Madhavan Mukund

Advanced Machine Learning,
Sep–Dec 2021

Advanced Machine Learning

Sep–Dec, 2021

Administrative details

  • Instructors: Pranabendu Misra, Madhavan Mukund

  • Lectures and Tutorials: Two live online classes each week. Tutorials as needed.

  • Evaluation:

    • Quizzes on Moodle, midsemester exam, programming assignments, final exam

    • Weightage approximately 10%, 20%, 30%, 40%, to be confirmed

    • Copying is fatal

  • Text and reference books:

    • Avrim Blum, John Hopcroft and Ravi Kannan: Foundations of Data Science, Cambridge University Press 2021 Available Here

    • Ian Goodfellow, Yoshua Bengio and Aaron Courville: Deep Learning, MIT Press 2016 Available Here

    • Richard S. Sutton and Andrew G. Barto: Reinforcement Learning: An Introduction, MIT Press (2nd ed) 2018 Available Here

    • Aurélien Géron: Hands-On Machine Learning with Scikit-Learn, Keras and Tensorflow (2nd ed), O'Reilly 2019

    • Francois Chollet: Deep Learning with Python, Manning Publications 2017

    • Nikhil Buduma: Fundamentals of Deep Learning, O'Reilly 2017

    • Stuart J Russell and Peter Norvig: Artificial Intelligence: A Modern Approach, Pearson (3rd ed) 2016

    • Daphne Koller and Nir Friedman: Probabilistic Graphical Models – Principles and Techniques, MIT Press 2009

Course plan

This list is approximate and subject to change.

  • Deep Learning Philosophy, Deep Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks, LSTM, Autoencoders, Generative Adversarial Networks
  • Gaussian Process Regression
  • Bayesian optimization
  • Reinforcement Learning
  • Probabilistic Graphical Models and Causality