Syllabus of B.tech. III SEM AIML (RGPV)
Syllabus of B. Tech. III Sem AIML (RGPV)
Syllabus of AL-301 Technical Communication
Source: (rgpv.ac.in)
Unit-1 : Technical Communication Skills
Understanding the process and scope of Communication,
Relevance, & Importance of Communication in a Globalized world,
Forms of Communication,
Role of Unity, Brevity and Clarity in various forms of communication
Unit-2 : Types of Communication
Verbal & Non-verbal Communication,
Classification of NVC,
Barriers to Communication,
Communicating Globally,
Culture and Communication.
Soft Skills: Interpersonal Communication,
Listening, Persuasion, Negotiation,
Communicating bad news/messages,
communicating in a global world.
Unit-3 : Writing Skills
Traits of Technical Writing,
Principles of Business Writing,
Style of Writing, Writing Memos,
Letters, Reports, and Types of technical reports,
Characteristics, format and structure of technical reports,
Writing Research Papers.
Speaking Skills: Audience-awareness, Voice, Vocabulary and Paralanguage,
Group Discussion, Combating Nervousness,
Speaking to one and to one thousand,
Mock Presentations.
Unit-4 : Job Interviews
Preparing for interviews, assessing yourself,
Drafting Effective Resume, Dress, decorum and Delivery techniques,
Techniques of handling interviews,
Use of Nonverbals during Interviews,
Handling turbulence during interviews.
Group Discussion: Objective, Method, Focus, Content,
Style and Argumentation skills.
Professional Presentations: Individual Presentations (Audience Awareness,
Body Language,
Delivery and Content of Presentation.
Unit-5 : Grammar & Linguistic ability
Basics of grammar,
common error in writing and speaking,
Study of advanced grammar,
Vocabulary, Pronunciation Etiquette, Syllables,
Vowel sounds, Consonant sounds,
Tone: Rising tone, Falling Tone,
Flow in Speaking, Speaking with a purpose,
Speech & personality,
Professional Personality Attributes
== END OF UNITS==
Syllabus of AL-302 Introduction to Probability and Statistics
Source: (rgpv.ac.in)
Unit-1 : Basic Probability
Probability spaces, conditional probability, independence;
Discrete random variables, Independent random variables,
the multinomial distribution,
Poisson approximation to the binomial distribution,
infinite sequences of Bernoulli trials,
sums of independent random variables;
Expectation of Discrete Random Variables,
Moments, Variance of a sum,
Correlation coefficient,
Chebyshev's Inequality.
Unit-2 : Continuous Probability Distributions
Continuous random varibales and their properties,
distribution functions and densities,
normal, exponential and gamma densities.
Unit-3 : Bivariate Distributions
Bivariate distributions and their properties,
distribution of sums and quotients,
conditional densities,
Bayes' rule.
Unit-4 : Basic Statistics
Measures of Central tendency: Moments, skewness and Kurtosis -
Probability distributions: Binomial,
Poisson and Normal - evaluation of statistical parameters for these three distributions,
Correlation and regression – Rank correlation.
Unit-5 : Applied Statistics
Curve fitting by the method of least squares- fitting of straight lines,
second degree parabolas and more general curves.
Test of significance: Large sample test for single proportion,
difference of proportions,
single mean, difference of means, and difference of standard deviations.
Unit-6 : Small samples
Test for single mean, difference of means and correlation coefficients,
test for ratio of variances - Chi-square test for goodness of fit and independence of attributes.
== END OF UNITS==
Syllabus of AL-303 Data Structures
Source: (rgpv.ac.in)
Unit-1 : Introduction to Data Structure
Concepts of Data and Information,
Classification of Data structures,
Abstract Data Types,
Implementation aspects: Memory representation.
Data structures operations and its cost estimation.
Introduction to linear data structures- Arrays,
Linked List: Representation of linked list in memory,
different implementation of linked list. Circular linked list, doubly linked list, etc.
Application of linked list: polynomial manipulation using linked list, etc.
Unit-2 : Stacks and Queue
Stacks as ADT, Different implementation of stack, multiple stacks.
Application of Stack: Conversion of infix to postfix notation using stack,
evaluation of postfix expression,
Recursion. Queues: Queues as ADT,
Different implementation of queue, Circular queue,
Concept of Dqueue and Priority Queue, Queue simulation, Application of queues.
Unit-3 : Tree
Definitions - Height, depth, order, degree etc.
Binary Search Tree - Operations, Traversal, Search.
AVL Tree, Heap,
Applications and comparison of various types of tree;
Introduction to forest, multi-way Tree, B tree, B+ tree, B* tree and red-black tree.
Unit-4 : Graphs
Introduction,
Classification of graph: Directed and Undirected graphs, etc,
Representation, Graph Traversal: Depth First Search (DFS), Breadth First Search (BFS),
Graph algorithm: Minimum Spanning Tree (MST)-Kruskal, Prim’s algorithms.
Dijkstra’s shortest path algorithm; Comparison between different graph algorithms.
Application of graphs.
Unit-5 : Sorting
Introduction,
Sort methods like: Bubble Sort, Quick sort.
Selection sort, Heap sort, Insertion sort, Shell sort, Merge sort and Radix sort;
comparison of various sorting techniques.
Searching: Basic Search Techniques: Sequential search, Binary search,
Comparison of search methods.
Hashing & Indexing.
Case Study: Application of various data structures in operating system, DBMS etc.
== END OF UNITS==
Syllabus of AL-304 Artificial Intelligence
Source: (rgpv.ac.in)
Unit-1 :
Fundamental of Artificial Intelligence, history,
motivation and need of AI,
Production systems, Characteristics of production systems,
goals and contribution of AI to modern technology,
search space, different search techniques: hill Climbing, Best first Search, heuristic search algorithm, A* and AO* search techniques etc.
UNIT-2 :
Knowledge Representation, Problems in representing knowledge,
knowledge representation using propositional and predicate logic,
comparison of propositional and predicate logic,
Resolution, refutation, deduction,
theorem proving, inferencing,
monotonic and non-monotonic reasoning.
UNIT-3 :
Probabilistic reasoning,
Baye's theorem,
semantic networks, scripts, schemas, frames,
conceptual dependency,
forward and backward reasoning.
UNIT-4 :
Game playing techniques like minimax procedure, alpha-beta cut-offs etc,
planning, Study of the block world problem in robotics,
Introduction to understanding,
natural language processing (NLP),
Components of NLP,
application of NLP to design expert systems.
UNIT-5 :
Expert systems (ES) and its Characteristics, requirements of ES,
components and capability of expert systems,
Inference Engine Forward & backward Chaining,
Expert Systems Limitation,
Expert System Development Environment, technology,
Benefits of Expert Systems.
== END OF UNITS==
Syllabus of AL-305 Object Oriented Programming & Methodology
Source: (rgpv.ac.in)
Unit-1 :
Introduction to Object Oriented Thinking & Object Oriented Programming: Comparison with Procedural Programming,
features of Object oriented paradigm– Merits and demerits of OO methodology;
Object model;
Elements of OOPS,
IO processing,
Data Type, Type Conversion,
Control Statement,
Loops, Arrays.
UNIT- 2 :
Encapsulation and Data Abstraction-
Concept of Objects: State, Behavior & Identity of an object;
Classes: identifying classes and candidates for Classes Attributes and Services,
Access modifiers,
Static members of a Class, Instances,
Message passing, and Construction and destruction of Objects.
UNIT-3 :
Relationships – Inheritance: purpose and its types, ‘is a’ relationship;
Association,
Aggregation.
Concept of interfaces and Abstract classes.
UNIT-4 :
Polymorphism: Introduction,
Method Overriding & Overloading,
static and run time Polymorphism.
Virtual Function, friend function, Static function,
friend class.
UNIT-5 :
Strings,
Exceptional handling,
Introduction of Multi-threading and Data collections.
Case study like: ATM, Library management system.
== END OF UNITS==
Syllabus of AL-306 Computer Workshop/Introduction to Python-I
Source: (rgpv.ac.in)
MODULE-1 :
Introduction to python language,
Basic syntax,
Literal Constants, Numbers,
Variable and Basic data types,
String, Escape Sequences,
Operators and Expressions, Evaluation Order, Indentation,
Input, Output, Functions, Comments..
MODULE-2 :
Data Structure: List, Tuples, Dictionary,
DataFrame and Sets,
constructing, indexing,
slicing and content manipulation
MODULE-3 :
Control Flow:Conditional Statements - If, If-else, Nested If-else.
Iterative Statement - For, While, Nested Loops.
Control statements - Break, Continue, Pass.
MODULE-4 :
Object oriented programming:Class and Object,
Attributes, Methods,
Scopes and Namespaces,
Inheritance,
Overloading, Overriding,
Data hiding,Exception: Exception Handling, Except clause, Try finally clause,
User Defined Exceptions.
MODULE-5 :
Modules and Packages: Standard Libraries: File I/0, Sys, logging,
Regular expression, Date and Time,
Network programming,
multi-processing and multithreading.
== END OF UNITS==