| Date |
Topic |
Suggested Readings |
Reference |
Homework |
Jan.
7, 8 |
Introduction
Mathematics
Machine Learning Basics
|
Deep Learning Book
Chap. 2
Chap. 3
Chap. 5
|
|
|
Jan.
14, 15 |
Feedforward Neural Networks & Optimization Tricks
|
Deep Learning Book
Chap. 6
Chap. 7
Chap. 8
|
|
|
Jan.
21, 22 |
PyTorch part 1
PyTorch Part 2
|
Python Numpy Tutorial
Neural Network from Scratch
Dive into Deep Learning
|
|
Instruction
Colab
|
Jan.
28, 29 |
Convolutional Neural Networks & Recurrent Neural Networks
|
Deep Learning Book
Chap. 9
Chap. 10
|
ResNet
GRU
DenseNet
|
|
Feb.
4, 5 |
Natural Language Processing I
|
Word2Vec
|
SGNS
|
|
Feb.
11, 12 |
Natural Language Processing II
|
CNN for sentence classification
Seq2Seq
|
Tree LSTM
CNN Multi-task Learning
|
|
Feb.
18, 19 |
Q & A for Projects
|
|
|
Instruction
Kaggle
|
Mar.
4, 5 |
Natural Language Processing III
|
Attention Seq2Seq
Transformer
|
Memory Networks
SQuAD Dataset
GLUE Benchmark
|
|
Mar.
11, 12 |
Natural Language Processing IV
|
BERT
GPT-3
|
RoBERTa
|
|
Mar.
18, 19 |
Graph Representation Learning I
|
DeepWalk
LINE
|
Node2Vec
Metapath2Vec
LargeVis
|
|
Mar.
25, 26 |
Graph Representation Learning II
|
Graph Convolutional Networks
Graph Attention Networks
Neural Message Passing
Graph Isomorphism Network
|
GraphSAGE
PinSAGE
HAN
|
|
Apr.
1, 9 |
Recommender Systems
|
Bayesian Personalized Ranking
Factorization Machines
RNN-based recommendations
|
NCF
AutoInt
DGRec
|
|
Apr.
8, 16 |
Poster Sessions
|
|
|
|