JNTUK R23 B.Tech AI & ML II Year I Semester (2-1) Syllabus & Subject-wise Topics

by | Jul 11, 2026 | JNTUK R23 Syllabus

The first semester of second year builds the mathematical and programming backbone the AI&ML specialization rests on, introducing Artificial Intelligence as a standalone course alongside advanced data structures and Java. Two hands-on labs pair with the theory courses, and Python is introduced as a skill-enhancement course to prepare students for the data-centric work ahead. A humanities course on human values and an audit course on environmental science round out the semester’s non-technical requirements.

Subjects

Discrete Mathematics & Graph Theory

• Unit 1: Mathematical logic — statements, connectives, well-formed formulas, truth tables, tautologies, normal forms, and inference techniques for both propositional and predicate calculus.
• Unit 2: Set theory — set operations, inclusion-exclusion, relations (partitions, closures, partial orders, Hasse diagrams), and functions including bijections, composition, and recursion.
• Unit 3: Combinatorics and recurrence relations — counting principles, permutations and combinations, binomial/multinomial theorems, generating functions, and methods for solving recurrences.
• Unit 4: Graph theory fundamentals — subgraphs, adjacency/incidence representations, isomorphism, and Eulerian/Hamiltonian paths.
• Unit 5: Multigraphs — bipartite and planar graphs, graph colouring and covering, chromatic number, spanning trees, and Prim’s/Kruskal’s/BFS/DFS-based tree construction.

L-T-P: 3-0-0, 3 credits

Universal Human Values — Understanding Harmony and Ethical Human Conduct

• Unit 1: Introduction to value education — self-exploration as a process, and the distinction between values and skills for sustained happiness.
• Unit 2: Harmony within the individual — the self as distinct from the body, and self-regulation as a path to inner balance.
• Unit 3: Harmony in family and society — trust and respect as foundational relational values, extending to a vision of a harmonious social order.
• Unit 4: Harmony with nature — interconnectedness among the orders of nature and existence understood as co-existence.
• Unit 5: Professional ethics — translating holistic understanding into ethical conduct, humanistic education, and value-based professional practice.

L-T-P: 2-1-0, 3 credits

Artificial Intelligence

• Unit 1: Foundations — the history of AI, intelligent agents, rationality, environment types, and problem formulation.
• Unit 2: Search — uninformed strategies (BFS, DFS), heuristic search (hill climbing, A, AO), and adversarial game-playing including minimax and alpha-beta pruning.
• Unit 3: Knowledge representation — predicate logic, semantic networks, frames, rule-based systems, and reasoning under uncertainty using Bayesian and Dempster-Shafer approaches.
• Unit 4: First-order logic and learning — inference techniques, forward/backward chaining, resolution, and learning paradigms including decision trees and reinforcement learning.
• Unit 5: Expert systems — architecture, knowledge acquisition, heuristics, and classic systems such as MYCIN and DART.

L-T-P: 3-0-0, 3 credits

Advanced Data Structures & Algorithm Analysis

• Unit 1: Algorithm analysis fundamentals, asymptotic notation, and self-balancing/multi-way search structures — AVL trees and B-trees.
• Unit 2: Heap-based priority queues, graph representations and traversal, and divide-and-conquer techniques such as quicksort, mergesort, and Strassen’s algorithm.
• Unit 3: Greedy strategies (job sequencing, minimum spanning trees, shortest paths) and dynamic programming (all-pairs shortest paths, optimal BSTs, knapsack, TSP).
• Unit 4: Backtracking (N-Queens, subset sum, graph colouring) and branch-and-bound approaches to knapsack and TSP.
• Unit 5: Computational complexity theory — NP-hard and NP-complete problems, Cook’s theorem, and classic hard graph/scheduling problems.

L-T-P: 3-0-0, 3 credits

Object Oriented Programming through Java

• Unit 1: OOP fundamentals, Java program structure, data types, operators, and control-flow statements.
• Unit 2: Classes, objects, constructors, access control, and method design including overloading and recursion.
• Unit 3: Arrays and inheritance mechanics, plus interfaces including default and static methods.
• Unit 4: Packages, the Java class library, exception handling, and Java I/O.
• Unit 5: String handling, multithreading, JDBC-based database connectivity, and building simple JavaFX GUIs.

L-T-P: 3-0-0, 3 credits

Advanced Data Structures & Algorithm Analysis Lab

• Hands-on construction and manipulation of AVL trees, B-trees, and heaps.
• Graph traversal implementations (BFS/DFS) and detection of connected/biconnected components.
• Comparative implementation of sorting algorithms and shortest-path techniques.
• Applying greedy and dynamic-programming strategies to knapsack, job sequencing, and spanning-tree problems.
• Backtracking and branch-and-bound implementations for N-Queens and the travelling salesperson problem.

L-T-P: 0-0-3, 1.5 credits

Object Oriented Programming through Java Lab

• Core language exercises covering primitive types, control structures, and basic I/O.
• Class design exercises: constructors, overloading, and access modifiers.
• Inheritance, interfaces, and runtime polymorphism implementations, including custom exception classes.
• Multithreading exercises (thread creation, synchronization, producer-consumer) and package creation.
• JavaFX GUI building and JDBC-based database connectivity exercises.

L-T-P: 0-0-3, 1.5 credits

Python Programming

(Skill Enhancement Course)

• Unit 1: Python fundamentals — installation and tooling, core language elements, and control-flow statements.
• Unit 2: Functions, string handling, and list operations.
• Unit 3: Dictionaries, tuples, and sets, along with their built-in operations.
• Unit 4: File handling and an introduction to object-oriented programming in Python.
• Unit 5: A first look at data-science tooling — JSON/XML handling, NumPy arrays, and pandas data frames.

L-T-P: 0-1-2, 2 credits

Environmental Science

(Audit Course)

• Unit 1: The multidisciplinary nature of environmental studies and the state of renewable/non-renewable natural resources.
• Unit 2: Ecosystem structure and function, energy flow, and biodiversity conservation.
• Unit 3: Causes, effects, and control measures for major categories of pollution, plus solid-waste and disaster management.
• Unit 4: Social dimensions of environmental sustainability, environmental legislation, and climate-related issues.
• Unit 5: Population growth, human health, and welfare programmes, supported by field-based observation exercises.

L-T-P: 2-0-0 (non-credit audit course)

Semester total: 16-2-8 contact hours, 20 credits, plus a mandatory 8-week community service project internship during the following summer vacation.

JNTUK R23 B.Tech AI & ML II Year II Semester (2-2) Syllabus & Subject-wise Topics

by | Jul 11, 2026 | JNTUK R23 Syllabus

This semester marks the shift into the AI&ML specialization proper, introducing Machine Learning and Database Management Systems as core subjects alongside the mathematical grounding of Optimization Techniques and Probability & Statistics. Digital Logic & Computer Organization supplies systems-level understanding, while two labs give students hands-on ML and SQL practice. Full Stack Development-1 and Design Thinking & Innovation add practical web-development and ideation skills.

Subjects

Optimization Techniques

(Management Course-I)

• Unit 1: Formulating optimization problems and classical techniques for single- and multi-variable optimization, including Lagrange multipliers and Kuhn-Tucker conditions.
• Unit 2: Linear programming — standard form, geometric interpretation, and the simplex algorithm.
• Unit 3: The transportation problem, including initial feasible solutions and optimality testing.
• Unit 4: Nonlinear programming for constrained and unconstrained cases, covering search methods and penalty-function approaches.
• Unit 5: Dynamic programming — multistage decision processes and the principle of optimality.

L-T-P: 2-0-0, 2 credits

Probability & Statistics

• Unit 1: Descriptive statistics for data science — central tendency, variability, skewness, and kurtosis.
• Unit 2: Correlation and regression, including linear, multiple, and curvilinear regression.
• Unit 3: Probability theory and standard discrete/continuous distributions (binomial, Poisson, normal).
• Unit 4: Sampling theory — sampling distributions, estimation, and the central limit theorem.
• Unit 5: Hypothesis testing — significance levels, error types, and tests for large and small samples.

L-T-P: 3-0-0, 3 credits

Machine Learning

• Unit 1: Introduction to machine learning — its evolution, learning paradigms, and the typical ML pipeline from data acquisition to model evaluation.
• Unit 2: Nearest-neighbour models — proximity measures, KNN classification and regression, and classifier performance evaluation.
• Unit 3: Decision-tree-based models and the Bayes classifier, including Naive Bayes and impurity measures.
• Unit 4: Linear discriminants — perceptrons, support vector machines, logistic regression, and multi-layer perceptrons trained via backpropagation.
• Unit 5: Clustering techniques — K-means, fuzzy C-means, rough clustering, expectation-maximization, and spectral clustering.

L-T-P: 3-0-0, 3 credits

Database Management Systems

• Unit 1: Database fundamentals, data models, schema architecture, and entity-relationship modelling.
• Unit 2: The relational model, constraints, relational algebra/calculus, and basic SQL DDL/DML.
• Unit 3: Advanced SQL querying — joins, nested queries, aggregation, and views.
• Unit 4: Normalization theory from 1NF through BCNF, 4NF, and 5NF.
• Unit 5: Transaction management — ACID properties, concurrency control, recovery, and indexing techniques including B+ trees and hashing.

L-T-P: 3-0-0, 3 credits

Digital Logic & Computer Organization

• Unit 1: Data representation and combinational logic circuits, including K-map minimization.
• Unit 2: Sequential circuits and the basic structural organization of a computer.
• Unit 3: Computer arithmetic and processor organization, including instruction execution and control unit design.
• Unit 4: Memory hierarchy — RAM, ROM, cache, and virtual memory considerations.
• Unit 5: I/O organization — interrupts, DMA, buses, and standard I/O interfaces.

L-T-P: 3-0-0, 3 credits

Machine Learning Lab

• Computing central tendency/dispersion measures and applying preprocessing techniques (attribute selection, missing-value handling, discretization, outlier removal).
• Implementing KNN, decision trees, and random forests for classification and regression.
• Applying Naive Bayes, support vector machines, and both simple linear and logistic regression.
• Building multi-layer perceptron classifiers.
• Implementing K-means, fuzzy C-means, and expectation-maximization clustering.

L-T-P: 0-0-3, 1.5 credits

Database Management Systems Lab

• DDL/DML/DCL practice — table creation, constraints, and basic queries.
• Nested queries, aggregate functions, grouping, and view management.
• PL/SQL programming — control structures, conversion/string/date functions.
• Building procedures, functions, cursors, and triggers.
• Database connectivity exercises using ODBC/JDBC.

L-T-P: 0-0-3, 1.5 credits

Full Stack Development-1

(Skill Enhancement Course)

• HTML fundamentals — lists, links, images, tables, forms, and frames.
• CSS styling — selector types, the box model, and color/font/text properties.
• JavaScript basics — input/output, type conversion, conditional statements, and loops.
• Working with predefined and user-defined JavaScript objects, functions, and events.
• An introduction to Node.js as a bridge toward server-side scripting.

L-T-P: 0-1-2, 2 credits

Design Thinking & Innovation

• Unit 1: Introduction to design thinking and its fundamental components and principles.
• Unit 2: The design thinking process from empathy through ideation.
• Unit 3: Innovation as a discipline and its role in product and service development.
• Unit 4: Product design, including specification-setting and prototyping activities.
• Unit 5: Applying design thinking to business processes.

L-T-P: 1-0-2, 2 credits

Semester total: 15-1-12 contact hours, 21 credits, continuing the mandatory community service project internship theme from the prior semester.

JNTUK R23 B.Tech AI & ML III Year I Semester (3-1) Syllabus & Subject-wise Topics

by | Jul 11, 2026 | JNTUK R23 Syllabus

Third year opens with core systems and information-retrieval courses — Computer Networks, Operating Systems, and Information Retrieval Systems — while introducing the branch’s first Professional Elective and Open Elective choice points. Two lab courses reinforce networking and retrieval theory, and a Flutter-based UI design course plus a second full-stack development module build front-end and mobile skills. A community service project internship is evaluated this semester as well.

Subjects

Information Retrieval Systems

• Unit 1: Foundations of information storage and retrieval, including domain analysis of IR systems.
• Unit 2: Inverted files and signature files as core indexing structures.
• Unit 3: Advanced text indices, lexical analysis, and PAT trees/arrays.
• Unit 4: Stemming algorithms and thesaurus construction.
• Unit 5: String-searching algorithms, from naive matching through more efficient pattern-matching techniques.

L-T-P: 3-0-0, 3 credits

Computer Networks

• Unit 1: Network types, topologies, and reference models (OSI/TCP-IP).
• Unit 2: The data link layer — framing techniques and flow control.
• Unit 3: Medium access control, including random-access schemes like ALOHA and CSMA.
• Unit 4: Network layer design issues, packet switching, and routing services.
• Unit 5: The transport layer — protocol services, port numbers, and connection management.

L-T-P: 3-0-0, 3 credits

Operating Systems

• Unit 1: OS overview, functions, and services.
• Unit 2: Process concepts, scheduling, threading, and inter-process communication.
• Unit 3: Synchronization tools (critical sections, mutex locks, semaphores) and deadlock handling.
• Unit 4: Memory-management strategies, paging, and virtual memory.
• Unit 5: File systems — interfaces, implementation, and storage internals.

L-T-P: 3-0-0, 3 credits

Professional Elective-I options:

• Software Engineering — covers the evolution of software development practice, project-management complexities, the software design process, coding and testing strategies (including black-box testing), and CASE tools.
• Cloud Computing — covers cloud fundamentals, enabling technologies like virtualization and distributed computing, containerization, cloud economics and interoperability, and emerging serverless/FaaS models.
• Internet of Things — covers IoT technology and architecture, business models for connected devices, web-connectivity design principles, data acquisition and analytics, and cloud-based IoT data platforms.
• Exploratory Data Analysis with Python — covers EDA fundamentals, visual aids like line/bar/scatter charts, data transformation and merging, descriptive statistics, and end-to-end model development and evaluation.
• Automata Theory & Compiler Design — covers regular languages and finite automata, context-free grammars and pushdown automata, lexical analysis and top-down parsing, bottom-up parsing, and intermediate code generation/optimization.

L-T-P: 3-0-0, 3 credits (one option selected)

Open Elective-I is filled either from the cross-department open elective pool (each engineering branch offers a course, commonly foundational subjects like Operating Systems or Database Management Systems, to other departments’ students) or by choosing the in-house alternative, Entrepreneurship Development & Venture Creation.

Information Retrieval Lab

• Representing text documents in a vector-space model and computing inter-document similarity.
• Text preprocessing — stop-word removal and stemming.
• Building an inverted index over a moderately sized document collection.
• Classifying and clustering text documents, and evaluating results with standard metrics.
• Web crawling, topic-specific PageRank computation, matrix decomposition/LSI, and social-media text mining.

L-T-P: 0-0-3, 1.5 credits

Computer Networks Lab

• Setting up LAN devices and implementing data-link framing methods (character/bit stuffing, checksums).
• Implementing Hamming code and CRC-based error detection.
• Building sliding-window protocols (Go-Back-N, selective repeat) and the stop-and-wait protocol.
• Implementing congestion control (leaky bucket) and routing algorithms (Dijkstra’s, distance vector).
• Using Wireshark and Nmap for packet analysis and scanning, plus NS2-based network simulation.

L-T-P: 0-0-3, 1.5 credits

Full Stack Development-2

(Skill Enhancement Course; alternative: SWAYAM Plus Data Engineer/AI Engineer track)

• Express.js fundamentals — routing, HTTP methods, and middleware.
• Sessions, cookies, authentication, and building RESTful APIs in Express.js.
• React.js fundamentals — components, JSX, props, state, and styling.
• Conditional rendering, list rendering, forms, and client-side routing in React.
• React hooks and inter-component data sharing, paired with MongoDB CRUD operations.

L-T-P: 0-1-2, 2 credits

User Interface Design using Flutter

• Installing the Flutter/Dart toolchain and writing introductory Dart programs.
• Exploring core widgets and layout structures (Row, Column, Stack).
• Designing responsive UIs with media queries and breakpoints.
• Implementing navigation, state management, and custom themed widgets.
• Building forms with validation, adding animations, and consuming REST APIs within the UI.

L-T-P: 0-0-2, 1 credit

The Community Service Project Internship completed over the prior summer is formally evaluated this semester for 2 credits.

Semester total: 15-1-10 contact hours, 23 credits. Students may additionally opt into a Minor specialization course (3-0-3, 4.5 credits), a NPTEL/SWAYAM minor course (3 credits), or Honors-pool courses (3 credits each).

JNTUK R23 B.Tech AI & ML III Year II Semester (3-2) Syllabus & Subject-wise Topics

by | Jul 11, 2026 | JNTUK R23 Syllabus

The second half of third year deepens the AI&ML core with Natural Language Processing, Deep Learning, and Data Visualization, alongside two further Professional Elective slots and a second Open Elective. Dedicated labs give hands-on practice in deep learning frameworks and visualization tooling, while Soft Skills and a Technical Paper Writing & IPR audit course build communication and research-documentation ability. A mandatory industry internship or mini-project follows over the summer.

Subjects

Natural Language Processing

• Unit 1: Origins and challenges of NLP, including grammar-based and statistical language modeling.
• Unit 2: Word-level analysis — N-gram models, their evaluation, and smoothing techniques.
• Unit 3: Syntactic analysis using context-free grammars and English grammar rules.
• Unit 4: Semantics and pragmatics — representation requirements and first-order logic approaches.
• Unit 5: Discourse analysis and lexical resource construction.

L-T-P: 3-0-0, 3 credits

Deep Learning

• Unit 1: Biological inspiration for neural computation and the McCulloch-Pitts unit.
• Unit 2: Feedforward networks, gradient descent, and backpropagation.
• Unit 3: Modern optimization methods for more effective neural network training.
• Unit 4: Recurrent neural networks, backpropagation through time, and LSTM units.
• Unit 5: Recent developments — variational autoencoders, transformers, and GPT-style applications across vision and language tasks.

L-T-P: 3-0-0, 3 credits

Data Visualization

• Unit 1: What visualization is, its history, and its relationship to related disciplines.
• Unit 2: Creating visual representations — reference models and visual mapping.
• Unit 3: Classifying visualization systems and interaction techniques, including common pitfalls like misleading charts.
• Unit 4: Visualizing groups, trees, graphs, clusters, and networks.
• Unit 5: Visualizing volumetric data, vector fields, and simulation processes.

L-T-P: 3-0-0, 3 credits

Professional Elective-II options:

• Software Testing Methodologies — covers the purpose of testing, transaction-flow testing, path-based testing techniques, state-graph-based transition testing, and graph-matrix approaches to test design.
• Cryptography & Network Security — covers core security principles, symmetric cryptography’s algebraic foundations, block ciphers (DES, AES, Blowfish), cryptographic hash functions, and transport/web-level security.
• DevOps — covers the SDLC-to-DevOps transition, source-code management with Git, build automation and continuous integration, continuous delivery practices, and configuration management with Ansible.
• Recommender Systems — covers recommender-system fundamentals, collaborative filtering, content-based recommendation, hybrid approaches, and methods for evaluating recommender quality.

L-T-P: 3-0-0, 3 credits (one option selected)

Professional Elective-III options:

• Software Project Management — covers conventional (waterfall) project management, lifecycle phases, model-based software architectures, project organization structures, and Agile/Scrum adoption.
• Mobile Adhoc Networks — covers ad hoc and cellular network fundamentals, MANET routing protocols, ad hoc network security, wireless sensor network basics, and WSN security/key management.
• Computer Vision — covers camera models and radiometry, linear filters and convolution, multi-view geometry and stereopsis, model-fitting segmentation (Hough transform), and geometric camera calibration.
• NoSQL Databases — covers the four major NoSQL database types, comparisons with relational databases, key/value and document stores (MongoDB), column-oriented stores (HBase), and Riak-based key/value databases.

L-T-P: 3-0-0, 3 credits (one option selected)

Open Elective-II is drawn from the cross-department elective pool in the same way as Open Elective-I.

Deep Learning Lab

• Building multi-layer perceptrons for MNIST digit classification.
• Designing networks for binary and multi-class text classification (IMDB, Reuters datasets).
• Predicting housing prices with a regression network on the Boston Housing dataset.
• Building convolutional neural networks for digit and image classification, including transfer learning with VGG16.
• Implementing word embeddings and a recurrent neural network for movie-review sentiment classification.

L-T-P: 0-0-3, 1.5 credits

Data Visualization Lab

• Visualizing datasets with histograms and line charts.
• Building bar charts and box plots across multiple datasets.
• Creating scatter plots, mosaic plots, and multi-variable scatter matrices.
• Producing map-based visualizations and heatmaps.
• Building correlograms and 3D visualizations for multivariate data.

L-T-P: 0-0-3, 1.5 credits

Soft Skills

(Skill Enhancement Course)

• Analytical thinking and listening skills, including self-introduction and structured talks.
• Self-management skills — anger, stress, and time management.
• Standard operating methods for communication — grammar, tenses, and pronunciation.
• Job-oriented skills — group discussions and resume preparation.
• Interpersonal relationships — their importance, types, and influencing factors.

L-T-P: 0-1-2, 2 credits

Technical Paper Writing & IPR

(Audit Course)

• Introduction to technical report writing and sentence construction.
• Drafting reports and handling illustrations/graphics.
• Proofreading and summarization practice.
• Using word-processing tools for structured report elements like tables of contents.
• The nature of intellectual property — patents, designs, trademarks, and copyright.

L-T-P: 2-0-0 (non-credit audit course)

A mandatory industry internship or mini-project of 8 weeks’ duration is undertaken during the following summer vacation.

Semester total: 20-1-8 contact hours, 23 credits, with optional Minor and Honors-pool courses available as in the prior semester.