Machine Learning (CS-601)
COURSE OUTCOMES:
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
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.
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B.Tech RGPV notes AICTE flexible curricula Bachelor of technology
Syllabus
UNIT 1:
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 2:
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 3:
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, keras etc.
UNIT 4:
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 5:
Support Vector Machines, Bayesian learning, application of machine learning in computer
vision, speech processing, natural language processing etc, Case Study: ImageNet Competition
NOTES
- Unit 1
- Unit 2
- Unit 3
- Unit 4
- Unit 5
TEXT BOOKS RECOMMENDED:
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
PRACTICAL:
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.
Books Recommended
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
