Description:

This course is about deep learning. Students taking this course will learn the theories, models, algorithms, and recent progress of deep learning. The course starts with machine learning basics and classical neural network models, followed by deep convolutional neural networks, recurrent neural networks, reinforcement learning, etc., and their applications. Students are expected to have good knowledge of calculus, linear algebra, probability and statistics as prerequisites.

This website contains only information of Part 2 (the second half of the course).

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Textbooks (The textbooks are used loosely):

• Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville.
• Dive into Deep Learning, Zhang et al.

Instructor: Robby T. Tan

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Teaching Schedule:

The following schedule will not be strictly followed.

Date	Topic	Lecture Note
February 28	1. CONVOLUTIONAL NEURAL NETWORK (CNN)	Lecture 1 Lecture 1 with annotation
March 7	2. RECURRENT NEURAL NETWORKS AND TRANSFORMER	Lecture 2 Lecture 2 with annotation
March 14	3. TRANSFORMER + DEEP GENERATIVE NETWORKS (AUTOENCODER)	Lecture 3 CEG5304: CA 3 and CA 4 EE6934: CA 3 + Essay
March 21	4. DEEP GENERATIVE NETWORKS: VAE + DIFFUSION MODELS	Lecture 3 with annotation
March 28	5. GAN + NERF + REINFORCEMENT LEARNING	See "Lecture 3 with annotatation"
April 4	6. REINFORCEMENT LEARNING: MDP + Q-LEARNING + POLICY GRADIENTS	Lecture 6 with annotation
April 11	7. BAYESIAN DEEP LEARNING	Lecture 7

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Syllabus:

• CNN, architectures, image classification, object detection, domain adaptation
• Deep Generative Networks: Autoencoders, UNets, variational autoencoders (VAE), generative adversarial networks (GAN), pix2pix, cycleGAN, recent developments and applications
• RNN, LSTM, Attention, Transformer, Vision Transformer, recent developments and applications
• Deep Reinforcement Learning, Q-Learning, DQN, Policy Gradient, Value Function, Actor-Critics, applications
• Bayesian Neural Network (BNN), Bayesian deep learning, applications
• Advanced topics: few shot, imbalanced classes, out of distribution, etc.

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