Syllabus - Introduction to Machine Learning (AB 604 (C))

Automation and Robotics

Introduction to Machine Learning (AB 604 (C))

VI-Semester

Unit - I

Introduction to machine learning

Scope and limitations, regression, 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 and unsupervised learning.

Unit - II

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.

Unit - III

Convolutional neural network

Flattening, subsampling, padding, stride, convolution layer, pooling layer, loss layer, dance layer 1x1 convolution, inception network, input channels, transfer learning, one shot learning, dimension reductions, implementation of CNN like tensor flow

Unit - IV

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

Unit - V

Support Vector Machines

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

Course Outcome

After Completing the course student should be able to: 1. Apply knowledge of computing and mathematics to machine learning problems, models and algorithms; 2. Analyze a problem and identify the computing requirements appropriate for its solution; 3. Design, implement, and evaluate an algorithm to meet desired needs; and 4. Apply mathematical foundations, algorithmic principles, and computer science theory to the modeling and design of computer-based systems in a way that demonstrates comprehension of the trade-offs involved in design choices.

Practicals

Reference Books

  • Christopher M. Bishop, “Pattern Recognition and Machine Learning”, Springer-Verlag New York Inc., 2nd Edition, 2011.

  • Tom M. Mitchell, “Machine Learning”, McGraw Hill Education, First edition, 2017.

  • Ian Goodfellow and Yoshua Bengio and Aaron Courville, “Deep Learning”, MIT Press, 2016

  • 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).

  • Francois Chollet, "Deep Learning with Python", Manning Publications, 1 edition (10 January 2018).

  • Andreas Muller, "Introduction to Machine Learning with Python: A Guide for Data Scientists", Shroff/O'Reilly; First edition (2016).

  • Russell, S. and Norvig, N. “Artificial Intelligence: A Modern Approach”, Prentice Hall Series in Artificial Intelligence. 2003