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RGPV Notes | Machine Learning


Introduction to machine learning, scope and limitations of machine learning, regression in machine learning, probability, statistics and linear algebra for machine learning, convex optimization, data visualization, hypothesis function and testing, data distributions, data preprocessing, data augmentation, normalizing data sets, machine learning models, supervised machine learning and unsupervised machine learning.


Linearity vs non linearity, activation functions like sigmoid, ReLU, etc., weights and bias, loss function, gradient descent, multilayer network, backpropagation, weight initialization, training, testing, unstable gradient problem, auto encoders, batch normalization, dropout, L1 and L2 regularization, momentum, tuning hyper parameters,


Convolutional neural network, flattening, subsampling, padding, stride, convolution layer, pooling layer, loss layer, dance layer 1×1 convolution, inception network, input channels, transfer learning, one shot learning, dimension reductions, implementation of CNN like tensor flow, keras etc.


Recurrent neural network, Long short-term memory, gated recurrent unit, translation, beam search and width, Bleu score, attention model, Reinforcement Learning, RL framework, MDP, Bellman equations, Value Iteration and Policy Iteration, , Actor-critic model, Q learning, SARSA.


Support Vector Machines, Bayesian learning, application of machine learning in computer vision, speech processing, natural language processing etc, Case Study: ImageNet Competition.


  1. Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer Verlag New York Inc., 2nd Edition, 2011.
  2. Tom M. Mitchell, “Machine Learning”, McGraw Hill Education, First edition, 2017.
  3. Ian Goodfellow and Yoshua Bengio and Aaron Courville, “Deep Learning”, MIT Press, 2016


  1. Aurelien Geon, “Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems”, Shroff/O’Reilly; First edition (2017).
  2. Francois Chollet, “Deep Learning with Python”, Manning Publications, 1 edition (10 January 2018).
  3. Andreas Muller, “Introduction to Machine Learning with Python: A Guide for Data Scientists”, Shroff/O’Reilly; First edition (2016).
  4. Russell, S. and Norvig, N. “Artificial Intelligence: A Modern Approach”, Prentice Hall Series in Artificial Intelligence. 2003.


Different problems to be framed to enable students to understand the concept learnt and get hands-on on various tools and software related to the subject. Such assignments are to be framed for ten to twelve lab sessions.