Course Schedule

Unless otherwise specified, the course lectures and meeting times are:

[Slides in one bulk] (.zip file from chapter 1 to 7)

Event TypeDateDescriptionCourse Materials
Lecture short Jan 10 Introduction to deep learning I
Course info, syllabus, tutorials schedule, etc.
[slides]
Lecture long Jan 12 Introduction to deep learning II
Applications in computer vision
[deep learning]
research survey published in Nature, 2015
Lecture short Jan 17 Machine learning basics I
Classification, regression, capacity, overfit/underfit
[slides]
Lecture long Jan 19 Machine learning basics II
Discriminative and generative model
[notes on machine learning]
course original blog
Lecture short Jan 24 Machine learning basics III
PCA
[chapter of machine learning]
deep learning textbook
Lectue long Jan 26 Multilayer neural network
Class cancelled; make-up will be announced later
Neural Nets notes 1
Neural Nets notes 2
Neural Nets notes 3
tips/tricks: [1], [2], [3] (optional)
Feb 2 Lunar New Year Vacation; no class
Lecture short Feb 7 Multilayer neural network I
History, three-layer NN, BP
[slides]
Lecture long Feb 9 Multilayer neural network II
SGD, learning curve, BP on flow graph
[notes on backpropagation]
course original blog
Lecture long Feb 11 Convolutional neural networks (CNN) I
CNN layer, pooling, LRN, typical architecture
Make-up class for Jan 26
[slides]
time: 10:30 - 12:15
venue: ERB 407 (next to Ho Sin Hang Engineering Bldg.)
Lecture short Feb 14 Convolutional neural networks (CNN) II
BP of CNN, case study: AlexNet of ImageNet 2012
gradient flow in convolution: [1] [2]
extension reading
Lecture long Feb 16 Optimization for Training Deep Models I
Local minimum/maximum, Jacobian matrix, data augmentation
[slides]
Lecture short Feb 21 Optimization for Training Deep Models II
Gradient vanishing, batch normalization, data/model parallelism
[notes on init and normalization]
course original blog
A1 Feb 21
confirmed
Assignment #1 Due date
Cross entropy, multi-layer neural network, equivariance
[assignment #1] [solution]
Release date: Jan 25. Grades out: Mar 7.
TA-in-charge: Tong Xiao
Lecture long Feb 23 Network structures I [slides]
Exam short Feb 28
confirmed
Quiz 1
Coverage from Introduction (Jan 10) to Network Structures (Feb 23)
time: 2:30 - 3:13 pm
venue: YIA LT5
Grades out: Mar 7 [Solutions]
TA-in-charge: Wei Yang
Lecture long Mar 2 Network structures II

Lecture short Mar 7 Recurrent Neural Networks and LSTM I
Language models, Image captioning
[slides]
Lecture long Mar 9 Recurrent Neural Networks and LSTM II DL book RNN chapter (optional)
min-char-rnn, char-rnn, neuraltalk2
Lecture short Mar 14 Deep Blief Nets I
[slides]
Lecture long Mar 16 Deep Blief Nets II
Lecture short Mar 21 Generative adversarial network (GAN) I [slides]
A2 Mar 21
confirmed
Assignment #2 Due date
CNN, optimization, PyTorch programming
[assignment #2] [solution]
Release date: Feb 15. Grades out: TBD.
TA-in-charge: Hongyang Li
Lecture long Mar 23 Generative adversarial network (GAN) II [NIPS 2016 GAN workshop]
[Tutorial for starters]
Milestone Mar 24 Discussion on final project proposal
Held in the tutorial session
Lecture short Mar 28 Auto-encoder and Deep reinforcement learning I
[autoencoder slides]
Lecture long Mar 30 Deep reinforcement learning II
Q-value network, DQN, Continuous actor-critic algorithm, deterministic policy.
[slides]
[NIPS 2016 Deep RL workshop]
[DeepMind DQN Nature paper]
Lecture long Apr 6 Structured deep learning [slides]
A3 Apr 6
confirmed
Assignment #3 Due date
RBM and auto-encoder
[assignment #3] [solution](updated on Apr 9)
Release date: Mar 27. Grades out: TBD.
TA-in-charge: Wei Yang
Lecture short Apr 11 Course sum-up
Apr 13 No class as scheduled
Exam long Apr 20 Quiz 2
Coverage from RNN (Mar 7) to Structured DL (Apr 6)
time: 14:30 - 16:15
venue: LSK LT7
Milestone May 2
confirmed
Poster and presentation session
Poster: Floor 6, Reading Room, SHB
Presentation: TBD.
[link]