Schedule

All course materials are placed in this OneDrive folder (password: See Piazza)

Week	Dates	Topics	HW
1	03/02	L00: Introduction to Course L01-1: Basics of Probability Theory    (caliberation)
	05/02	L01-2: Basics of Information Theory
		Tutorial 1: Pytorch Basics (video inside folder)  Colab
Part 1: Foundation of Machine Learning
2	10/02	L02: Linear Regression, Polynomial regression, Model capacity, underfitting, overfitting, regularization L03
	12/02		WA1 out
3	17/02	L03: Logistic regression, softmax regression (probabilities of classes), gradient descent, optimization approach to classification (where to put decision boundary)	HA1 out
	19/02	L04: Generative Models for Classification and Naïve Bayes (how data are generated) L05
4	24/02	L05: The Bias-Variance Decomposition, variance reduction (regularization, bagging), bias reductio (boosting) L06	WA1 due WA2 out
Part 2: Foundation of Deep Learning
4	26/02	L06: Feedforward Neural Networks, backprop, dropout, optimization in deep learning TensorFlow Playground, OptAlgo	HA1 due HA2 out
		Tutorial 2: FNN in PyTorch (video inside folder) Colab (upload data)
5	03/03	L06 L07: Convolutional Neural Network, parameter sharing, batch normalization, residual connections Computation at Convolutional Layer, CNN not like human
	05/03	L07	HA2 due HA3/4 out
	06/03	Tutorial 3: CNN in PyTorch Colab (CIFAR10, ResNet) Live by Jason LI, 3pm, Zoom
6	10/03	L08: Recurrent Neural Networks, next token prediction, LSTM, layer normalization, seq2seq model, teacher forcing, attention in seq2seq model Seq2seq Attention in RNN	WA2 due HA5 out
		Tutorial 4: RNN in PyTorch (video) Colab (upload data.zip from OneDrive)
Part 3: Introduction to Advanced Deep Learning
6	12/03	L08 L09-1: Transformer Models and BERT Transformer: Demo Demo code Self-attention in PyTorch	WA3 out
		Tutorial 5: BERT (OneDrive)  Google Colab (upload data)
7	17/03	L09-1	HA3/4 due HA6 out
	19/03	L09-2: GPT and Introduction of LLM L10	HA5 due HA-x out
	7:00pm 19/03	HA-x Info Session, https://hkust.zoom.us/j/95539602373?pwd=B9t9ykhLoJIlCwXm6hsRr6RIlhmapw.1
		Tutorial 6: CLIP (OneDirve) GoogleColab
8	24/03	L10: Vision Transformers and VLM	HA7 out
Part 4: Generative Models for Images
8	26/03	L11: Variational Autoencoders	HA6 due
		Tutorial 7: GAN & VAE    Colab
9	31/03	L12: Generative Adversarial Networks GAN Zoo	WA3 due HA8 out WA 4 out
	07/04	L12 L13-1: Diffusion Models	HA-x due
	09/04	L13-1: Diffusion Models (I) Stable Diffusion 2.0, Stable Diffusion 2.1 Demo	HA7 due HA9 out
	14/04	Tutorial 8: Diffusion Modles Colab 14/04, 19:00pm, Live tutorial: Zoom
10	14/04	L13-2: Diffusion Models (II)	HA8 due
Part 5: Reinforcement Learning
10	16/04	L13-2 L14:  Introduction to Reinforcement Learning Hide and Seek,  QLearning
11	21/04	Public holiday
	23/04	L14 DQN-Demo	HA9 due
	23/04	Tutorial 9: Q-Lerning 15:00pm, Live tutorial: Zoom
12	28/04	L14: L15: Value-Based Deep RL	HA10 out
		Tutorial 10: DQN
	30/04	L14 L16: Policy-based Deep RL
		Tutorial 11: A2C in Pytorch
13	05/05	Public holiday
	07/05	L16: Policy-based Deep RL Live tutorial on XAI	WA4 due HA10 due
	20/05	Final Exam: 20/5/2025, Tuesday 12:30PM - 3:30PM Lecture Theater A (401)

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* Submissions are to be made by 11:59pm on the due date.