Machine Learning and Deep Learning - Fundamentals and Applications

By Prof. M. K. Bhuyan | IIT Guwahati

Learners enrolled: 13917 | Exam registration: 2790

ABOUT THE COURSE:

In this course we will start with traditional Machine Learning approaches, e.g. Bayesian Classification, Multilayer Perceptron etc. and then move to modern Deep Learning architectures like Convolutional Neural Networks, Autoencoders etc. We will learn about the building blocks used in these Deep Learning based solutions. Specifically, we will learn about feedforward neural networks, convolutional neural networks, recurrent neural networks and attention mechanisms. On completion of the course students will acquire the knowledge of applying Machine and Deep Learning techniques to solve various real-life problems.

INTENDED AUDIENCE: UG, PG and PhD students and industry professionals who want to work in Machine and Deep Learning.

PREREQUISITES: Knowledge of Linear Algebra, Probability and Random Process, PDE will be helpful.

INDUSTRY SUPPORT: This is a very important course for industry professionals.

Summary

Course Status :	Completed
Course Type :	Core
Language for course content :	English
Duration :	12 weeks
Category :	Computer Science and Engineering Electrical, Electronics and Communications Engineering Communication and Signal Processing
Credit Points :	3
Level :	Undergraduate/Postgraduate
Start Date :	22 Jul 2024
End Date :	11 Oct 2024
Enrollment Ends :	05 Aug 2024
Exam Registration Ends :	16 Aug 2024
Exam Date :	03 Nov 2024 IST

Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.

Page Visits

Course layout

Week 1: Introduction
Introduction to ML, Performance Measures, Bias-Variance Trade off, Linear Regression.

Week 2: Bayes Decision Theory
Bayes Decision Theory, Normal Density and Discriminant Function, Bayes Decision Theory - Binary Features, Bayesian Belief Network

Week 3: Parametric and Non- Parametric Density Estimation
Parametric and Non- Parametric Density Estimation – ML and Bayesian Estimation, Parzen Window and KNN

Week 4:Perceptron Criteria and Discriminative Models
Perceptron Criteria, Discriminative models, Support Vector Machines (SVM)

Week 5: Logistic Regression, Decision Trees and Hidden Markov Model
Logistic Regression, Decision trees, Hidden Markov Model (HMM)

Week 6: Ensemble methods
Ensemble methods: Ensemble strategies, boosting and bagging, Random Forest

Week 7: Dimensionality Problem
Dimensionality Problem, Principal Component Analysis (PCA), Linear Discriminant Analysis (LDA)

Week 8: Mixture Model and Clustering
Concept of mixture model, Gaussian mixture model, Expectation Maximization Algorithm, K- means clustering.

Week 9: Clustering
Fuzzy K-means clustering, Hierarchical Agglomerative Clustering, Mean-shift clustering.

Week 10: Neural Network
Neural network: Perceptron, multilayer network, backpropagation, RBF Neural Network, Applications

Week 11: Introduction to Deep Neural Networks
Introduction to Deep Learning, Convolutional Neural Networks (CNN),
Vanishing and Exploding Gradients in Deep Neural Networks, LeNet - 5, AlexNet, VGGNet, GoogleNet, and ResNet.

Week 12: Recent Trends in Deep Learning
Generative Adversarial Networks (GAN), Auto Encoders and Relation to PCA, Recurrent Neural Networks, U-Net, Applications and Case studies.

Books and references

1. E. Alpaydin, Introduction to Machine Learning, 3rd Edition, Prentice Hall (India) 2015.

2. R. O. Duda, P. E. Hart and D. G. Stork, Pattern Classification, 2nd Edn., Wiley India, 2007.

3. C. M. Bishop, Pattern Recognition and Machine Learning (Information Science and Statistics),Springer, 2006.

4. M.K. Bhuyan, Computer Vision and Image Processing: Fundamentals and Applications, published by CRC press, USA, 2019.

5. S. O. Haykin, Neural Networks and Learning Machines, 3rd Edition, Pearson Education (India), 2016.

6. Ian Goodfellow, Yoshua Bengio and Aaron Courville, Deep Learning, MIT Press, 2016

7. Michael A. Nielsen, Neural Networks and Deep Learning , Determination Press, 2015

8. Yoshua Bengio, Learning Deep Architectures for AI, now Publishers Inc., 2009

Instructor bio

Prof. M. K. Bhuyan

IIT Guwahati

Prof. Manas Kamal Bhuyan received a Ph.D. degree in electronics and communication engineering from the India Institute of Technology (IIT) Guwahati, India. He was with the School of Information Technology and Electrical Engineering, University of Queensland, St. Lucia, QLD, Australia, where he was involved in postdoctoral research. Subsequently, he was a Researcher with the SAFE Sensor Research Group, NICTA, Brisbane, QLD, Australia. He was an Assistant Professor with the Department of Electrical Engineering, IIT Roorkee, India and Jorhat Engineering College, Assam, India, and he also worked in Indian Engineering Services. In the year 2014, he was a Visiting Professor with Indiana University and Purdue University, Indiana, USA. Dr. Bhuyan was a recipient of the National Award for Best Applied Research/Technological Innovation , which was presented by the Honorable President of India in the year 2012, the Prestigious Fullbright-Nehru Academic and Professional Excellence Fellowship, and the BOYSCAST Fellowship. He is an IEEE senior member. He is currently a Professor with the Department of Electronics and Electrical Engineering, IIT Guwahati, and Dean of Infrastructure, Planning and Management, IIT Guwahati. He is also currently working as a Visiting Professor, Department of Computer Science, Chubu University, Japan. His current research interests include Machine Learning and Artificial Intelligence, Image/Video Processing, Computer Vision, Human Computer Interactions (HCI), Virtual Reality & Augmented Reality, and Biomedical Signal Processing. He has almost 27 years of industry, teaching, and research experience. He is the author of the text book "Computer Vision and Image Processing: Fundamentals and Applications", published by CRC press, USA, 2019.

Professor, Department of Electronics & Electrical Engineering, IIT Guwahati, Assam, INDIA,
Dean of Infrastructure Planning and Management (IPM), IIT Guwahati, Assam, INDIA,
Visiting Professor, Department of Computer Science, Chubu University, JAPAN.
Fulbright Scholar.

http://www.iitg.ac.in/mkb

http://www.cvl.cs.chubu.ac.jp/member/2022/index-e.html

Course certificate

The course is free to enroll and learn from. But if you want a certificate, you have to register and write the proctored exam conducted by us in person at any of the designated exam centres.
The exam is optional for a fee of Rs 1000/- (Rupees one thousand only).
Date and Time of Exams: 03 November 2024 Morning session 9am to 12 noon; Afternoon Session 2pm to 5pm.
Registration url: Announcements will be made when the registration form is open for registrations.
The online registration form has to be filled and the certification exam fee needs to be paid. More details will be made available when the exam registration form is published. If there are any changes, it will be mentioned then.
Please check the form for more details on the cities where the exams will be held, the conditions you agree to when you fill the form etc.

CRITERIA TO GET A CERTIFICATE

Average assignment score = 25% of average of best 8 assignments out of the total 12 assignments given in the course.
Exam score = 75% of the proctored certification exam score out of 100

Final score = Average assignment score + Exam score

YOU WILL BE ELIGIBLE FOR A CERTIFICATE ONLY IF AVERAGE ASSIGNMENT SCORE >=10/25 AND EXAM SCORE >= 30/75. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >= 40/100.

Certificate will have your name, photograph and the score in the final exam with the breakup.It will have the logos of NPTEL and IIT Guwahati .It will be e-verifiable at nptel.ac.in/noc.

Only the e-certificate will be made available. Hard copies will not be dispatched.

Once again, thanks for your interest in our online courses and certification. Happy learning.

- NPTEL team

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