KTU 2024 Scheme · Semester 3 · Mechanical Engineering

Introduction to Artificial Intelligence and Data Science (GNEST305) Syllabus

Official KTU 2024 Scheme syllabus for Introduction to Artificial Intelligence and Data Science, Semester 3, Mechanical Engineering (Mechanical Engineering).

This page compiles APJ Abdul Kalam Technological University's officially published 2024 Scheme syllabus for Mechanical Engineering, Semester 3, sourced directly from KTU's official website (ktu.edu.in). Learnizo is an independent online tuition platform and is not affiliated with, endorsed by, or officially connected to APJKTU. The university may revise syllabus content after this page was last updated — always cross-check with the official KTU source for the current, authoritative version.

Course Code

GNEST305

Credits

Teaching Hours

3:1:0:0 (L:T:P:R)

CIE Marks

ESE Marks

Exam Duration

2 Hrs 30 Min

Prerequisites

None

Semester

Semester 3

Course Objective

To demonstrate a solid understanding of advanced linear algebra concepts, machine learning algorithms and statistical analysis techniques relevant to engineering applications, and to apply theoretical concepts to solve practical engineering problems, analyze data to extract meaningful insights, and implement appropriate mathematical and computational techniques for AI and data science applications.

Module-wise Syllabus

Module 1

11 contact hours

Introduction to AI and Machine Learning: basics of machine learning, types of ML systems, challenges in ML. Supervised learning model example — regression models, classification model example (logistic regression). Unsupervised model example — K-means clustering. Artificial Neural Network: perceptron, Universal Approximation Theorem (statement only), multi-layer perceptron, deep neural network, demonstration of regression and classification using MLP.

Module 2

11 contact hours

Mathematical Foundations of AI and Data Science: role of linear algebra in data representation and analysis, matrix decomposition — Singular Value Decomposition (SVD), spectral decomposition, dimensionality reduction technique — Principal Component Analysis (PCA).

Module 3

11 contact hours

Applied Probability and Statistics for AI and Data Science: basics of probability, random variables and statistical measures, rules in probability, Bayes theorem and its applications, statistical estimation — Maximum Likelihood Estimator (MLE), statistical summaries, correlation analysis (linear correlation, direct problems only), regression analysis — linear regression using least square method.

Module 4

11 contact hours

Basics of Data Science: benefits of data science, use of statistics and machine learning in data science, data science process, applications of machine learning in data science, modelling process, demonstration of ML applications in data science, Big Data and Data Science (visualization tools such as Tableau, PowerBI, R or Python; ML implementation using Python, MATLAB or R).

Course Outcomes

CO1Apply the concept of machine learning algorithms including neural networks and supervised/unsupervised learning techniques for engineering applications.
CO2Apply advanced mathematical concepts such as matrix operations, singular values, and principal component analysis to analyze and solve engineering problems.
CO3Analyze and interpret data using statistical methods including descriptive statistics, correlation, and regression analysis to derive meaningful insights and make informed decisions.
CO4Integrate statistical approaches and machine learning techniques to ensure practically feasible solutions in engineering contexts.

Assessment Pattern (CIE: 40 marks, ESE: 60 marks)

Continuous Internal Evaluation (CIE)

Attendance	5
Assignment / Microproject	15
Internal Examination 1 (Written)	10
Internal Examination 2 (Written)	10

End Semester Examination (ESE)

Total 60 marks, 2 Hrs 30 Min. See the official KTU syllabus document for the exact Part A / Part B question pattern for this course.

Textbooks & Reference Books

Textbooks

Introduction to Linear Algebra — Gilbert Strang (Wellesley-Cambridge Press, 6th edition, 2023)
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow — Aurelien Geron (O'Reilly Media, Inc., 2nd edition, 2022)
Mathematics for Machine Learning — Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong (Cambridge University Press, 1st edition, 2020)
Fundamentals of Mathematical Statistics — Gupta, S. C., and V. K. Kapoor (Sultan Chand & Sons, 2020)
Introducing Data Science: Big Data, Machine Learning, and More, Using Python Tools — Cielen, Davy, and Arno Meysman (Simon and Schuster, 1st edition, 2016)

Reference Books

Data Science: Concepts and Practice — Kotu, Vijay, and Bala Deshpande (Morgan Kaufmann, 2nd edition, 2018)
Probability and Statistics for Data Science — Carlos Fernandez-Granda (Center for Data Science in NYU, 1st edition, 2017)
Foundations of Data Science — Avrim Blum, John Hopcroft, and Ravi Kannan (Cambridge University Press, 1st edition, 2020)
Statistics For Data Science — James D. Miller (Packt Publishing, 1st edition, 2019)
Probability and Statistics - The Science of Uncertainty — Michael J. Evans and Jeffrey S. Rosenthal (University of Toronto, 1st edition, 2009)
An Introduction to the Science of Statistics: From Theory to Implementation — Joseph C. Watkins (—, Preliminary edition)

Frequently Asked Questions

How many credits is KTU Introduction to Artificial Intelligence and Data Science (GNEST305)?

4 credits, with 3:1:0:0 (L:T:P:R) teaching hours per week, under the KTU 2024 Scheme.

How many modules are in the GNEST305 syllabus?

4 modules, 44 total contact hours.

What is the CIE and ESE mark split for this course?

CIE (Continuous Internal Evaluation): 40 marks. ESE (End Semester Examination): 60 marks, 2 Hrs 30 Min. Total: 100 marks.

What are the recommended textbooks for GNEST305?

Introduction to Linear Algebra (Gilbert Strang); Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (Aurelien Geron); Mathematics for Machine Learning (Deisenroth, Marc Peter, A. Aldo Faisal, and Cheng Soon Ong); Fundamentals of Mathematical Statistics (Gupta, S. C., and V. K. Kapoor); Introducing Data Science: Big Data, Machine Learning, and More, Using Python Tools (Cielen, Davy, and Arno Meysman).

Is this syllabus specific to one branch, or common to others too?

This Semester 3 course is listed under Mechanical Engineering at KTU under the 2024 Scheme — check the course header above for which branches it's common to.

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