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AL-702 (B) · Advance Machine Learning/Official Syllabus

Advance Machine Learning (AL-702 (B)) - Official Syllabus

1
Unit 1

Artificial Neural Network

  • Introduction to ANN
  • Perceptron
  • Cost Function
  • Gradient Checking
  • multi-layer perceptron and backpropagation algorithm that is used to help learn parameters for a neural network
  • Random Initialization
2
Unit 2

Decision Trees

  • Representing concepts as decision trees
  • Recursive induction of decision trees
  • best splitting attribute: entropy and information gain
  • Searching for simple trees and computational complexity
  • Overfitting, noisy data, and pruning
3
Unit 3

Ensemble Methods

  • Bagging
  • boosting
  • stacking and learning with ensembles
  • Random Forest
4
Unit 4

Introduction to reinforcement learning(RL)

  • Reinforcement Learning
  • RL-framework
  • MDP
  • Bellman equations
  • Value Iteration and Policy Iteration
  • Actor-critic model
  • Q-learning
  • SARSA
  • Bandit algorithms UCB
  • PAC,Median Elimination
  • Policy Gradient
  • Full RL & MDPs, Bellman Optimality
5
Unit 5

Dynamic Programming

  • Value iteration, Policy iteration, and Q-learning & Temporal Difference Methods
  • Temporal-Difference Learning
  • Eligibility Traces
  • Function Approximation
  • Least Squares Methods,Fitted Q
  • Deep Q-Learning
  • Advanced Q-learning algorithms
  • Inverse reinforcement learning
  • Deep Inverse Reinforcement Learning, Generative Adversarial Imitation Learning
  • Recent Trends in RL Architectures

Reference Books

  • Pattern Recognition and Machine Learning
  • Machine Learning
  • Introduction to Machine Learning
  • Machine Learning
  • Introduction to Machine Learning, 2e
  • Reinforcement Learning: An Introduction
  • Reinforcement Learning: State-of-the-Art
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