GUJARAT TECHNOLOGICAL UNIVERSITY
Bachelor of Engineering
Subject Code: 3170924
Semester – VII
Subject Name: AI and Machine learning
Type of course: Professional Elective Course
Prerequisite: Linear Algebra, Probability
Rationale:
Artificial Intelligence is now a day used in nontechnical and technical fields. In every branch of engineering
the AI has been used and Electrical engineering is also one of them infect, the use of AI technique in Electrical
engineering is inevitable. The branches of electrical Engineering like Electrical Power Systems, Power
Electronics and Smart grid technologies are some of them. The course is aimed to provide exposure about the
fundamentals of AI techniques and use of some basic machine learning algorithms to be used in electrical
engineering and in other branches of electrical engineering too; the commonly used AI techniques from the
application viewpoints will be covered in the this course.
Teaching and Examination Scheme:
Teaching Scheme Credits Examination Marks Total
L T P C Theory Marks Practical Marks Marks
ESE PA ESE PA
(E) (M) Viva (V) (I)
3 0 0 3 70 30 0 0 100
Contents:
Sr. No. Content Total
Hrs
1 Introduction: Scope of the Course, Introduction to AI, Brief review of History 03
of AI, Related fields
2 Introduction to Artificial Neural Networks: Biological Neurons and 07
Biological Neural Networks, Artificial Neural Networks, Activation Functions,
Perceptron NN, Multilayer Perceptron NN, Back-propagation Neural Networks,
Training Methods, Basic definition of supervised and unsupervised Learning.
3 Introduction to Machine Learning: Introduction (Different Types of Learning) 02
Hypothesis Space, Inductive Bias, Evaluation and Cross Validation
4 Main Algorithms used in Machine Learning: Linear Regression, Decision 08
Trees, Learning Decision Trees, K-nearest Neighbour, Collaborative Filtering,
Overfitting, Dimensionality Reduction Technique :Feature Selection, Feature
Extraction
5 Logistic Regression and Support Vector Machine: 06
Logistic Regression, Introduction to Support Vector Machine, The Dual
Formation, Maximum Margin with Noise, Nonlinear SVM and Kernel Function,
SVM: Solution to the Dual Problem
6 Advanced Learning methods and Clustering: Introduction to Clustering, K- 06
means Clustering, Agglomerative Hierarchical Clustering, Basics of Semi-
Supervised and Reinforcement Learning, Introduction to Deep Learning
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w.e.f. AY 2018-19
GUJARAT TECHNOLOGICAL UNIVERSITY
Bachelor of Engineering
Subject Code: 3170924
7 . Fuzzy Logic Introduction , Conventional set vs fuzzy set, Operations of fuzzy 06
set , Membership function, Fuzzy rules, Fuzzy inference, De-fuzzification,,
Application for control
8 Genetic algorithm Introduction, Comparison with traditional optimisation 07
Technique, Steps for GA, reproduction, Crossover, Mutation, Termination
parameter of GA, Application.
Suggested Specification table with Marks (Theory): (For BE only)
Distribution of Theory Marks
R Level U Level A Level N Level E Level C Level
10 25 30 15 10 10
Legends: R: Remembrance; U: Understanding; A: Application, N: Analyze and E: Evaluate C: Create
and above Levels (Revised Bloom’s Taxonomy)
Note: This specification table shall be treated as a general guideline for students and teachers. The actual
distribution of marks in the question paper may vary slightly from above table.
Reference Books:
1. Machine Learning with Python for Everyone, Mark Fenner, Pearson
2. Machine Learning, Anuradha Srinivasaraghavan, Vincy Joseph, Wiley
3. Machine Learning with Python, U Dinesh Kumar Manaranjan Pradhan, Wiley
4. Neural Networks, Fuzzy Logic, and Genetic Algorithms : Synthesis and Applications By S.
Rajshekharan, G. A. Vijayalakshmi Pai, PHI
5. KishanMehrotra, Chilukuri Mohan and Sanjay Ranka, Elements of Artificial Neural Networks,
Penram International
6. Tom Mitchell, Machine Learning, TMH
7. AthemEalpaydin, Introduction to Machine Learning, PHI
8. Andries P. Engelbrecht, Computational Intelligence - An Introduction, Wiley Publication
Course Outcomes:
After completing the course, students will be able to;
Sr. CO statement Marks %
No. weightage
CO-1 Learn the basic concepts of how to use various AI techniques. 25
CO-2 Learn, realize and implement various basic machine learning algorithms. 25
CO-3 Learn the appropriateness and steps to use fuzzy systems and Genetic algorithm 25
for engineering problem solving
CO-4 Comprehend basic concepts of Neural network and use of machine learning for 25
training
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w.e.f. AY 2018-19
GUJARAT TECHNOLOGICAL UNIVERSITY
Bachelor of Engineering
Subject Code: 3170924
List of Open Source Software/learning website:
1. https://nptel.ac.in/
2. https://www.coursera.org/
3. https://www.geeksforgeeks.org/machine-learning/
4. https://www.tutorialspoint.com/machine_learning_with_python/index.htm
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