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Subject Syllabus

AL702 (B) - Advance Machine Learning

AIML · Semester 7

Course Modules

Learn topic-wise with Hyper AI and revise using mapped PYQs.

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

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

Bagging, boosting, stacking and learning with ensembles Random Forest

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

Dynamic Programming - Value iteration, Policy iteration 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