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CS-702 (B) · Deep & Reinforcement Learning/Official Syllabus

Deep & Reinforcement Learning (CS-702 (B)) - Official Syllabus

1
Unit 1

History of Deep Learning

  • History of Deep Learning
  • McCulloch Pitts Neuron
  • Thresholding Logic
  • Activation functions
  • Gradient Descent (GD)
  • Momentum Based GD
  • Nesterov Accelerated GD
  • Stochastic GD
  • AdaGrad
  • RMSProp
  • Adam
  • Eigenvalue Decomposition
  • Recurrent Neural Networks
  • Backpropagation through time (BPTT)
  • Vanishing and Exploding Gradients
  • Truncated BPTT
  • GRU
  • LSTMs
  • Encoder Decoder Models
  • Attention Mechanism
  • Attention over images
2
Unit 2

Autoencoders and relation to PCA

  • Autoencoders and relation to PCA
  • Regularization in autoencoders
  • Denoising autoencoders
  • Sparse autoencoders
  • Contractive autoencoders
  • Regularization: Bias Variance Tradeoff
  • L2 regularization
  • Early stopping
  • Dataset augmentation
  • Parameter sharing and tying
  • Injecting noise at input
  • Ensemble methods
  • Dropout
  • Batch Normalization
  • Instance Normalization
  • Group Normalization
3
Unit 3

Greedy Layerwise Pre-training

  • Greedy Layerwise Pre-training
  • Better activation functions
  • Better weight initialization methods
  • Learning Vectorial Representations Of Words
  • Convolutional Neural Networks
  • LeNet
  • AlexNet
  • ZF-Net
  • VGGNet
  • GoogLeNet
  • ResNet
  • Visualizing Convolutional Neural Networks
  • Guided Backpropagation
  • Deep Dream
  • Deep Art
  • Recent Trends in Deep Learning Architectures
4
Unit 4

Introduction to reinforcement learning (RL)

  • Introduction to reinforcement learning (RL)
  • 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
5
Unit 5

Fitted Q, Deep Q-Learning

  • Fitted Q
  • Deep Q-Learning
  • Advanced Q-learning algorithms
  • Learning policies by imitating optimal controllers
  • DQN & Policy Gradient
  • Policy Gradient Algorithms for Full RL
  • Hierarchical RL
  • POMDPs
  • Actor-Critic Method
  • Inverse reinforcement learning
  • Maximum Entropy Deep Inverse Reinforcement Learning
  • Generative Adversarial Imitation Learning
  • Recent Trends in RL Architectures

Reference Books

  • Deep Learning, An MIT Press book — Ian Goodfellow, Yoshua Bengio, Aaron Courville
  • Pattern Classification — Richard O. Duda, Peter E. Hart, David G. Stork
  • Reinforcement Learning: An Introduction — Sutton, Barto
  • Reinforcement Learning: State-of-the-Art — Marco Wiering, Martijn van Otterlo
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