The winter course on machine intelligence and brain research offers credits for selected IIT M students who are undertaking this course. The course is available for all students from any department in IIT M. The selection criteria for this course is based on the (a)current CGPA of the student (b) Interest in brain research and machine intelligence as demonstrated by a small write up (c) Potential to do a project under the Faculty and Staff of CCBR.

This is a short duration course and invitations for expressions of interest will be open soon following which selected students based on the selection criteria will be offered an offer of acceptance. Please note, due to the short duration of the course, once accepted, withdrawl from the course is only possible within 2 days of acceptance. Failiure to attend the lectures or completion of assignemnts and evaluation means a "U" or "W" grade


To understand the fundamentals of research areas, where Neuroscience, Machine learning and Engineering interact such as Vision, Audition, Natural Language and Reinforcement learning.

This interdisciplinary course will consist of lectures and hands on tutorials and is based on the premise that a two-way interchange between neuroscience and machine learning/AI will be mutually productive. The Lectures will be conducted by the faculty of IIT Madras, interacting with scientists at the Center for Computational Brain Research as well as world renowned international experts visiting the center during the course. The course focuses on four main areas of robust interactions between engineering and neuroscience (Vision, Speech/Audition, Natural Language and Reinforcement Learning) and also includes related topics (classical Machine Learning, Hardware and Statistical Physics).

Course Contents:

Module 1: Vision

  • Classical Computer/Machine vision:

    • Feature Extraction: Canny, Corners - Harris & Hessian Affine;

    • Image Segmentation: Graph-Cut;

    • Motion Analysis: - Tracking (MOG), Optical Flow.

    • Deep learning techniques for:

    • Object recognition and detection,

    • Depth estimation,

    • Video based activity recognition.

Module 2: Audition

  • The Neurobiology of Speech and Hearing,

  • Machine learning in Audition.

Module 3: Reinforcement Learning (RL)

  • Reinforcement Learning: Rewards and returns,

  • Markov decision process,

  • Dynamic programming,

  • Temporal difference learning,

  • Function approximation, policy gradient approach.

Module 4: Natural Language Processing (NLP)

  • Challenges in NLP,An intuitive overview of the role of Machine Learning (ML) in NLP tasks like:

    • Concept-level Information Retrieval,

    • Word Sense Disambiguation (WSD),

    • Probabilistic Parsing,

    • Machine Translation.


Additional Topics: Topics in ML

  • Singular Value Decomposition of matrices encoding the Pointwise Mutual Information between words,

  • Skip gram models for learning word representations,

  • Global Vectors for words,

  • Studying the equivalence between SVD and the newer neural network based methods.


    Neuroanatomy of the brain: organizational principles, development and evolution;

  • Microcircuits and Mesocircuits;

  • Action potentials, synapses, neurotransmitters and modulators.


Laboratory and Tutorials

Fundamentals of Neurobiology Laboratory: Brain dissection; Demonstration of electrophysiological recordings;

Fundamentals of Machine Learning and machine Vision Laboratory


Evaluation methodology (only over modules above):

(a)Students have to read assigned papers and submit reports/scribe notes (30% Marks);

(b)Wet and computer lab assignments, (as teams), (40% Marks)

(c)Tutorials and online quizzes – 30%

Special Invited talks by renowned CCBR (Dr. Partha Mitra, Dr. Mriganka Sur, Dr. Anand Raghunathan) and other International scientists will be given on related areas, such as:

a:Visual Neuroscience; Brain Activity Mapping; Neurotechnologies

b:Hardware for artificial neural networks and machine learning


Recommended Text Books:

Deep Learning; Ian Goodfellow and Yoshua Bengio and Aaron Courville, An MIT Press book, 2016.

Computer Vision: Algorithms and Applications; Richard Szeliski, Springer- Verlag London Limited, 2011.

Reinforcement Learning: An Introduction; R. S. Sutton and A. G. Barto, MIT Press, 1st Edn., 1998.

Neuroscience; Dale Purves; [5th Edition], Sinauer Associates, Inc., USA, 2012.

Speech and Language Processing; Daniel Jurafsky, James H. Martin; Prentice Hall Series in Artificial Intelligence; 2nd Edn., 2013.

Reference Books:

ISL - An Introduction to Statistical Learning with Applications in R; Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani. Springer. 2013 (7th printing 2017).

Computational Neuroscience of Vision; by Edmund Rolls, Gustavo Deco; Oxford University Press, 1st edition, 2002.

Information, Physics and Computation; Marc Mezard and Andrea Montanari, Oxford University Press, 1st Edn., 2009.

Brain Architecture – Understanding the basic plan; Larry Swanson, Oxford University Press, 2nd Edn., 2012.

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