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CS-601 · Machine Learning/Official Syllabus

Machine Learning (CS-601) - Official Syllabus

1
Unit 1

Introduction to machine learning

  • Introduction to machine learning
  • scope and limitations
  • regression
  • probability
  • statistics
  • 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 learning
  • unsupervised learning
2
Unit 2

Linearity vs non linearity

  • Linearity vs non linearity
  • activation functions like sigmoid
  • ReLU
  • weights and bias
  • loss function
  • gradient descent
  • multilayer network
  • backpropagation
  • weight initialization
  • training
  • testing
  • unstable gradient problem
  • auto encoders
  • batch normalization
  • dropout
  • L1 regularization
  • L2 regularization
  • momentum
  • tuning hyper parameters
3
Unit 3

Convolutional neural network

  • 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
4
Unit 4

Recurrent neural network

  • 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
5
Unit 5

Support Vector Machines

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

Reference Books

  • Hands-On Machine Learning with Scikit-Learn and Tensorflow: Concepts, Tools, and Techniques to Build Intelligent Systems — Aurelien Geon
  • Deep Learning with Python — Francois Chollet
  • Introduction to Machine Learning with Python: A Guide for Data Scientists — Andreas Muller
  • Artificial Intelligence: A Modern Approach — S. Russell, N. Norvig
  • Pattern Recognition and Machine Learning — Christopher M. Bishop
  • Machine Learning — Tom M. Mitchell
  • Deep Learning — Ian Goodfellow, Yoshua Bengio, Aaron Courville
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