Syllabus of B. Tech. IV Sem AIML (RGPV)

• AL-401 Introduction to Discrete Structure & Linear Algebra

• AL-402 Analysis & Design of Algorithms

• AL-403 Software Engineering

• AL-404 Computer Organization& Architecture

• AL-405 Machine Learning

• AL-406 Java Lab

​Syllabus ofAL-401 Introduction to Discrete Structure &Linear Algebra

Source: (rgpv.ac.in)

UNIT-1 :

• Set Theory Relation Function

• Theorem Proving Techniques:

• Set theory: definition of sets

• Venn Diagram

• proofs of some general identities on set

• Relation: Definition Types of relation Composition of relation

• Equivalence relation

• Partial ordering relation

• POSET Hasse diagram and Lattice

UNIT-2 :

• Algebraic structure: Definition Properties

• types: Semi Group Monoid Groups Abelian Group

• Properties of group cyclic group Normal subgroup

• Ring and Fields: definition and standard result

• Introduction to Recurrence Relation and Generating Functions.

UNIT-3 :

• Propositional logic: Proposition First order Logic

• Basic logical operation Truth tables

• Tautologies and Contradiction algebra of proposition

• logical implication logical equivalence predicates

• Normal Forms Quantifiers

• Graph theory: Introduction and basic terminology of graph

• types of graph Path Cycles

• Shortest path in weighted graph

• graph colorings.

UNIT-4 :

• Matrices: Determinant and Trace

• Cholesky Decomposition

• Eigen decomposition

• Singular Value decomposition(SVD)

• Gradient of a matrix:Useful identities For computing Gradient.

UNIT-5 :

• Test of Hypothesis : Concept and Formulation

• Type-I and Type-II Errors

• Time Series Analysis

• Analysis of Variance (ANOVA).

== END OF UNITS==

Syllabus of AL-402 Analysis & Design of Algorithms

Source: (rgpv.ac.in)

UNIT-1 :

• Definitions of algorithms and complexity

• Time and Space Complexity; Time space trade off

• various bounds on complexity Asymptotic notation

• Recurrences and Recurrences solving techniques

• Introduction to divide and conquer technique

• example: binary search merge sort quick sort heap sort

• strassen’s matrix multiplication etc

• Code tuning techniques: Loop Optimization

• Data Transfer Optimization Logic Optimization etc.

UNIT-2 :

• Study of Greedy strategy

• examples of greedy method like optimal merge patterns

• Huffman coding

• minimum spanning trees

• knapsack problem job sequencing with deadlines

• single source shortest path algorithm etc.

• Correctness proof of Greedy algorithms.

UNIT-3 :

• Concept of dynamic programming

• problems based on this approach such as 0/1 knapsack

• multistage graph reliability design

• Floyd-Warshall algorithm etc.

UNIT-4 :

• Backtracking concept and its examples like 8 queen’s problem

• Hamiltonian cycle Graph colouring problem etc.

• Introduction to branch & bound method examples of branch and bound method like travelling salesman problem etc.

• Meaning of lower bound theory and its use in solving algebraic problem

• introduction to parallel algorithms.

UNIT-5 :

• Advanced tree and graph algorithms

• NP-hard and NP-complete problems

• Approximations Algorithms

• Data Stream Algorithms

• Introduction to design and complexity of Parallel Algorithms.

List of Experiments :

1. Write a program for Iterative and Recursive Binary Search.
2. Write a program for Merge Sort.
3. Write a program for Quick Sort.
4. Write a program for Strassen’s Matrix Multiplication.
5. Write a program for optimal merge patterns.
6. Write a program for Huffman coding.
7. Write a program for minimum spanning trees using Kruskal’s algorithm.
8. Write a program for minimum spanning trees using Prim’s algorithm.
9. Write a program for single sources shortest path algorithm.
10. Write a program for Floye-Warshal algorithm.
11. Write a program for traveling salesman problem.
12. Write a program for Hamiltonian cycle problem.

== END OF UNITS==

Syllabus of AL-403 Software Engineering

Source: (rgpv.ac.in)

UNIT-1 : The Software Product and Software Process

• Software Product and Process Characteristics

• Software Process Models: Linear Sequential Model Prototyping Model

• RAD Model Evolutionary Process Models like Incremental Model Spiral Model Component Assembly Model

• RUP and Agile processes. Software Process customization and improvement

• CMM

• Product and Process Metrics

UNIT-2 : Requirement Elicitation Analysis and Specification

• Functional and Non-functional requirements

• Requirement Sources and Elicitation Techniques

• Analysis Modeling for Function-oriented and Object-oriented software development

• Use case Modeling

• System and Software Requirement Specifications

• Requirement Validation

• Traceability

UNIT-3 : Software Design

• The Software Design Process

• Design Concepts and Principles

• Software Modeling and UML

• Architectural Design

• Architectural Views and Styles

• User Interface Design

• Function- oriented Design

• SA/SD Component Based Design

• Design Metrics

UNIT-4 : Software Analysis and Testing

• Software Static and Dynamic analysisCode inspections

• Software Testing Fundamentals

• Software Test Process

• Testing Levels Test Criteria Test Case Design Test Oracles

• Test Techniques BlackBox Testing White-Box Unit Testing and Unit

• Testing Frameworks Integration Testing

• System Testing and other Specialized Testing

• Test Plan Test Metrics Testing Tools.

• Introduction to Object-oriented analysis

• design and comparison with structured Software Engg.

UNIT-5 : Software Maintenance & Software Project Measurement

• Need and Types of Maintenance

• Software Configuration Management (SCM) Software Change Management

• Version Control Change control and Reporting

• Program Comprehension Techniques

• Re-engineering Reverse Engineering

• Tool Support. Project Management Concepts Feasibility Analysis

• Project and Process PlanningResources Allocations

• Software efforts Schedule and Cost estimations

• Project Scheduling and Tracking Risk Assessment and Mitigation

• Software Quality Assurance (SQA).

• Project Plan Project Metrics.

Practical and Lab work :

• Lab work should include a running case study problem for which different deliverable sat the end of each phase of a software development life cycle are to be developed. This will include modeling the requirements architecture and detailed design. Subsequently the design models will be coded and tested. For modeling tools like Rational Rose products. For coding and testing IDE like Eclipse Net Beans and Visual Studio can be used

== END OF UNITS==

Syllabus of AL-404 Computer Organization & Architecture

Source: (rgpv.ac.in)

UNIT-1 :

• Basic Structure of Computer:Structure of Desktop Computers

• CPU: General Register Organization-Memory Register

• Instruction Register Control Word

• Stack Organization Instruction Format ALU

• I/O System busCPU and Memory Program Counter Bus Structure Register Transfer Language-Bus and Memory Transfer addressing modes.

• Control Unit Organization: Basic Concept of Instruction Instruction Types Micro Instruction Formats Fetch and Execution cycle

• Hardwired control unit Micro- programmed Control unit micro program sequencer Control Memory

• Sequencing and Execution of Micro Instruction.

UNIT-2 :

• Computer Arithmetic: Addition and Subtraction

• Tools Compliment Representation

• Signed Addition and Subtraction Multiplication and division

• Booths Algorithm Division Operation

• Floating Point Arithmetic Operation.

• design of Arithmetic unit

UNIT-3 :

• I/O Organization: I/O Interface –PCI Bus SCSI Bus USB

• Data Transfer: Serial Parallel Synchronous

• Asynchronous Modes of Data Transfer

• Direct Memory Access(DMA)

• I/O Processor

UNIT-4 :

• Memory Organization:Main memory-RAM ROM

• Secondary Memory –Magnetic Tape Disk Optical Storage

• Cache Memory: Cache Structure and Design

• Mapping Scheme Replacement Algorithm

• Improving Cache Performance

• Virtual Memory

• memory management hardware

UNIT-5 :

• Multiprocessors: Characteristics of Multiprocessor

• Structure of Multiprocessor-Inter processor Arbitration

• Inter-Processor Communication and Synchronization.

• Memory in Multiprocessor System

• Concept of Pipelining Vector Processing

• Array Processing RISC And CISC

• Study of Multicore Processor –IntelAMD.

List of Experiments :

1. Study of Multiplexer and Demultiplexer
2. Study of Half Adder and Subtractor
3. Study of Full Adder and Subtractor
4. WAP to add two 8 bit numbers and store the result at memory location 2000
5. WAP to multiply two 8 bit numbers stored at memory location 2000 and 2001 and stores the result at memory location 2000 and 2001.
6. WAP to add two 16-bit numbers. Store the result at memory address starting from 2000.
7. WAP which tests if any bit is ‘0’ in a data byte specified at an address 2000. If it is so 00 would be stored at address 2001 and if not so then FF should be storedata the same address.
8. Assume that 3 bytes of data are stored at consecutive memory addresses of the data memory starting at 2000. Write a program which loads register C with (2000) i.e. with data contained at memory address 2000 D with (2001) E with (2002) and A with (2001).
9. Sixteen bytes of data are specified at consecutive data-memory locations starting at 2000. Write a program which increments the value of all sixteen bytes by01.
10. WAP to add t 10 bytes stored at memory location starting from 3000. Store the result at memory location 300A

== END OF UNITS==

Syllabus of AL-405 Machine Learning

Source: (rgpv.ac.in)

UNIT-1 :

• Introduction to machine learning scope and limitations

• machine learning models

• Supervised Learning Unsupervised Learning

• hypothesis space and inductive bias evaluation cross-validation

• Dimensionality Reduction: Subset Selection Shrinkage Methods

• Principle Components Analysis

• Partial Least Squares.

UNIT- 2 :

• Neural Networks: From Biology to Simulation Neural network representation

• Neural Networks as a paradigm for parallel processing Perceptron Learning

• Training a perceptron

• Multilayer perceptron back propagation Algorithm

• Training & ValidationActivation functions

• Vanishing and Exploding Gradients

UNIT-3 :

• Supervised Learning Techniques:- Decision Trees Naive Bayes

• Classification Support vector machines for classification problems

• Random forest for classification and regression problems

• Linear regression for regression problems

• Ordinary Least Squares Regression

• Logistic Regression.

UNIT-4 :

• Unsupervised Learning

• Clustering: k-means adaptive hierarchical clustering

• Gaussian mixture model

• Optimization Using Evolutionary Techniques

• Number of Clusters Advanced discussion on clustering

• Expectation Maximization.

UNIT-5 :

• Design and Analysis of Machine Learning Experiments: Factors

• response and strategy of experimentation

• Guidelines for machine learning experiments

• cross-validation and resampling methods

• Measuring classifier performance

• Hypothesis testing comparing multiple algorithms

• comparison over multiple datasets

List of Experiments:

• Different problems to be framed to enable students to understand the concept learnt and get hands-on on various tools and software related to the subject

1. How to calculate important numbers based on data sets how to use various Python modules and how to make functions that are able to predict the outcome based on what we have learned (Small Dataset)
2. How can we get Big Data Sets Learn: Data Distribution Normal data distribution Random Data Distribution Scatter Plot.
3. Build an Artificial Neural Network by implementing the Backpropagation algorithm and test the same using appropriate data sets.
4. The probability that it is Friday and that a student is absentis 3%. Since there are 5 school days in a weekthe probability that it is Friday is 20%. What is the probability that a student is absent given that today is Friday? Apply Baye’s rule in python to get the result.
5. Write a program to implement k-Nearest Neighbour algorithm to classify the iris data set.
6. Implement and demonstrate simple Linear Regression Algorithm based on a given set of training data samples. Read the training data from a .CSV file.
7. Implement and demonstrate Logistic Regression (Binomial) based on a given set of training data samples. Read the training data from a .CSV file.
8. Implement and demonstrate naïve Bayesian classifier for a sample training data set stored as a .CSV file. Compute the accuracy of the classifier considering few test data sets.
9. Write a program to demonstrate the working of the decision tree based ID3 algorithm. Use an appropriate data set for building the decision tree and apply this knowledge to classify a new sample.
10. Given the following data which specify classifications for nine combinations of VAR1 and VAR2 predict a classification for a case where VAR1=0.906 and VAR2=0.606 using the result of k-means clustering with 3 means (i.e. 3 centroids)

VAR1  VAR2  CLASS

1.713  1.586     0

0.180  1.786    1

0.353  1.240   1

0.940  1.566   0

1.486  0.759   1

1.266  1.106    0

1.540  0.419   1

0.459  1.799   1

0.773  0.186   1

== END OF UNITS==

Syllabus of AL-406 Java Lab

Source: (rgpv.ac.in)

UNIT-1 :

• Overview of Java Installation

• First Simple Program Compilation process

• Java Keywords Identifiers Literals Comments

• Data Types Variables Dynamic initialization

• type conversion and casting

• Operators Control Statements. .

UNIT-2 :

• Declaring Objects Introducing Methods

• Constructors this Keyword

• Garbage Collection finalize Method

• Overloading Methods Overloading Constructors

• Using Objects as Parameters

• Inheritance

• Creating a Multilevel Hierarchy Packages and Interfaces

• Exception Handling Multi threaded

UNIT-3 :

• The Applet Class: Applet Basics

• The Applet Class Applet Architecture

• Applet Initialization and Termination

• Simple Applet Display Methods Simple Banner Applet

• Using the Status Window

• The HTML APPLET Tag

• Passing Parameters to Applets

• Improving the Banner Applet.

UNIT-4 :

• Introducing the AWT: Working with Windows

• Graphics and Text AWT Classes

• Window Fundamentals Component Container

• Panel Frame Working with Frame Windows

• Handling Events in a Frame Window

• AWT Controls Layout Managers and Menus

• Adding and Removing Controls

• Grid Layout Border Layout

• introduction to swing and servlet

UNIT-5 :

• Event Handling Two Event Handling Mechanisms

• The Delegation Event Model Events Event Sources Event Listeners Event Classes

• The Mouse Event Class and others

• JDBC: JDBCODBC bridge the connectivity model

• the driver manager navigating the result set object contents

• the JDBC exceptional classes

• connecting to remote database.

List of Experiments :

1. Write a program that accepts two numbers from the user and print their sum.
2. Write a program to calculate addition of two number using prototyping of methods.
3. Program to demonstrate function overloading for calculation of average.
4. Program to demonstrating overloaded constructor for calculating box volume.
5. Program to show the detail of students using concept of inheritance.
6. Program to demonstrate package concept.
7. Program to demonstrate implementation of an interface which contains two methods declaration square and cube.
8. Program to demonstrate exception handling in case of division by zero error.
9. Program to demonstrate multi threading.
10. Program to demonstrate JDBC concept using create a GUI based application for student information.
11. Program to display “Hello World” in web browser using applet.
12. Program to add user controls to applets.
13. Write a program to create an application using concept of swing.
14. Program to demonstrate student registration functionality using servlets with session management.

== END OF UNITS==

==End of Syllabus==