Schedule for In-class Presentations

Date	Topic	Suggested Readings	Reference	Homework
Jan. 5,6	Introduction (Eng) Mathematics (Eng) ML Basics (Eng) Introduction (Fr) Mathematics (Fr) ML Basics (Fr)	Deep Learning Book Chap. 2 Chap. 3 Chap. 5
Jan. 12,13	Feedforward Neural Networks & Optimization Tricks (Eng) Feedforward Neural Networks & Optimization Tricks (Fr)	Deep Learning Book Chap. 6 Chap. 7 Chap. 8
Jan. 19,20	PyTorch (Eng) Notebook Part I (Eng) Notebook Part II (Eng) PyTorch (Fr) Notebook (Fr)	Python Numpy Tutorial Neural Network from Scratch Dive into Deep Learning
Jan. 26,27	Convolutional Neural Networks & Recurrent Neural Networks (Eng) Convolutional Neural Networks & Recurrent Neural Networks (Fr)	Deep Learning Book Chap. 9 Chap. 10	ResNet GRU DenseNet
Feb. 2,3	Natural Language Processing I (Eng) Natural Language Processing I (Fr)	Word2Vec	SGNS
Feb. 9,10	Natural Language Processing II (Eng) Natural Language Processing II (Fr)	CNN for sentence classification Seq2Seq	Tree LSTM CNN Multi-task Learning	Instruction (Eng) Instruction (Fr)
Feb. 16,17	Q & A for Projects
Mar. 2,3	Natural Language Processing III (PDF, Eng) Natural Language Processing III (PPT/Keynote, Eng) Natural Language Processing III (PDF, Fr) Natural Language Processing III (PPT/KetNote, Fr)	Attention Seq2Seq Transformer	Memory Networks SQuAD Dataset GLUE Benchmark
Mar. 9,10	Natural Language Processing IV (Eng) Natural Language Processing IV (Fr)	BERT GPT-3	RoBERTa	Instruction (Eng & Fr)
Mar. 16,17	Graph Representation Learning I (Eng) Graph Representation Learning I (Fr)	DeepWalk LINE	Node2Vec Metapath2Vec LargeVis
Mar. 23,24	Graph Representation Learning II (Eng) Graph Representation Learning II (Fr)	Graph Convolutional Networks Graph Attention Networks Neural Message Passing Graph Isomorphism Network	GraphSAGE PinSAGE HAN
Marh. 30,31	AI for drug discovery (Eng)
Apr. 6,7	Poster Sessions