Database Management Systems

CMSACOR10T: Database Management Systems Theory: 60 Lectures 1. Introduction (6 Lectures) Characteristics of database approach, data models, database system architecture and data
independence. 2. Entity Relationship(ER) Modeling (8 Lectures) Entity types, relationships, constraints. 3. Relation data model (20 Lectures) Relational model concepts, relational constraints, relational algebra, SQLqueries 4. Database design (15 Lectures) Mapping ER/EER model to relational database, functional dependencies,Lossless decomposition, Normal forms (upto BCNF). 5. Transaction Processing (3 Lectures) ACID properties, concurrency control 6. File Structure and Indexing (8 Lectures) Operations on files, File of Unordered and ordered records, overview of File organizations, Indexing structures for files( Primary index, secondary index, clustering index), Multilevel indexing using B and B+ trees.

Internet Technologies

Java (5 lectures) Use of Objects, Array and ArrayList class JavaScript (15 lectures) Data types, operators, functions, control structures, events and event handling. JDBC (10 lectures) JDBC Fundamentals, Establishing Connectivity and working with connection interface, Working with statements, Creating and Executing SQL Statements, Working with Result Set Objects. JSP (20 lectures) Introduction to JavaServer Pages, HTTP and Servlet Basics, The Problem with Servlets, The Anatomy of a JSP Page, JSP Processing, JSP Application Design with MVC, Setting Up the JSP Environment, Implicit JSP Objects, Conditional Processing, Displaying Values, Using an expression to Set an Attribute, Declaring Variables and Methods, Error Handling and Debugging, Sharing Data Between JSP Pages, Requests, and Users, Database Access. Java Beans (10 lectures) Java Beans Fundamentals, JAR files, Introspection, Developing a simple Bean, Connecting to DB

Theory of Computation

CMSACOR12T: Theory of Computation Theory: 75 Lectures AdditionalTutorial: 15 Lectures
1. Languages
(10 Lectures)
Alphabets, string, language, Basic Operations on language, Concatenation, Kleene Star 2. Finite Automata and Regular Languages (25 Lectures) Regular Expressions, Transition Graphs, Deterministics and non-deterministic finite automata, NFA to DFA Conversion, Regular languages and their relationship with finite automata, Pumping lemma and closure properties of regular languages. 3. Context free languages (20 Lectures) Context free grammars, parse trees, ambiguities in grammars and languages, Pushdown automata (Deterministic and Non-deterministic), Pumping Lemma, Properties of context free languages, normal forms. 4. Turing Machines and Models of Computations (20 Lectures) RAM, Turing Machine as a model of computation, Universal Turing Machine, Language acceptability, decidability, halting problem, Recursively enumerable and recursive languages, unsolvability problems.

Artificial Intelligence

CMSACOR13T: Artificial Intelligence Theory: 60 Lectures 1. Introduction (06 Lectures) Introduction to Artificial Intelligence, Background and Applications, Turing Test and Rational Agent approaches to AI, Introduction to Intelligent Agents, their structure, behavior and environment.
2. Problem Solving and Searching Techniques (20 Lectures) Problem Characteristics, Production Systems, Control Strategies, Breadth First Search, Depth First Search, Hill climbing and its Variations, Heuristics Search Techniques: Best First Search, A* algorithm, Constraint Satisfaction Problem, Means-End Analysis, Introduction to Game Playing, Min-Max and Alpha-Beta pruning algorithms.
3. Knowledge Representation (20 Lectures) Introduction to First Order Predicate Logic, Resolution Principle, Unification, Semantic Nets, Conceptual Dependencies, Frames, and Scripts, Production Rules, Conceptual Graphs. Programming in Logic (PROLOG)
4. Dealing with Uncertainty and Inconsistencies (08 Lectures) Truth Maintenance System, Default Reasoning, Probabilistic Reasoning, Bayesian Probabilistic Inference, Possible World Representations.
5. Understanding Natural Languages (06 Lectures) Parsing Techniques, Context-Free and Transformational Grammars, Recursive and Augmented Transition Nets.

Computer Graphics

CMSACOR14T: Computer Graphics Theory: 75 Lectures
1. Introduction (5 Lectures) Basic elements of Computer graphics, Applications of Computer Graphics.
2. Graphics Hardware (8 Lectures) Architecture of Raster and Random scan display devices, input/output devices.
3. Fundamental Techniques in Graphics (22 Lectures) Raster scan line, circle and ellipse drawing, thick primitives, Polygon filling, line and polygon clipping algorithms, 2D and 3D Geometric Transformations, 2D and 3D Viewing Transformations (Projections- Parallel and Perspective), Vanishing points.
4. Geometric Modeling (10 Lectures)
Representing curves & Surfaces.
5.Visible Surface determination (8 Lectures) Hidden surface elimination.
6.Surface rendering (7 Lectures) Illumination and shading models. Basic color models and Computer Animation.

Microprocessor

CSMADSE01T:Microprocessor Theory: 60 Lectures Microprocessor architecture: Internal architecture, system bus architecture, memory and I/Ointerfaces. Microprocessor programming: Register Organization, instruction formats, assembly languageprogramming. Interfacing: Memory address decoding, cache memory and cache controllers, I/O interface,keyboard, display, timer, interrupt controller, DMA controller, video controllers, communication interfaces.

Data Mining

CSMADSE02T: Data Mining Theory: 60 lectures
Overview: Predictive and descriptive data mining techniques, supervised and unsupervisedlearning techniques, process of knowledge discovery in databases, pre-processing methods
Data Mining Techniques: Association Rule Mining, classification and regressiontechniques, clustering, Scalability and data management issues in data mining algorithms, measures of interestingness

Cloud Computing

Cloud Computing
Overview of Computing Paradigm ( 8 lectures)
Recent trends in Computing : Grid Computing, Cluster Computing, Distributed Computing, Utility Computing, Cloud Computing,
Introduction to Cloud Computing ( 7 lectures) Introduction to Cloud Computing, History of Cloud Computing, Cloud service providers, Benefits and limitations of Cloud Computing,
Cloud Computing Architecture ( 20 lectures) Comparison with traditional computing architecture (client/server), Services provided at various levels, Service Models- Infrastructure as a Service(IaaS), Platform as a Service(PaaS), Software as a Service(SaaS), How Cloud Computing Works, Deployment
Models- Public cloud, Private cloud, Hybrid cloud, Community cloud, Case study of NIST architecture.
Case Studies ( 13 lectures) Case study of Service model using Google App Engine, Microsoft Azure, Amazon EC2 , Eucalyptus. Service Management in Cloud Computing ( 7 lectures) Service Level Agreements(SLAs), Billing & Accounting, Comparing Scaling Hardware: Traditional vs. Cloud, Economics of scaling. Cloud Security ( 5 lectures) Infrastructure Security- Network level security, Host level security, Application level security, Data security and Storage- Data privacy and security Issues, Jurisdictional issues raised by Data location, Authentication in cloud computing.

Big Data

Big Data Theory: 60 lectures
UNDERSTANDING BIG DATA
What is big data – why big data –.Data!, Data Storage and Analysis, Comparison with Other Systems, Rational Database Management System , Grid Computing, Volunteer Computing, convergence of key trends – unstructured data – industry examples of big data – web analytics – big data and marketing – fraud and big data – risk and big data – credit risk management – big data and algorithmic trading – big data and healthcare – big data in medicine – advertising and big data – big data technologies – introduction to Hadoop – open source technologies – cloud and big data – mobile business intelligence – Crowd sourcing analytics – inter and trans firewall analytics.
NOSQL DATA MANAGEMENT
Introduction to NoSQL – aggregate data models – aggregates – key-value and document data models – relationships – graph databases – schema less databases – materialized views – distribution models – shading –– version – map reduce – partitioning and combining – composing map-reduce calculations.
BASICS OF HADOOP Data format – analyzing data with Hadoop – scaling out – Hadoop streaming – Hadoop pipes – design of Hadoop distributed file system (HDFS) – HDFS concepts – Java interface – data flow – Hadoop I/O – data integrity – compression – serialization – Avro – file-based data structures.
MAPREDUCE APPLICATIONS MapReduce workflows – unit tests with MRUnit – test data and local tests – anatomy of MapReduce job run – classic Map-reduce – YARN – failures in classic Map-reduce and YARN – job scheduling – shuffle and sort – task execution – MapReduce types – input formats – output formats
HADOOP RELATED TOOLS Hbase – data model and implementations – Hbase clients – Hbase examples –praxis. Cassandra – Cassandra data model – Cassandra examples – Cassandra clients –Hadoop integration. Pig – Grunt – pig data model – Pig Latin – developing and testing Pig Latin scripts. Hive – data types and file formats – HiveQL data definition – HiveQL data manipulation – HiveQL queries.

Digital Image Processing

CMSADSE05T: Digital Image Processing Lab Theory: 60 Lectures
1. Introduction (6 Lectures)
Light, Brightness adaption and discrimination, Pixels, coordinate conventions, Imaging Geometry, Perspective Projection, Spatial Domain Filtering, sampling and quantization.
2. Spatial Domain Filtering (7 Lectures) Intensity transformations, contrast stretching, histogram equalization, Correlation and convolution, Smoothing filters, sharpening filters, gradient and Laplacian.
3. Filtering in the Frequency domain (8 Lectures) Hotelling Transform, Fourier Transforms and properties, FFT (Decimation in Frequency and Decimation in Time Techniques), Convolution, Correlation, 2-D sampling, Discrete Cosine Transform, Frequency domain filtering.
4. Image Restoration (8 Lectures) Basic Framework, Interactive Restoration, Image deformation and geometric transformations, image morphing, Restoration techniques, Noise characterization, Noise restoration filters,
Adaptive filters, Linear, Position invariant degradations, Estimation of Degradation functions, Restoration from projections.
5. Image Compression (10 Lectures) Encoder-Decoder model, Types of redundancies, Lossy and Lossless compression, Entropy of an information source, Shannon's 1st Theorem, Huffman Coding, Arithmetic Coding, Golomb Coding, LZW coding, Transform Coding, Sub-image size selection, blocking artifacts, DCT implementation using FFT, Run length coding, FAX compression (CCITT Group-3 and Group-4), Symbol-based coding, JBIG-2, Bit-plane encoding, Bit-allocation, Zonal Coding, Threshold Coding, JPEG, Lossless predictive coding, Lossy predictive coding, Motion Compensation
6. Wavelet based Image Compression (5 Lectures) Expansion of functions, Multi-resolution analysis, Scaling functions, MRA refinement equation, Wavelet series expansion, Discrete Wavelet Transform (DWT), Continuous Wavelet Transform, Fast Wavelet Transform, 2-D wavelet Transform, JPEG-2000 encoding, Digital Image Watermarking.
7. Morphological Image Processing (7 Lectures) Basics, SE, Erosion, Dilation, Opening, Closing, Hit-or-Miss Transform, Boundary Detection, Hole filling, Connected components, convex hull, thinning, thickening, skeletons, pruning, Geodesic Dilation, Erosion, Reconstruction by dilation and erosion.
8. Image Segmentation (9 Lectures) Boundary detection based techniques, Point, line detection, Edge detection, Edge linking, local processing, regional processing, Hough transform, Thresholding, Iterative thresholding, Otsu's method, Moving averages, Multivariable thresholding, Region-based segmentation, Watershed algorithm, Use of motion in segmentation