Applicable from AY 2022-23 Batch - B.Tech. Computer Science and Engineering (AI & ML) Course Structure & I Year to IV Year Syllabus

Sep 2, 2026

Course structure and syllabus for B.Tech. in Computer Science and Engineering (AI & ML) under JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD, applicable from the AY 2022-23 batch. Covers curriculum organization across I year to IV year semesters and lists open and professional elective offerings, including Fundamentals of AI, Machine Learning Basics, NLP, Chatbots, Genetic Algorithms, Fuzzy logic, and related guidance. Includes detailed unit-wise outlines for core subjects such as Matrices and Calculus and Engineering Chemistry, specifying learning objectives, outcomes, and assessment notes.

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B.Tech Computer Science and Engineering (AI & ML) - Course Structure & Syllabus

Applicable from AY 2022-23 Batch

Professional and Open Electives

Matrices and Calculus

Engineering Chemistry

JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD B.Tech. in COMPUTER SCIENCE AND ENGINEERING (AI & ML) COURSE STRUCTURE, I & II YEAR SYLLABUS (R22 Regulations) Applicable from AY 2022-23 Batch I YEAR I SEMESTER I YEAR IISEMESTER II YEAR I SEMESTER YEAR II SEMESTER YEAR I SEMESTER YEAR II SEMESTER Environmental Science in III Yr II Sem Should be Registered by Lateral Entry Students Only. IV YEAR I SEMESTER IV YEAR II SEMESTER *MC – Satisfactory/Unsatisfactory Professional Elective-I Professional Elective - II Professional Elective - III # Courses in PE - III and PE - III Lab must be in 1-1 correspondence. Professional Elective -IV Professional Elective - V Professional Elective – VI Open Elective I: Fundamentals of AI Machine Learning Basics Open Elective II: Introduction to Natural Language Processing AI applications Open Elective III: Chatbots Genetic Algorithms & Fuzzy logic MATRICES AND CALCULUS B.Tech. I Year I Sem. L T P C 3 1 0 4 Pre-requisites: Mathematical Knowledge at pre-university level Course Objectives: To learn Types of matrices and their properties. Concept of a rank of the matrix and applying this concept to know the consistency and solving the system of linear equations. Concept of eigenvalues and eigenvectors and to reduce the quadratic form to canonical form Geometrical approach to the mean value theorems and their application to the mathematical problems Evaluation of surface areas and volumes of revolutions of curves. Evaluation of improper integrals using Beta and Gamma functions. Partial differentiation, concept of total derivative Finding maxima and minima of function of two and three variables.  Evaluation of multiple integrals and their applications Course outcomes: After learning the contents of this paper the student must be able to Write the matrix representation of a set of linear equations and to analyse the solution of the system of equations Find the Eigenvalues and Eigen vectors Reduce the quadratic form to canonical form using orthogonal transformations. Solve the applications on the mean value theorems. Evaluate the improper integrals using Beta and Gamma functions Find the extreme values of functions of two variables with/ without constraints. Evaluate the multiple integrals and apply the concept to find areas, volumes UNIT - I: Matrices 10 L Rank of a matrix by Echelon form and Normal form, Inverse of Non-singular matrices by Gauss-Jordan method, System of linear equations: Solving system of Homogeneous and Non-Homogeneous equations by Gauss elimination method, Gauss Seidel Iteration Method. UNIT - II: Eigen values and Eigen vectors 10 L Linear Transformation and Orthogonal Transformation: Eigenvalues, Eigenvectors and their properties, Diagonalization of a matrix, Cayley-Hamilton Theorem (without proof), finding inverse and power of a matrix by Cayley-Hamilton Theorem, Quadratic forms and Nature of the Quadratic Forms, Reduction of Quadratic form to canonical forms by Orthogonal Transformation. UNIT - III: Calculus 10 L Mean value theorems: Rolle’s theorem, Lagrange’s Mean value theorem with their Geometrical Interpretation and applications, Cauchy’s Mean value Theorem, Taylor’s Series. Applications of definite integrals to evaluate surface areas and volumes of revolutions of curves (Only in Cartesian coordinates), Definition of Improper Integral: Beta and Gamma functions and their applications. UNIT - IV: Multivariable Calculus (Partial Differentiation and applications) 10 L Definitions of Limit and continuity. Partial Differentiation: Euler’s Theorem, Total derivative, Jacobian, Functional dependence & independence. Applications: Maxima and minima of functions of two variables and three variables using method of Lagrange multipliers. UNIT-V: Multivariable Calculus (Integration) 8 L Evaluation of Double Integrals (Cartesian and polar coordinates), change of order of integration (only Carte

FAQs

What electives are listed under Open Electives for the AI & ML program?

Open Electives include Fundamentals of AI, Machine Learning Basics, Introduction to Natural Language Processing (with AI applications), Chatbots, and Genetic Algorithms & Fuzzy logic.

What topics are covered in the Matrices and Calculus course units?

Units cover matrices and their rank, eigenvalues and eigenvectors with diagonalization, calculus including mean value theorems and Taylor’s series, multivariable calculus with partial differentiation and Lagrange multipliers, and integration for double/triple integrals with applications.

Which core syllabus details are specified for Engineering Chemistry?

Engineering Chemistry includes course objectives and course outcomes, followed by unit-wise themes starting with Water and its treatment (including hardness of water) and further engineering chemistry concepts.

Sep 2, 2026

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