GNEST 305 Introduction to Artificial Intelligence and Data Science KTU BTech S3 2024 scheme

 

SEMESTER S3

INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND DATA SCIENCE

 

Course Code

GNEST305

CIE Marks

40

Teaching Hours/Week (L: T:P: R)

 

3:1:0:0

 

ESE Marks

 

60

Credits

4

Exam Hours

2Hrs.30Min.

Prerequisites(ifany)

None

CourseT ype

Theory

Course Objectives:

1.       Demonstrate a solid understanding of advanced linear algebra concepts, machine learning algorithmsandstatisticalanalysistechniquesrelevanttoengineeringapplications,principles and algorithms.

2.       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.

SYLLABUS

 

Module No.

Syllabus Description

Contact Hours

 

 

1

Introduction to AI and Machine Learning: Basics of Machine Learning - types of Machine Learning 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 problems usingMLP.(Text-2)

 

 

 

 

11

2

Mathematical Foundations of AI and Datascience:Role of linear algebra in Data representation and analysis–Matrix decomposition-Singular Value

Decomposition (SVD)- Spectral decomposition- Dimensionality reduction technique-Principal Component Analysis (PCA). (Text-1)

 

 

11

 

 

3

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)(Textbook4)

 

 

11

 

 

4

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.(For visualization the software tools like Tableau, PowerBI, R or Python can be used. For Machine Learning implementation,Python,MATLAB or R canbe

used.)(Textbook-5)

 

 

 

11

 

Course Assessment Method (CIE: 40 marks, ESE: 60 marks)

 

 Continuous Internal Evaluation Marks(CIE):

 

 

Attendance

 

Assignment/ Microproject

Internal Examination-1 (Written)

Internal Examination-2 (Written )

 

Total

5

15

10

10

40

End Semester Examination Marks(ESE)

In Part A, all questions need to be answered and in Part B, each student can choose any one full question out of two questions

 

Part A

Part B

Total

·        2 Questions from each module.

·        Total of 8 Questions, each carrying 3marks

 

(8x3=24marks)

·         Each question carries 9marks.

·         Two questions will be given from each module, out of which 1 question should be answered.

·         Each question can have a maximum of 3 sub divisions.

(4x9=36marks)

 

 

 

60

Course Outcomes(COs)

At the end of the course students should be able to:

 

 

Course Outcome

Bloom’s Knowledge Level(KL)

 

CO1

Apply the concept of machine learning algorithms including neural networks and supervised/unsupervised learning techniques for engineering applications.

K3

 

CO2

Apply advanced mathematical concepts such as matrix operations,

Singular values, and principal component analysis to analyze and solve engineering problems.

K3

 

CO3

Analyze and interpret data using statistical methods including descriptive statistics, correlation, and regression analysis to derive

Meaningful insights and make informed decisions.

K3

CO4

Integrate statistical approaches and machine learning techniques to

Ensure practically feasible solutions in engineering contexts.

K3

Note:K1-Remember,K2-Understand,K3-Apply,K4-Analyse,K5-Evaluate,K6-Create

 

 

CO-PO Mapping Table:

 

 

PO1

PO2

PO3

PO4

PO5

PO6

PO7

PO8

PO9

PO10

PO11

PO12

CO1

3

3

3

3

 

 

 

 

 

 

 

 

CO2

3

3

3

3

 

 

 

 

 

 

 

 

CO3

3

3

3

3

 

 

 

 

 

 

 

 

CO4

3

3

3

3

 

 

 

 

 

 

 

 



Text Books

Sl.No

Title of the Book

Name of the Author/s

Name of the Publisher

Edition

and Year

1

Introduction to Linear Algebra

Gilbert Strang

Wellesley- Cambridge

Press

6thedition, 2023

2

Hands-on machine learning with Scikit-Learn, Keras, and

TensorFlow

Aurélien Géron

O'ReillyMedia,Inc.

2nd

edition,202 2

 

3

Mathematics for machinelearning

Deisenroth,Marc Peter,

A.AldoFaisal,and Cheng Soon Ong

Cambridge

University Press

1stedition.

2020

4

Fundamentals of mathematical statistics

Gupta,S.C.,andV. K.

Kapoor

Sultan Chand&Sons

9thedition, 2020

5

Introducing data science:big data, machine learning, and more, using Python tools

Cielen,Davy,and

ArnoMeysman

 

Simonand Schuster

1st

edition

,2016

 

 

Reference Books

Sl. No

Title of the Book

Name of the Author/s

Name of the Publisher

Edition and Year

 

1

 

Datascience: concepts and practice

Kotu, Vijay,and Bala

Deshpande

 

Morgan Kaufmann

 

2ndedition,2018

2

Probability and Statistics for Data Science

Carlos Fernandez- Granda

Center for Data ScienceinNYU

1stedition,2017

 

3

 

Foundations of  DataScience

Avrim Blum,John Hopcroft, and Ravi

Kannan

 

Cambridge University Press

 

1stedition,2020

4

Statistics For DataScience

JamesD. Miller

PacktPublishing

1stedition,2019

 

5

 

Probability and Statistics-The Science of Uncertainty

MichaelJ. Evansand Jeffrey S.

Rosenthal

 

UniversityofToronto

 

1stedition,2009

 

6

An Introduction to the Science of Statistics:From Theory to Implementation

 

JosephC. Watkins

chrome- extension://efaidnbmn nnibpcajpcglclefindm

kaj/https://www.math. arizo

 

Preliminary Edition.

 

Video Links( NPTEL, SWAYAM…)

Module No.

LinkID

1

https://archive.nptel.ac.in/courses/106/106/106106198/

 

2

https://archive.nptel.ac.in/courses/106/106/106106198/

https://ocw.mit.edu/courses/18-06-linear-algebra-spring-2010/resources/lecture-29-singular-value-decomposition/

3

https://ocw.mit.edu/courses/18-650-statistics-for-applications-fall-2016/resources/lecture-19-video/

4

https://archive.nptel.ac.in/courses/106/106/106106198/

 

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