Machine Learning

GANPAT UNIVERSITY

FACULTY OF ENGINEERING AND TECHNOLOGY

Programme

Bachelor of Technology

Branch/Spec.

Computer Science & Engineering (BDA/CBA)

Semester

VII

Version

1.1.0.2

Effective from Academic Year

2022-23

Effective for the batch Admitted in

June 2019

Subject code

2CSE702

Subject Name

MACHINE LEARNING

Teaching scheme

Examination scheme (Marks)

(Per week)

Lecture (DT)

Practical (Lab.)

Total

CE

SEE

Total

L

TU

P

TW

Credit

2

0

1

0

3

Theory

40

60

100

Hours

2

0

2

0

4

Practical

30

20

50

Pre-requisites:

Probability theory and Bayesian Concept Learning, Statistics and data science, Algorithm analysis and design, Data Mining.

Learning Outcome:

After successful completion of the course, students will be able to

• Learn various machine learning approaches
• Learn different dimensionality reduction techniques
• Apply theoretical foundations of decision trees to identify best split and Bayesian classifier to label data points.
• Apply various classifier models like SVM, Neural Networks and identify classifier models for typical machine learning applications.

Theory syllabus

Unit

Content

Hrs

1

Introduction:

Machine Learning Foundations: Design of a Learning system - Types of machine learning, Applications of machine learning.  

1

2

Supervised Learning:

Regression algorithms, Classification algorithms

Unsupervised Learning:

Clustering algorithms

9

3

Dimensionality Reduction

Introduction, Feature Selection and Feature Extraction, Principal Component Analysis, Decision Tree algorithms, Ensemble methods

5

4

Neural Network:

Neural Networks - Introduction, Perceptron Learning, Backpropagation, Initialization, Training & Validation, Parameter Estimation - MLE, MAP, Bayesian Estimation.

5

5

Convolutional Neural Networks (CNN): Introduction, Convolutional Neural Network (ConvNet/CNN), Evolution and Convolution Operation in CNN, Architecture of CNN, Convolution Layer, Activation Function (ReLU), Pooling Layer, Fully Connected Layer, Dropout

5

6

Sequential Networks: Sequence modeling using RNNs, Backpropagation through time, Long Short-Term Memory (LSTM), Bidirectional LSTMs

5

Self-Study:

LeNet-5, AlexNet, ZFNet, VGGNet, GoogleNet, ResNet.

Practical contents

Practicals shall be based on supervised, unsupervised learning and deep learning

Mooc Course

Course Name: Introduction to Machine Learning

Link:

https://www.edx.org/course/machine-learning-with-python-a-practical-introduct

https://www.coursera.org/learn/machine-learning-with-python

Text Books

1

Christopher Bishop, “Pattern Recognition and Machine Learning” Springer.

2

Kevin P. Murphy, “Machine Learning: A Probabilistic Perspective”, MIT Press.

Reference Books

1

EthemAlpaydin, “Introduction to Machine Learning”, MIT Press.

2

Tom Mitchell, "Machine Learning", McGraw-Hill.

3

Trevor Hastie, Robert Tibshirani, Jerome Friedman, "The Elements of Statistical Learning", Springer.

4

Stephen  Marsland,  “Machine  Learning  -  An  Algorithmic  Perspective”,  Chapman  and Hall/CRC Press.

Course Outcomes:

COs

Description

CO1

Learn various machine learning approaches

CO2

Learn different dimensionality reduction techniques

CO3

Apply theoretical foundations of decision trees to identify best split and Bayesian classifier to label data points.

CO4

Apply various classifier models like SVM, Neural Networks and identify classifier models for typical machine learning applications.

Mapping of CO and PO: