AI61002 | Spring 2019

Announcements	Deep Learning Foundations and Applications	Quick Links
Coming up in an OBE format! OBE is outcome-based education according to Washington Accord 1 Jan. 2019* Classes start on Wed, 9 Jan. 2019. Open for UG3, UG4, PG1 and MS/PhD only. Limited to 200 seats, apply through ERP within 2nd Jan 2019. Shortlisting would be declared on the evening of 3rd Jan 2019. UG students to apply on ERP and await approvals. PG and DualDegree students to apply with hard copy application using this form. MS/PhD to apply directly through ERP as Recommended or Additional coursework subject only. As a matter of inclusiveness and fairness policy with seat limitations we are not able to acknowledge audit requests. Students are expected to be proficient in Python programming.	AI61002 Spring 2019 Subject Type: Elective \| LTP: 3-1-0 \| Credits: 4 Location: NR121, Nalanda Lecture Hall Complex, IIT Kharagpur Time: Slot E / Wed (12:00 PM - 12:55 AM) + Thu (11:00 AM - 11:55 AM) + Fri (9:00 AM - 10:55 AM) Instructors: Dr. Debdoot Sheet, Dr. Abir Das, Dr. Pawan Goyal TAs: Ram Rakesh, Ankit Singh, Omprakash Chakraborty, Rachana Sathish Grading: Attendance 10%, Quizzes 10%, Coding Assignments 20%, Mid-Term 25%, End-Term 35%	Linear Algebra - Gilbert Strang A Gentle Introduction to Programming Using Python - Sarina Canelake Design and Analysis of Algorithms - Dana Moshkovitz and Bruce Tidor Tools of the Trade: Anaconda Python 3.6 \| PyTorch \| Getting started with PyTorch Related MOOCs: Deep Learning for Visual Computing \| Github repository List of Shortlisted Candidates

Why this subject?	Snapshot
This subject aims to provide students with foundational concepts required for deep learning which is now prevalent across various applications ranging across speech and natural language processing to machine vision to medical imaging. The course will introduce the fundamental principles of deep neural networks and the important paradigms of deep learning. On having studied this subject a student is expected to be able to build analytics solutions to problems in signal, image and text paradigm using deep neural networks. The course will focus on demonstrating different applications of deep neural networks to a number of examples across domains including computer vision and NLP. The course will contain tutorials which will focus on hands-on session and implementation of deep neural networks and applications that use them. Students on completion are expected to be able to understand the concepts of deep neural networks and will be able to develop solutions using deep neural networks. Text books: [1]. I. Goodfellow, Y, Bengio, A. Courville, Deep Learning, MIT Press, 2016. [2]. S. Haykin, Neural Networks and Learning Machines, 3rd Edition, Pearson, 2008. Reference books: [R1]. C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995. [R2]. R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, 2nd Edition, Wiley, 2001. [R3]. D. Cohen-Or, C. Greif, T. Ju, N. J. Mitra, A. Shamir, O. Sorkine-Hornung, H. Zhang, A Sampler of Useful Computational Tools for Applied Geometry, Computer Graphics and Image Processing, CRC Press, 2015. [R4]. T. M. Mitchell, Machine Learning, Mc. Graw Hill Education, 1997. [R5]. C.M. Bishop, Pattern Recognition and Machine Learning, 2nd Edition, Springer, 2011. [R6]. S. Russel and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall/Pearson, 2015. Measure of Outcome: A student undertaking this subject would be graded based on perfromance in all of the following: (1) Regular participation in class activity. (2) Timely submission of all quizzes, online assignments. (3) Participation in tutorials in class. (4) Appear for all the exams. (5) Also attend the practice tutorials and workshops.	Foundational concepts of linear algebra, probability, neural networks and learning theory Familiarity with software toolkits and deep learning libraries Convolutional and recurrent neural networks Regularization and learning concepts Basics of dataset handling for deep learning Deep learning for machine translations and text summarization Building chatbots with cognitive capability Deep learning for image classification, scene understanding Semantic segmentation and single shot multibox detection Medical image classification, compression and super resolution Deep learning for Digital Pathology and Digital Radiology

Why this subject?

Snapshot

This subject aims to provide students with foundational concepts required for deep learning which is now prevalent across various applications ranging across speech and natural language processing to machine vision to medical imaging. The course will introduce the fundamental principles of deep neural networks and the important paradigms of deep learning. On having studied this subject a student is expected to be able to build analytics solutions to problems in signal, image and text paradigm using deep neural networks.

The course will focus on demonstrating different applications of deep neural networks to a number of examples across domains including computer vision and NLP. The course will contain tutorials which will focus on hands-on session and implementation of deep neural networks and applications that use them. Students on completion are expected to be able to understand the concepts of deep neural networks and will be able to develop solutions using deep neural networks.

Text books:
[1]. I. Goodfellow, Y, Bengio, A. Courville, Deep Learning, MIT Press, 2016.
[2]. S. Haykin, Neural Networks and Learning Machines, 3rd Edition, Pearson, 2008.

Reference books:
[R1]. C. M. Bishop, Neural Networks for Pattern Recognition, Oxford University Press, 1995.
[R2]. R. O. Duda, P. E. Hart, D. G. Stork, Pattern Classification, 2nd Edition, Wiley, 2001.
[R3]. D. Cohen-Or, C. Greif, T. Ju, N. J. Mitra, A. Shamir, O. Sorkine-Hornung, H. Zhang, A Sampler of Useful Computational Tools for Applied Geometry, Computer Graphics and Image Processing, CRC Press, 2015.
[R4]. T. M. Mitchell, Machine Learning, Mc. Graw Hill Education, 1997.
[R5]. C.M. Bishop, Pattern Recognition and Machine Learning, 2nd Edition, Springer, 2011.
[R6]. S. Russel and P. Norvig, Artificial Intelligence: A Modern Approach, 3rd Edition, Prentice Hall/Pearson, 2015.

Measure of Outcome:
A student undertaking this subject would be graded based on perfromance in all of the following:
(1) Regular participation in class activity.
(2) Timely submission of all quizzes, online assignments.
(3) Participation in tutorials in class.
(4) Appear for all the exams.
(5) Also attend the practice tutorials and workshops.

Foundational concepts of linear algebra, probability, neural networks and learning theory
Familiarity with software toolkits and deep learning libraries
Convolutional and recurrent neural networks
Regularization and learning concepts
Basics of dataset handling for deep learning
Deep learning for machine translations and text summarization
Building chatbots with cognitive capability
Deep learning for image classification, scene understanding
Semantic segmentation and single shot multibox detection
Medical image classification, compression and super resolution
Deep learning for Digital Pathology and Digital Radiology