectures) Synchronization: Memory consistency. Barriers (local versus global), atomics, memory fence, Prefix sum, reduction. Programs for concurrent data structures such as worklists, linkedlists, Synchronization across CPU and GPU (6 lectures) Functions: Device functions, host functions, kernels, functors, Using libraries (such as Thrust), developing libraries, (3 lectures) Streams: Asynchronous processing, tasks, taskdependence, Overlapped data transfers, default stream, synchronization with streams, Events, eventbasedsynchronization  overlapping data transfer and kernel execution, pitfalls (6 lectures) Advanced topics: Case studies, Dynamic Parallelism, Unified virtual memory, MultiGPU processing, Peer access, Heterogeneous processing (8 lectures)
Textbook 1. 1. Kirk,D. and Hwu,W., (2010), Programming Massively Parallel Processors: A Handson Approach, Hwu; Morgan Kaufman 1. SelfLearning Material 1. Cook,S., (2012), CUDA Programming: A Developer's Guide to Parallel Computing with GPUs, Morgan Kaufman
Preparatory Course Material 1. Introduction to Parallel Programming, https://www.youtube.com/watch?v=F620ommtjqk&list=PLAwxTw4SYaPnFKojVQrmyOGFCqHTxfdv2&index=1

Title 
Data Visualization 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
M.Tech. 2^{nd} Year 
Type 
Elective 
Prerequisite 



Objectives The Instructor will: 1. Provide background to understand various aspects of Data Visualization 2. Discuss various principles of visualizing heterogeneous types of data Learning Outcomes The students are expected to have the ability to: 1. Present data with visual representations for the target audience, task, and data 2. Analyze, critique, and revise data visualizations 3. Apply appropriate design principles in the creation of presentations and visualizations
Contents Visual Queries: Process of Seeing, The Act of Perception, Design Implications, Distributed Cognition, Visual Search Strategies (3 lectures) Data and Visualization: Data Type, Coordinate Systems, Scale (2 lectures) Visualization Design: Amount, Distribution, Proportion, Trends, Time Series, Geospatial (10 lectures) Narratives: Telling Stories with Data, Sequencing, Visualization Rhetoric, text visualization (4 lectures) Mapping and Cartography: The Cartogram, ValuebyArea Mapping (4 lectures) Optimal Space Usage: Aspect Ratio Selection, Geometry & Aesthetics, Wilkinson’s Algorithm and its extension (6 lectures) Networks: Scalable, Versatile and Simple Constrained Graph Layout, Visualization of Adjacency, Multiple Network Analysis and Visualization, Visualizing Online Social Networks (7 lectures) Animation and Color: Trend Visualization, Transitions in Statistical Data Graphics, Graphs with Radial Layout, Cartoons, Color and Information, Infographics (7 lectures)
Textbook 1. Tufte,E., (2001), The Visual Display of Quantitative Information, 2^{nd} Edition, Graphics Press 2. Tufte,E., (1990), Envisioning Information, Graphics Press
SelfLearning Material 1. Wilke,C.O., (2019), Fundamentals of Data Visualization: A Primer on Making Informative and Compelling Figures, O’Reilly Media 2. Ware,C. and Kaufman,M., (2008), Visual thinking for design. Burlington: Morgan Kaufmann Publishers 3. Wong,D., (2011), The Wall Street Journal guide to information graphics: The dos and don’ts of presenting data, facts and figures, New York: W.W. Norton & Company
Preparatory Course Material 1. Data Visualization Course, https://curran.github.io/datavizcourse2018/

Title 
Introduction to Augmented Reality and Virtual Reality 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
M.Tech. 
Type 
Elective 
Prerequisite 



Objectives The Instructor will: 1. Discusses such issues, focusing upon the human element of AR and VR 2. Explain the Hardware and software related issues related to AR and VR
Learning Outcomes The students are expected to have the ability to: 1. Explain perceptual concepts governing augmented reality and virtual reality 2. Identify and solve the issues of various augmented reality and virtual reality frameworks 3. Design immersive experience using AR and VR Software
Contents Introduction: Definition of XR (AR, VR, MR), modern experiences, historical perspective, Hardware, sensors, displays, software, virtual world generator, game engines (6 lectures) Geometry of Visual World: Geometric modeling, transforming rigid bodies, yaw, pitch, roll, axisangle representation, quaternions, 3D rotation inverses and conversions, homogeneous transforms, transforms to displays, lookat, and eye transform, canonical view and perspective transform, viewport transforms (8 lectures) Light and Optics: Interpretation of light, reflection, optical systems (4 lectures) Visual Perception: Photoreceptors, Eye and Vision, Motion, Depth Perception, Frame rates and displays (6 lectures) Tracking: Orientation, Tilt, Drift, Yaw, Lighthouse approach (4 lectures) Head Mounted Display: Optics, Inertial Measurement Units, Orientation Tracking with IMUs, Panoramic Imaging and Cinematic VR, Audio (8 lectures) Frontiers: Touch, haptics, taste, smell, robotic interfaces, telepresence, brainmachine interfaces (6 lectures)
Textbook 1. Shirley, M., (2016), Fundamentals of Computer Graphics, 4^{th} Edition, CRC Press 2. LaValle, (2016), Virtual Reality, Cambridge University Press 3. Schmalstieg D, and Hollerer T. (2016). Augmented Reality: Principles & Practice, Pearson Education India
Reference Books 1. Jerald,J., (2015), The VR Book: HumanCentered Design for Virtual Reality, Morgan & Claypool 2. Mather,G., (2009), Foundations of Sensation and Perception, 2^{nd} Edition, Psychology Press 3. Shirley,P., Ashikhmin,M., Marschner,S. and Peters,A.K., Fundamentals of Computer Graphics, 3^{rd} Edition, CRC Press 4. Bowman,D.A., Kruijff,E., LaViola,J.J. and Poupyrev,I., (2014), 3D User Interfaces: Theory and Practice, 2nd Edition, Addison Wesley Professional
Self Learning Material 1. Steven M. LaValle, Video Lectures, https://www.youtube.com/playlist?list=PLbMVogVj5nJSyt80VRXYCYrAvQuUb6dh

Title 
Advanced Computer Graphics 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
M.Tech. 
Type 
Elective 
Prerequisite 



Objectives The Instructor will: 1. Discusses fundamentals of 2D and 3D object modeling and rendering 2. Explain the Hardware and software related issues of Computer Graphics
Learning Outcomes The students are expected to have the ability to: 1. Understand fundamentals of graphics used in various real life applications 2. Identify the performance characteristics of graphics algorithms 3. Employ algorithm to model engineering problems, when appropriate
Contents Introduction: Review of 2D, and 3D Geometry, Viewing and Clipping (5 lectures) Curves and Fractals: Parametric Cubic curves: Bspline, Bezier, Hermite, Surfaces, Fractals and its applications (9 lectures) Solid Modeling: Representation of Solids, Sweep and Boundary Representation, Constructive Solid Geometry (8 lectures) Illumination and Shading: Surface detail, shadows and Transparency, Inter object Reflections Illumination Models, Extended Light Sources, Ray Tracing, Radiosity (6 lectures) Image Based Rendering: Image synthesis, Geometry based, Plenoptic Function, Panorama, Lumigraph, Rendering Virtual Reality (8 lectures) Animation: Introduction, morphing, character animation and facial animation (3 lectures) Graphics Hardware: Specialpurpose computer graphics processors and accelerators (3 lectures)
Textbook 1. Shirley,M., (2016), Fundamentals of Computer Graphics, 4^{th} Edition, CRC Press 2. vanDam,F. and Hughes,F., (2013), Computer Graphics: Principles and Practice, 3^{rd} Edition, Addision Wesley
Reference Books 1. Mukundan,R., (2012), Advanced Methods in Computer Graphics: With Examples in OpenGL, Springer 2. Ruben H., (2017), Computer Graphics: Principles and Practice, Larsen and Keller Education
Self Learning Material 1. Computer Graphics, NPTEL Video Lectures, https://nptel.ac.in/courses/106106090/

Title 
Embedded Systems 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
M.Tech., PhD 
Type 
Elective 
Prerequisite 
Computer Organization and Architecture 


Objectives The Instructor will: 1. Explain the design of embedded systems and introduce concepts of different architectures and programming languages of embedded processors.
Learning Outcomes The students are expected to have the ability to: 1. Program and to design embedded system using embedded processors 2. Design Embedded AI systems 3. Use different IDE and debugging tools
Contents
Introduction: Review of Embedded Computing, embedded system design process (4 lectures)
Architectures of embedded processors: Architecture of ARM Cortex M3, DSP and graphics processors, memory system mechanism, caches, memory management units and address translation, interfacing (10 lectures)
Programming and Software: models for program, data flow graphs, C and assembly language programming of ARM Cortex M3, Hardware Software Codesign (12 lectures)
Embedded Operating Systems: Linux, Processes and real time operating systems; Multirate system; scheduling algorithms (8 lectures)
Embedded AI: Basics of embedded learning and adaptive systems, intelligent sensors, rulebased systems, hardware accelerators for AI, heterogeneous memory system design, current trends and future directions (8 lectures)
Textbook 1. Wolf, M., (2012), Computers as Components: Principles of Embedded Computing System Design, 3^{rd} Edition, Elsevier. 2. Yiu, J., (2013), The definitive Guide to ARM Cortex M3 and M4 Processors, 3^{rd} Edition, Elsevier. 3. Alippi, C., (2014), Intelligence for Embedded Systems: A Methodological Approach, Springer.
Preparatory Course Material 1. Mazidi, M.A., (2007), The 8051 Microcontroller and Embedded Systems: Using Assembly and C, 2^{nd} Edition, Pearson Education India. 
Title 
Bioimage Computing 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 

Type 
Elective 
Prerequisite 
Computer Programming, Basics in linear algebra, probability and statistics 


Objectives The Instructor will: 1. Provide details of biosignal and medical image acquisition process 2. Explain information extraction and image analysis techniques using machine learning with emphasis on the field of healthcare, agriculture and environment
Learning Outcomes The students are expected to have the ability to: 1. Understand different imaging modalities and acquisition process 2. Apply machine learning techniques for biosignal interpretation, image representation and analysis
Contents
CSL7XX1 Biosignal Acquisition and Representation 100 [1] Introduction: Overview of biological signals and biomedical imaging modalities, ECG, NMR spectroscopy, electron microscopy, magnetic resonance imaging, Xray, computed tomography, positron emission tomography, ultrasound, elastography, optical imaging and others, Noise and error propagation in biomedical signals and image data (10 lectures) Visualization: Sectioning, multimodal images, overlays, rendering surfaces and volumes (4 lectures)
CSL7XX1 Machine Learning for Biosignal analysis 100 [1] Reconstruction: Mathematical models of image regularity, random fields, practical data sampling and acquisition schemes (4 lectures) Restoration: Deconvolution, degradation models for corrupted and missing data, Bayesian graphical modeling and inference, regression methods for filtering of CT, MRI ultrasound and other images (4 lectures) Image segmentation, object delineation, classification: Clustering, graph partitioning, classification, mixture models, expectation maximization, variational methods using geometric and statistical modeling, computer aided diagnosis (4 lectures) Registration: Deformation models, optimization algorithms, 2D3D registration, multimodal registration (2 lectures)
CSL7XX2 Deep Learning for Bioimaging 100[1] Enhancement, Segmentation of anatomical structures, subcellular objects, cells, learning with little or no training data, spatial transformer network for registration, imagebased phenotyping, analysis of radiogenomic data (10 lectures) Analysis of motion: Tracking of cells, tissues, organisms, and particles (2 lectures) Interactive image analysis: Human in loop, image interpretation (2 lectures)
Laboratory Experiments Ultrasound image enhancement, tumor segmentation in BraTS dataset, registration of MRI images etc.
Textbook 1. Wu, G., (2016), Machine Learning and Medical Imaging, Elsevier. 2. Epstein, C.L., (2003), Mathematics of Medical Imaging, Prentice Hall. 3. Bankman, I., (2009), Handbook of Medical Image Processing and Analysis, 2^{nd} Edition, Academic Press.
Preparatory Course Material 1. Bishop, C., (2006), Pattern Recognition and Machine Learning, Springer.

Title 
Neuromorphic Computing and Design 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 

Type 
Elective 
Prerequisite 



Objectives The Instructor will: 1. Provide information about neuroscientific progress towards reverseengineering the brain 2. Provide essentials on key hardware building blocks, system level VLSI design and practical realworld applications of neuromorphic Systems
Learning Outcomes The students are expected to have the ability to: 1. View neuromorphic computing as a computer architecture research problem 2. Perform software and hardware implementation of basic biological neural circuits
Contents
CSL7xx1 Introduction to Neuromorphic Engineering 100 [1] (fractal 1) Foundational Concepts: Introduction to neuromorphic engineering, neuroanatomy of human brain, signaling and operation of biological neurons, neuron models  LIF, IF, HH, synapses and plasticity rules, spiketimedependent plasticity (STDP), biological neural circuits, nonvon Neumann computing approach, learning rules, retina, cochlea (14 lectures)
CSL7xx2 Neuromorphic Computing 100 [1] (fractal 2) Neuromorphic Computing: Spiking Neural Networks (SNN), Advanced Nanodevices for Neuron Implementation, Synaptic emulation  nonvolatile memory (NVM), Flash, RRAM, memristors, CNT, Case study on Intel’s Loihi neuromorphic chip (14 lectures)
CSL7xx3 Neuromorphic Hardware Implementation 100 [1] (fractal 3) Hardware Implementation: Electronic synapses, Digital/Analog neuromorphic VLSI, Hardware Implementation of Neuron circuits, Hardware Implementation of Synaptic and Learning circuits, Synaptic programming methodology optimization (14 lectures)
Textbook 1. Liu, S.C., (2002), Analog VLSI: Circuits and Principles, MIT Press. 2. Kozma, R., (2012), Advances in Neuromorphic Memristor Science, Springer. 3. Kandel, E., (2012), Principles of neural science, McGraw Hill.

Title 
Dependable AI 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
B.Tech 4^{th} Year and M.Tech., PhD 
Type 
Elective 
Prerequisite 
Machine Learning, Artificial Intelligence 


Objectives The Instructor will: 1. Provide characteristic details of AI and machine learning systems to make them dependable, such as explainability, interpretability, safety etc.
Learning Outcomes The students are expected to have the ability to: 1. Assess the dependability of AI systems. 2. Develop explainable, robust, and safe AI models.
Contents
Introduction: Overview, motivation, challenges – medical and surveillance (4 lectures) Explainable AI: Accuracyexplainablity tradeoff, interpretability problem, predictability, infobesity, Transparency, Traceability, Environmental effects on AI systems (4 lectures) Methods: Causality, reasoning, layerwise relevance propagation (LRP), attention maps, saliency maps, DeepLIFT, Local Interpretable ModelAgnostic Explanations (LIME) (10 lectures)
Interpretable AI: Prediction consistency, Application Level Evaluation, Human Level Evaluation, Function level evaluation (4 lectures)
Trustworthy AI: Integrity, Reproducibility, Accountability, Interactive AI  Human in the loop, Human on the loop, Human in command, Adaptability, fallback plan, Machine learning as service (MLaaS), General Data Protection Right (GDPR) (6 lectures)
Safe AI: Robustness, Adversarial attacks and defenses  Whitebox, blackbox, graybox attacks, Defence mechanisms (10 lectures)
Biasfree AI: Accessibility, Fair, data agnostics design, disentanglement (4 lectures)
Textbook 1.Pearl, J., (2018), The Book of Why: The New Science of Cause and Effect, Basic Books. Reference Book 1. Bostrom, N., (2014), The Ethics of Artificial Intelligence. The Cambridge handbook of artificial intelligence, Cambridge University Press.
Preparatory Course Material 1. Proceedings of IJCAI: Workshop on Explainable Artificial Intelligence (XAI).

Title 
Resource Constrained AI 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
B.Tech 4^{th} Year and M.Tech., PhD 
Type 
Elective 
Prerequisite 
Machine Learning 


Objectives The Instructor will: 1. Explain the challenges of implementing AI and machine learning algorithms on devices with memory and power constraints 2. Provide methods to reduce computational complexity of AI techniques
Learning Outcomes The students are expected to have the ability to: 1. Understand the constraints of implementing AI algorithms on limited memory devices 2. Design and develop techniques to reduce inference time memory footprint of machine learning models
Contents
Introduction: Overview and motivation, challenges of resource constrained AI, why AI on edge (4 lectures) Edge Computing: Edge devices and their limitations, Edge and fog computing, Distributed computing, communication links, communication overhead in IoT devices (8 lectures) Monitoring: Prediction accuracy, numeric accuracy, precision, memory footprints, computational complexity of AI models (4 lectures)
Memory Optimization of Models: KiloBytesize models, floatingpoint v/s fixedpoint, SeeDot (8 lectures) Edge AI: Resourceefficient kNN, SVM and deep learning models, Toeplitz matrix, Bonsai, ProtoNN, EMIRNN, FastRNN, FastGRNN (10 lectures)
Current Trends and Future: Hardware accelerators for Edge AI, Vision Processing Unit (VPU), Streaming Hybrid Architecture Vector Engine (SHAVE), Intel’s Movidius Neural Compute Stick (NCS), Open Neural Network Exchange (ONNX), Future trends (10 lectures)
Laboratory Experiments
Implementation of Bonsai, CNN training using SeeDot language etc.
Textbook 1. Alippi, C., (2014), Intelligence for Embedded Systems: A Methodological Approach, Springer.
Preparatory Course Material 1. EdgeML by Microsoft, https://github.com/Microsoft/EdgeML/#edgemachinelearning 2. NCSDK by Intel https://github.com/movidius/ncsdk 
Title 
AdHoc Wireless Networks 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP[C] 
3–0–0 [3] 
Offered for 
M.Tech. 1^{st} Year, PhD 1^{st} Year 
Type 

Prerequisite 
Networks 


Objectives The Instructor will: 1. Introduce the mathematical models and network protocol designs in wireless Adhoc networks 2. Provide a systematic exposition of network protocols and their cross‐layer interactions 3. To provide more advanced indepth networking knowledge. Upon completion of this course, students will be able to apply the knowledge in their networking research. A broad perspective on the active research areas in wireless Adhoc networks Learning Outcomes The students are expected to have the ability to: 1. Demonstrate advanced knowledge of networking and wireless networking in particular 2. Compare different solutions for communications at each network layer 3. Demonstrate knowledge of protocols used in wireless communications Contents Basics of wireless networks and mobile computing: Ad hoc Networks: Introduction, Issues in Ad hoc wireless networks, Ad hoc wireless internet (3 lectures) Media access control in ad hoc and sensor networks: MAC Protocols for Ad hoc Wireless Networks: Introduction, Issues in designing a MAC protocol for Ad hoc Wireless Networks, Design goals of a MAC protocol for Ad hoc Wireless Networks, Classification of MAC protocols, Contention based protocols with reservation mechanisms. Contentionbased MAC protocols with Scheduling mechanism, MAC protocols that use directional antennas, Other MAC protocols, Network and transport layer issues for ad hoc and sensor networks (8 lectures) Routing protocols for Ad hoc Wireless Networks: Introduction, Issues in designing a routing protocol for Ad hoc Wireless Networks, Classification of routing protocols, Table drive routing protocol, Ondemand routing protocol, Hybrid routing protocol, Routing protocols with effective flooding mechanisms, Hierarchical routing protocols, Power aware routing protocols (8 lectures) Transport layer protocols: Transport layer protocols for Ad hoc Wireless Networks: Introduction, Issues in designing a transport layer protocol for Ad hoc Wireless Networks, Design goals of a transport layer protocol for Ad hoc Wireless Networks, Classification of transport layer solutions, TCP over Ad hoc Wireless Networks, Other transport layer protocols for Ad hoc Wireless Networks (8 lectures) Security issues for ad hoc networks: Security: Security in wireless Ad hoc Wireless Networks, Network security requirements, Issues & challenges in security provisioning, Network security attacks, Key management, Secure routing in Ad hoc Wireless Networks (6 lectures) QoS for ad hoc Networks: Quality of service in Ad hoc Wireless Networks: Introduction, Issues and challenges in providing QoS in Ad hoc Wireless Networks, Classification of QoS solutions, MAC layer solutions, network layer solutions (3 lectures) Advanced Topics: Softwaredefined network (SDN), Mesh networking, Energy issues and Sensor networks (6 lectures) Laboratory Experiments Programming exercises using NS2/NS3, QualNet, Java and OmNet++ Textbook 1.Siva Ram Murthy, C., & Manoj, B. S. (2015). Ad hoc wireless networks: Architectures and protocols. PHI Pearson Education 2. Akyildiz, Ian F., and Xudong Wang(2015). Wireless mesh networks. Vol. 3. John Wiley & Sons Reference Books 1. Basagni, S., Conti, M., Giordano, S., & Stojmenovic, I. (Eds.). (2015). Mobile ad hoc networking. John Wiley & Sons2. Perkins, C. E. (2001). Ad hoc networking (Vol. 1). Reading: Addisonwesley3. Toh, C. K. (2001). Ad hoc mobile wireless networks: protocols and systems. Pearson Education 4. Cheng, X., Huang, X., & Du, D. Z. (Eds.). (2013). Ad hoc wireless networking (Vol. 14). Springer Science & Business Media Self Learning Material 1. 1. Computer Networks  MIT OpenCourseWare 2. Mobile and Wireless Networks and Applications, Stanford University, https://web.stanford.edu/class/cs444n/

Title 
Vehicular AdHoc Networks(VANETs) 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
M.Tech. 
Type 
Elective 
Prerequisite 
Network 


Objectives The Instructor will: 1. Introduce the emerging technologies, standards and applications in vehicular communication systems. 2. Provide the design considerations and challenges of vehicletoinfrastructure and vehicletovehicle communications. Theories such as vehicular mobility modeling, and vehicular technologies and standards from the physical to network layers will be introduced in the course. Examples of emerging applications of vehicular communications in Intelligent Transportation Systems will also be studied and discussed.
Learning Outcomes The students are expected to have the ability to: 1. Understand and describe the basic theories and principles, technologies, standards, and system architecture of vehicular adhoc networks (VANET) or intervehicle communication networks. 2. Analyze, design, and evaluate vehicular communication platforms for various kinds of safety and infotainment applications. Contents Introduction: Basic principles and challenges, past and ongoing VANET activities (2 Lectures) Cooperative Vehicular Safety Applications: Enabling technologies, cooperative system architecture, safety applications (2 lectures) Vehicular Mobility Modeling: Random models, flow and traffic models, behavioral models, trace and survey based models, joint transport and communication simulations (4 lectures) Physical Layer Considerations for Vehicular Communications: Signal propagation, Doppler spread and its impact on OFDM systems (4 lectures) MAC Layer of Vehicular Communication Networks: Proposed MAC approaches and standards, IEEE 802.11p (8 lectures) VANET Routing protocols: Opportunistic packet forwarding, topologybased routing, geographic routing (8 lectures) Emerging VANET Applications: Limitations, example applications, communication paradigms, message coding and composition, data aggregation (8 lectures) Standards and Regulations: Regulations and Standards, DSRC Protocol Stack, Cellular V2X (6 lectures) Laboratory Experiments Programming exercises using NS3, QualNet and Java Textbook 1.Olariu, S., & Weigle, M. C. (2017). Vehicular networks: from theory to practice. Chapman and Hall/CRC 2. Murthy, C. S. R. (2006). Ad hoc wireless networks: Architectures and protocols. Pearson Education India Reference Books 1. Emmelmann, M., Bochow, B., & Kellum, C. (Eds.). (2010). Vehicular networking: Automotive applications and beyond (Vol. 2). John Wiley & Sons 2. Claudia Campolo , Antonella Molinaro, Riccardo Scopigno(2015). Vehicular ad hoc Networks, Springer3. Hartenstein, H., & Laberteaux, K. (2010). VANET: vehicular applications and internetworking technologies (Vol. 1). Chichester: Wiley4. Sommer, C., & Dressler, F. (2015). Vehicular networking. Cambridge University Press5. Moustafa, H., & Zhang, Y. (2009). Vehicular networks: techniques, standards, and applications. Auerbach publications Self Learning Material 1. Center for Autonomous Intelligent Networks and Systems (CAINS), University of California, Los Angeles (UCLA),http://www.cains.cs.ucla.edu/

Title 
Selected Topics in Computer Science  I 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
1–0–0 [1] 
Offered for 
M.Tech., PhD 
Type 
Elective 
Prerequisite 
Decided by the instructor 


Objectives The Instructor will: 1. Expose the students to the latest upcoming fields in the area of computer science
Learning Outcomes The students are expected to have the ability to: 1. Apply the knowledge of recent topics to specific research areas in the field of computer science
Contents The topic clouds for the course include contemporary topics in computer science and may be updated according to the instructor.
Textbook Relevant Textbook and/or research papers to be announced by the instructor.
SelfLearning Material Relevant Textbook and/or research papers to be announced by the instructor.
Preparatory Course Material Relevant Textbook and/or research papers to be announced by the instructor. 
Title 
Selected Topics in Computer Science  II 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
2–0–0 [2] 
Offered for 
M.Tech., PhD 
Type 
Elective 
Prerequisite 
Decided by the instructor 


Objectives The Instructor will: 1. Expose the students to the latest upcoming fields in the area of computer science
Learning Outcomes The students are expected to have the ability to: 1. Apply the knowledge of recent topics to specific research areas in the field of computer science
Contents The topic clouds for the course include contemporary topics in computer science and may be updated according to the instructor.
Textbook Relevant Textbook and/or research papers to be announced by the instructor.
SelfLearning Material Relevant Textbook and/or research papers to be announced by the instructor.
Preparatory Course Material Relevant Textbook and/or research papers to be announced by the instructor. 
Title 
Selected Topics in Computer Science  III 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
M.Tech., PhD 
Type 
Elective 
Prerequisite 
Decided by the instructor 


Objectives The Instructor will: 1. Expose the students to the latest upcoming fields in the area of computer science
Learning Outcomes The students are expected to have the ability to: 1. Apply the knowledge of recent topics to specific research areas in the field of computer science
Contents The topic clouds for the course include contemporary topics in computer science and may be updated according to the instructor.
Textbook Relevant Textbook and/or research papers to be announced by the instructor.
SelfLearning Material Relevant Textbook and/or research papers to be announced by the instructor.
Preparatory Course Material Relevant Textbook and/or research papers to be announced by the instructor. 
Title 
Selected Topics in Artificial Intelligence  I 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
1–0–0 [1] 
Offered for 
M.Tech., PhD 
Type 
Elective 
Prerequisite 
Decided by the instructor 


Objectives The Instructor will: 1. Expose the students to the latest upcoming fields in the area of artificial intelligence
Learning Outcomes The students are expected to have the ability to: 1. Apply the knowledge of recent topics to specific research areas in the field of artificial intelligence
Contents The topic clouds for the course include contemporary topics in artificial intelligence and may be updated according to the instructor.
Textbook Relevant Textbook and/or research papers to be announced by the instructor.
SelfLearning Material Relevant Textbook and/or research papers to be announced by the instructor.
Preparatory Course Material Relevant Textbook and/or research papers to be announced by the instructor. 
Title 
Selected Topics in Artificial Intelligence  II 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
2–0–0 [2] 
Offered for 
M.Tech., PhD 
Type 
Elective 
Prerequisite 
Decided by the instructor 


Objectives The Instructor will: 1. Expose the students to the latest upcoming fields in the area of artificial intelligence
Learning Outcomes The students are expected to have the ability to: 1. Apply the knowledge of recent topics to specific research areas in the field of artificial intelligence
Contents The topic clouds for the course include contemporary topics in artificial intelligence and may be updated according to the instructor.
Textbook Relevant Textbook and/or research papers to be announced by the instructor.
SelfLearning Material Relevant Textbook and/or research papers to be announced by the instructor.
Preparatory Course Material Relevant Textbook and/or research papers to be announced by the instructor. 
Title 
Selected Topics in Artificial Intelligence  III 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
M.Tech., PhD 
Type 
Elective 
Prerequisite 
Decided by the instructor 


Objectives The Instructor will: 1. Expose the students to the latest upcoming fields in the area of artificial intelligence
Learning Outcomes The students are expected to have the ability to: 1. Apply the knowledge of recent topics to specific research areas in the field of artificial intelligence
Contents The topic clouds for the course include contemporary topics in artificial intelligence and may be updated according to the instructor.
Textbook Relevant Textbook and/or research papers to be announced by the instructor.
SelfLearning Material Relevant Textbook and/or research papers to be announced by the instructor.
Preparatory Course Material Relevant Textbook and/or research papers to be announced by the instructor. 
Title 
Graph Theory and Applications 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
M.Tech. 1^{st} Year, Ph.D. 1^{st} Year 
Type 
Elective 
Prerequisite 
None 


Objectives The Instructor will: 1. Introduce various terminologies, concepts and algorithms related to graphs, and discuss their applications in realworld scenarios.
Learning Outcomes The students are expected to have the ability to: 1. Formulate and solve realworld problems using the mathematical foundations of graph theory.
Contents Preliminaries: Graphs, Isomorphism, Subgraphs, Matrix representations, Degree, Operations on graphs, Degree sequences (3 lectures) Connected graphs and shortest paths: Walks, Trails, Paths, Connected graphs, Distance, Cutvertices, Cutedges, Blocks, Connectivity, Weighted graphs, Shortest path algorithms (4 lectures) Trees: Characterizations, Number of trees, Minimum spanning trees (3 lectures) Special classes of graphs: Bipartite graphs, Line graphs, Chordal graphs (2 lectures) Eulerian graphs: Characterization, Fleury’s algorithm, Chinesepostmanproblem (2 lectures) Hamilton graphs: Necessary conditions and sufficient conditions (3 lectures) Independent sets, coverings, matchings: Basic equations, Matchings in bipartite graphs, Perfect matchings, Greedy and approximation algorithms (6 lectures) Vertex colorings: Chromatic number and cliques, Greedy coloring algorithm, Coloring of chordal graphs, Brook’s theorem (2 lectures) Edge colorings: GuptaVizing theorem, Class1 graphs and Class2 graphs, Equitable edgecoloring (5 lectures) Planar graphs: Basic concepts, Euler’s formula, Polyhedrons and planar graphs, Characterizations, Planarity testing, 5colortheorem (3 lectures) Directed graphs: Outdegree, Indegree, Connectivity, Orientation, Eulerian directed graphs, Hamilton directed graphs (5 lectures) Applications: Biology, Social Sciences, Engineering, Computer Science (4 lectures)
Textbook 1.West,D.B., (2002), Introduction to Graph Theory, 2^{nd} Edition, Prentice Hall of India 2.Deo,N., (2003), Graph Theory: With Application to Engineering and Computer Science, Prentice Hall of India
Reference Books 1. Research literature
Self Learning Material 1. NPTEL: Graph Theory (for CSE), https://nptel.ac.in/courses/106108054/39 2. NPTEL: Graph Theory (for Mathematics), https://nptel.ac.in/courses/111106050/

Title 
Social Network Analysis 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
M.Tech. 1^{st} Year, Ph.D. 1^{st} Year 
Type 
Elective 
Prerequisite 
None 


Objectives The Instructor will: 1. Introduce the social networks and the research areas therein. 2. Provide with the mathematical foundation required for social network analysis 3. Cover various concepts, terminologies and algorithms related to social network analysis 4. Conduct tutorial sessions to use NetworkX library in Python for network analysis Learning Outcomes The students are expected to have the ability to: 1. Understand the applications related to social networks 2. Write program with social network datasets in Python 3. Formulate realworld problems with any relational data set resembling social networks
Contents Introduction and Different Types of Networks (1 Lecture) Graph Introduction: Adjacency Matrix, Paths, Connectivity, Incidence Matrix, Distance, BreadthFirstSearch, Directed Graph (1 Lecture) Introduction to Python and NetworkX (1) Network Measures, Centrality, Core, Cliques and Clan, Strong and Weak Ties, Homophily, Structural Balance, Components (4 Lectures) Network Data Sets and Structural Analysis in Python+NetworkX+Pandas (2) Network Models: Random Networks, Scale Free Networks, The BarabásiAlbert Model, FuzzyGranular Social Network (4 Lectures) Generate Synthetic Networks, Using Network Models in Python+NetworkX (2) Game Theory Introduction, Modeling Network Traffic using Games (3 Lectures) Information Cascades, SmallWorld Phenomenon, Epidemics (4 Lectures) Implementing Information Diffusion Algorithms in Python+NetworkX (2) Community Detection (3 Lectures) Implementing Community Detection Algorithms in Python+NetworkX (2) Link Prediction (2 Lectures) Implementing Link Prediction Algorithms in Python+NetworkX (2) Evolving Network and Temporal Networks (2 Lectures) Working with Temporal Network Data (2) Connected Caveman Problem, Link Analysis and Web Search (2 Lectures) Implementing PageRank algorithm in Python+NetworkX (1) Network Data Science Stateoftheart (2 Lectures) Textbook 1. Networks, Crowds, and Markets: Reasoning About a Highly Connected World, by David Easley and Jon Kleinberg, (Cambridge University Press  Sep 2010)  Prepublication draft available online. 2. Network Science, by AlbertLaszlo Barabasi, (Cambridge University Press  August 2016)  freely available under the Creative Commons licence. 3. Networks, by Mark Newman, (Oxford University Press, 2ndedition  Sep 2018) Reference Books 1. Complex and Adaptive Dynamical Systems, by Claudius
Gros, (Springer, 4th Edition  2015). 2. The Structure of Complex Networks Theory and
Applications, by Ernesto Estrada, (Oxford University Press  Dec 2011). 3. Exploratory Social Network Analysis with Pajek, by Wouter de Nooy, Andrej Mrvar, and Vladimir Batagelj, (Cambridge University Press, 3rd Edition  July 2018)
Self Learning Material 1. https://www.barabasilab.com/course 2. https://nptel.ac.in/courses/106106169/#

Title 
Blockchain 
Number 
CSL8XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 
M.Tech. 
Type 
Elective 
Prerequisite 
Security 


Objectives The Instructor will: 1. Explain how blockchain technology works 2. Integrate blockchain technology into the current business processes to make them Secure
Learning Outcomes The students are expected to have the ability to:
1. Understand what and why of Blockchain 2. Explore the major components of Blockchain and Identify a use case for a Blockchain application 3. Create your own Blockchain network application
Contents
Introduction to Blockchain: Digital Trust, Asset, Transactions, Distributed Ledger Technology, Types of network, Components of blockchain (cryptography, ledgers, consensus, smart contracts) (5 lectures) PKI and Cryptography: Private keys, Public keys, Hashing, Digital Signature (3 lectures) Consensus: Byzantine Fault, Proof of Work, Poof of Stake (4 lectures) Cryptocurrency: Bitcoin creation and economy, Limited Supply and Deflation, Hacks, Ethereum concept and Ethereum classic, Hacks Why it is so revolutionary – both (8 lectures) Hyperledger Fabric: Hyperledger Architecture, Membership, Blockchain, Transaction, Chaincode, Hyperledger Fabric, Features of Hyperledger, Fabric Demo (4 lectures) Blockchain Applications: Building on the Blockchain, Ethereum Interaction  Smart Contract and Token (Fungible, nonfungible), Languages, How you would go about creating your own blockchain, Blockchainasaservice (4 lectures) Blockchain Use Cases: Finance, Industry and Blockchain in Government (4 lectures) Security and Research Aspects: Blockchain Security (DDos), Research Aspects in Blockchain, AI, Blockckahin and Big Data (4 lectures)
Laboratory Experiments
Textbook 1. Bahga, A., & Madisetti, V. (2017). Blockchain Applications: A HandsOn Approach. VPT.
Self Learning Material 1. Swan, M. (2015). Blockchain: Blueprint for a new economy. " O'Reilly Media, Inc.". 2. Wattenhofer, Roger. The science of the blockchain. CreateSpace Independent Publishing Platform, 2016. 3. Bashir, I. (2017). Mastering blockchain. Packt Publishing Ltd. 4. Levy, K. E. (2017). Booksmart, not streetsmart: blockchainbased smart contracts and the social workings of law. Engaging Science, Technology, and Society, 3, 115.
Preparatory Course Material 1. MIT Online Blockchain Course, Learn Blockchain Technology, https://getsmarter.mit.edu/

Title 
Computational Optimization 
Number 
CSL7XX0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 

Type 
Elective 
Prerequisite 



Objectives The Instructor will: 1. Introduce various terminologies, concepts and algorithms related to classical, heuristic and nature inspired optimization algorithms 2. Discuss their applications in realworld scenarios
Learning Outcomes The students are expected to have the ability to: 1. Utilize state of the art heuristic optimization algorithms in their research activities 2. Design and propose new and hybrid optimization algorithms 3. Customize heuristic optimization algorithms for special applications
Contents Introduction, Definitions and Concepts: Optimization, Operational Research (OR), Engineering Optimization, Definition of an Optimization Problem, Feasibility Problem, Classification of Optimization Problems, Classification of Optimization Techniques, Heuristic Algorithms vs. Metaheuristics, Swarm Intelligence, PopulationBased Optimization, Multiobjective Optimization, Parallelization, Evaluation of the Optimization Algorithms (6 lectures) Overview of Classical Optimization Techniques: Linear programming, Nonlinear Programming (3 lectures) Overview of Heuristic Optimization Algorithms: Neighborhood Search, Hill Climbing Methods, Greedy Algorithms, Simulated Annealing (3 lectures) Overview of Nature Inspired Optimization Algorithms: Evolutionary Algorithms, Tabu Search, Ant Colony Optimization, Particle Swarm Optimization (2 lectures) Simulated Annealing: Real Annealing and Simulated Annealing, Metropolis Algorithm, Simulated Annealing Algorithm, Continuous Simulated Annealing, Oneloop Simulated Annealing, Temperature Scheduling, Convergence of Simulated Annealing, Applications, Normalization of the Parameters, Tuning the Parameters of an algorithm (9 lectures) Evolutionary Algorithms: Methods of encoding, Operators of Evolution, Models of Evolution, Genetic Algorithms, Steady State Genetic Algorithms, Genetic Programming, Memetic Algorithms, Differential Evolution (7 lectures) Tabu Search: Basic Tabu Search, Shortterm Memory, Longterm Memory, Diversification and Intensification, Continuous Tabu Search (4 lectures) Ant Colony Optimization: Collective Behavior of Social Insects, Basic ACO Algorithms, Ant Algorithms for TSP, Adaptation to Continuous Problems, Applications (5 lectures) Particle Swarm Optimization: Canonical PSO Algorithm, Important Parameters, Neighborhood Topologies (3 lectures)
Textbook 1.Michalewicz,Z. and Fogel,D.B., (2004), How to Solve it: Modern Heuristics, 2^{nd} Edition, Springer 2.Simon, D., (2013), Evolutionary Optimization Algorithms, Wiley 3.Yang,X.S., (2014), Natureinspired Metaheuristic Algorithms, Luniver Press
Reference Books 1. Rao, S.S., (2013), Engineering Optimization: Theory and Practice, 3^{rd} Edition, New Age International Publishers
Self Learning Material 1. NPTEL: Traditional and Nontraditional Optimization Tools https://nptel.ac.in/courses/112105235/1

Title 
Computer Graphics 
Number 
CSL7xx0 
Department 
Computer Science and Engineering 
LTP [C] 
3–0–0 [3] 
Offered for 

Type 
Elective 
Prerequisite 



Objectives The Instructor will: 1. Provide a thorough introduction to computer graphics techniques, focusing on 2D and 3D modelling, image synthesis and rendering
Learning Outcomes The students are expected to have the ability to: 1. Explain and create interactive graphics application 2. Implement graphics primitives 3. Synthesize and render images for animation and visualization
Contents CSL7xx1 Introduction to Graphical Primitives 100 [1] Introduction: Overview of computer graphics, representing pictures, preparing, presenting & interacting with pictures for presentations; Scan conversion: 2D Geometric Primitives; Area Filling algorithms. Clipping algorithms, Anti Aliasing Transformations and viewing: 2D and 3D transformations, Matrix representations & homogeneous coordinates, Viewing pipeline, Window to viewport coordinate transformation, clipping operations, viewport clipping, 3D viewing.
CSL7xx2 Graphical Object Representation 100 [1] Curves and Surfaces: Conics, parametric and nonparametric forms; Bezier (Bernstein Polynomials) Curves, CubicSplines, BSplines; Quadratic surfaces, Bezier surfaces and NURBS, 3D modelling.
CSL7xx3 Graphics Rendering 100 [1] Hidden surfaces: Depth comparison, Zbuffer algorithm, Back face detection, BSP tree method, the Printer’s algorithm, scanline algorithm; Hidden line elimination, wire frame methods, fractal  geometry. Color & shading models: Phong's shading model, Gouraud shading, Shadows and background, Color models, Photorealistic rendering Animation and OpenGL primitives: Functions, pipeline, sample programs for drawing 2D, 3D objects; event handling and view manipulation, Introduction to GPU and animation
Textbook 1. J. D. Foley, A. Van Dam, S. K. Feiner and J. F. Hughes, Computer Graphics; Principles and practice, Addison Wesley, 2nd Edition in C, 1997. 2. D. F. Rogers and J. A. Adams, Mathematical elements for Computer Graphics, McGrawHill, 2nd Edition, 1990.
SelfLearning Material 1. Blender: https://www.blender.org/download/ 2. OpenGL: http://www.opengltutorial.org/
Preparatory Course Material 1.Department of Computer Science and Engineering, Indian Institute of Technology Madras, https://nptel.ac.in/courses/106106090/
