Computer Networks

1. Introduction to Computer Networks (8 Lectures) Network definition; network topologies; network classifications; network protocol; layered network architecture; overview of OSI reference model; overview of TCP/IP protocol suite. 2. Data Communication Fundamentals and Techniques (10 Lectures) Analog and digital signal; data-ratelimits; digital to digital line encoding schemes; pulse code modulation; parallel and serial transmission; digital to analog modulation-; multiplexing techniques- FDM, TDM; transmission media.
3. Networks Switching Techniques and Access mechanisms (10 Lectures) Circuit switching; packet switching- connectionless datagram switching, connection-oriented virtual circuit switching; dial-up modems; digital subscriber line; cable TV for data transfer. 4. Data Link Layer Functions and Protocol (10 Lectures) Error detection and error correction techniques; data-link control- framing and flow control; error recovery protocols- stop and wait ARQ, go-back-n ARQ; Point to Point Protocol on Internet. 5. Multiple Access Protocol and Networks (5 Lectures) CSMA/CD protocols; Ethernet LANS; connecting LAN and back-bone networks- repeaters, hubs, switches, bridges, router and gateways; 6. Networks Layer Functions and Protocols (6 Lectures) Routing; routing algorithms; network layer protocolof Internet- IP protocol, Internet control protocols. 7. Transport Layer Functions and Protocols (6 Lectures) Transport services- error and flow control, Connection establishment and release- three way handshake; 8. Overview of Application layer protocol (5 Lectures) Overview of DNS protocol; overview of WWW &HTTP protocol.

Design and Analysis of Algorithms

CMSACOR08T: Design and Analysis of Algorithms Lab Theory: 60 Lectures 

1. Introduction (5 Lectures) 

Basic Design and Analysis techniques of Algorithms, 

orrectness of Algorithm.

 2. Algorithm Design Techniques (8 Lectures) 

Iterative techniques, 

Divide and Conquer, 

Dynamic Programming, 

Greedy Algorithms. 

3. Sorting and Searching Techniques (20 Lectures) 

Elementary sorting techniques–

Bubble Sort -   CLICK HERE

Insertion Sort -  CLICK HERE 

Merge Sort - CLICK HERE

Advanced Sorting techniques -:

 Heap Sort -  CLICK HERE 

Quick Sort -   CLICK HERE

Sorting in Linear Time -: 

Bucket Sort - 

Radix Sort - CLICK HERE

and Count Sort, 

Searching Techniques:  i) LINEAR_SEARCH -- CLICK

                                      ii)  BINARY_SEARCH --CLICK

Medians & Order Statistics, complexity analysis; 

4. Lower Bounding Techniques (5 Lectures)

 Decision Trees 

5. Balanced Trees (7 Lectures) 

Red-Black Trees  i ) INSERTION

                             

6. Advanced Analysis Technique (5 Lectures) 

Amortized analysis 

7. Graphs (5 Lectures) 

Graph Algorithms–

Breadth First Search - CLICK HERE

Depth First Search -  CLICK HERE

and its Applications, 

Minimum Spanning Trees. -  1. PRIMS - CLICK

                                               2. KRUSKAL - CLICK

 8. String Processing (5Lectures)

 String Matching,

KMP Technique

Software Engineering

1.Introduction (8 Lectures) The Evolving Role of Software, Software Characteristics, Changing Nature of Software, Software Engineering as a Layered Technology, Software Process Framework, Framework and Umbrella Activities, Process Models, Capability Maturity Model Integration (CMMI).
2.Requirement Analysis (10 Lectures) Software Requirement Analysis, Initiating Requirement Engineering Process, Requirement Analysis and Modeling Techniques, Flow Oriented Modeling, Need for SRS, Characteristics and Components of SRS.
3.Software Project Management
(8Lectures) Estimation in Project Planning Process, Project Scheduling.
4.Risk Management
(8 Lectures) Software Risks, Risk Identification, Risk Projection and Risk Refinement, RMMM Plan. 5.Quality Management
(8 Lectures) Quality Concepts, Software Quality Assurance, Software Reviews, Metrics for Process and Projects.
6.Design Engineering
(10 Lectures) Design Concepts, Architectural Design Elements, Software
Architecture, Data Design at the
Architectural Level and Component Level, Mapping of Data Flow into Software Architecture, Modeling Component Level Design. 7.Testing Strategies & Tactics (8 Lectures) Software Testing Fundamentals, Strategic Approachto Software Testing, Test Strategies for Conventional Software, Validation Testing, System testing, Black-Box Testing, White-Box Testing and their type, Basis Path Testing.

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