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RGPV B.Tech · 2nd Year

AIML 3rd Semester Syllabus

Artificial Intelligence & Machine Learning (AIML): the official syllabus of all 6 3rd sem subjects, unit by unit, with previous year papers, notes and important questions. Free to read.

Subjects

AIML 3rd semester syllabus, unit by unit

Every subject of RGPV Artificial Intelligence & Machine Learning 3rd semester (2nd year), as the official syllabus lists it.

Technical Communication (TC)

AL-301

  1. Unit 1Technical Communication Skills

    Understanding the process and scope of communication · Relevance and importance of communication in a globalized world · Forms of communication · Role of unity, brevity and clarity in communication

  2. Unit 2Types of Communication & Soft Skills

    Verbal communication · Non‑verbal communication (NVC) classification · Barriers to communication · Communicating globally · Culture and communication · Interpersonal communication · Listening skills · Persuasion techniques · Negotiation skills · Communicating bad news/messages · Communicating in a global world

  3. Unit 3Writing Skills & Speaking Skills

    Traits of technical writing · Principles of business writing · Writing style · Writing memos, letters, and reports · Types, characteristics, format and structure of technical reports · Writing research papers · Audience‑awareness in speaking · Voice, vocabulary and paralanguage · Group discussion techniques · Combating nervousness · Speaking to small and large audiences · Mock presentations

  4. Unit 4Job Interviews, Group Discussion & Professional Presentations

    Preparing for job interviews · Self‑assessment for interviews · Drafting effective résumé · Dress, decorum and delivery techniques · Techniques of handling interview questions · Use of non‑verbals during interviews · Handling turbulence during interviews · Group discussion objectives, method, focus, content, style and argumentation · Professional presentations: individual presentations, audience awareness, body language, delivery and content

  5. Unit 5Grammar & Linguistic Ability

    Basics of grammar · Common errors in writing and speaking · Advanced grammar study · Vocabulary development · Pronunciation etiquette · Syllable structure · Vowel sounds · Consonant sounds · Tone: rising and falling · Flow in speaking · Speaking with purpose · Speech and personality · Professional personality attributes

Introduction to Probability and Statistics (IPS)

AL-302

  1. Unit 1Basic 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

  2. Unit 2Continuous Probability Distributions

    Continuous random variables and their properties · Distribution functions and densities · Normal, exponential and gamma densities

  3. Unit 3Bivariate Distributions

    Bivariate distributions and their properties · Distribution of sums and quotients · Conditional densities · Bayes' rule

  4. Unit 4Basic 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

  5. Unit 5Applied 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

  6. Unit 6Small 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

Data Structures (DS)

AL-303

  1. Unit 1Introduction to Data Structure

    Concepts of Data and Information · Classification of Data structures · Abstract Data Types · Implementation aspects: Memory representation · Data structures operations and cost estimation · Linear data structures: Arrays · Linked List representation in memory · Different implementations of linked list · Circular linked list · Doubly linked list · Applications of linked list (polynomial manipulation)

  2. Unit 2Stacks and Queue

    Stacks as ADT · Different implementations of stack · Multiple stacks · Applications of stack: infix‑to‑postfix conversion, postfix evaluation, recursion · Queues as ADT · Different implementations of queue · Circular queue · Dequeue and Priority Queue concepts · Queue simulation · Applications of queues

  3. Unit 3Tree

    Tree definitions: height, depth, order, degree · Binary Search Tree operations, traversal, search · AVL Tree · Heap · Applications and comparison of various tree types · Forest · Multi‑way Tree · B‑tree · B+ tree · B* tree · Red‑black tree

  4. Unit 4Graphs

    Graph introduction and classification (directed, undirected) · Graph representation · Graph traversal: Depth‑First Search (DFS), Breadth‑First Search (BFS) · Minimum Spanning Tree algorithms: Kruskal, Prim · Dijkstra’s shortest‑path algorithm · Comparison of graph algorithms · Applications of graphs

  5. Unit 5Sorting

    Sorting introduction · Sorting algorithms: Bubble Sort, Quick Sort, Selection Sort, Heap Sort, Insertion Sort, Shell Sort, Merge Sort, Radix Sort · Comparison of sorting techniques · Searching techniques: Sequential search, Binary search · Comparison of search methods · Hashing & Indexing · Case study: application of data structures in operating systems and DBMS

Artificial Intelligence (AI)

AL-304

  1. Unit 1Fundamentals and Search Techniques

    Fundamentals of AI · History of AI · Motivation and need of AI · Production systems · Characteristics of production systems · Goals and contributions of AI to modern technology · Search space · Hill Climbing · Best‑first Search · Heuristic search algorithms · A* search · AO* search

  2. Unit 2Knowledge Representation and Reasoning

    Knowledge representation · Problems in representing knowledge · Propositional logic · Predicate logic · Comparison of propositional and predicate logic · Resolution · Refutation · Deduction · Theorem proving · Inferencing · Monotonic reasoning · Non‑monotonic reasoning

  3. Unit 3Probabilistic and Symbolic Reasoning

    Probabilistic reasoning · Bayes' theorem · Semantic networks · Scripts · Schemas · Frames · Conceptual dependency · Forward reasoning · Backward reasoning

  4. Unit 4Game Playing, Planning and NLP

    Minimax procedure · Alpha‑beta pruning · Planning · Block world problem in robotics · Natural Language Processing (NLP) introduction · Components of NLP · Application of NLP to expert systems

  5. Unit 5Expert Systems

    Characteristics of expert systems · Requirements of expert systems · Components of expert systems · Capabilities of expert systems · Inference engine · Forward chaining · Backward chaining · Limitations of expert systems · Expert system development environment · Technology for expert systems · Benefits of expert systems

Object Oriented Programming & Methodology (OOP)

AL-305

  1. Unit 1Introduction to Object Oriented Thinking & Object Oriented Programming

    Comparison with procedural programming · Features of the object‑oriented paradigm · Merits and demerits of OO methodology · Object model and its components · Elements of OOP (objects, classes, inheritance, etc.) · I/O processing in OO programs · Data types and type conversion · Control statements · Loops · Arrays

  2. Unit 2Encapsulation and Data Abstraction

    Concept of objects: state, behavior, identity · Identifying classes and candidate classes · Attributes and services of classes · Access modifiers · Static members of a class · Object instances · Message passing between objects · Construction and destruction of objects

  3. Unit 3Relationships and Inheritance

    Purpose and types of inheritance · "is‑a" relationship · Association relationship · Aggregation relationship · Concept of interfaces · Abstract classes

  4. Unit 4Polymorphism

    Introduction to polymorphism · Method overriding · Method overloading · Static (compile‑time) polymorphism · Run‑time (dynamic) polymorphism · Virtual functions · Friend functions · Static functions · Friend classes

  5. Unit 5Advanced Topics: Strings, Exception Handling, Multithreading, and Collections

    String handling · Exception handling mechanisms · Introduction to multithreading · Data collections · Case study: ATM system · Case study: Library management system

Computer Workshop/Introduction to Python-I (CWIP)

AL-306

  1. Unit 1Introduction to Python Language

    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

  2. Unit 2Data Structures

    List · Tuples · Dictionary · DataFrame · Sets · constructing · indexing · slicing · content manipulation

  3. Unit 3Control Flow

    Conditional Statements – If · If-else · Nested If-else · Iterative Statement – For · While · Nested Loops · Control statements – Break · Continue · Pass

  4. Unit 4Object Oriented Programming

    Class and Object · Attributes · Methods · Scopes and Namespaces · Inheritance · Overloading · Overriding · Data hiding · Exception Handling · Except clause · Try finally clause · User Defined Exceptions

  5. Unit 5Modules and Packages

    File I/O · Sys · logging · Regular expression · Date and Time · Network programming · multi-processing · multithreading