Schedule

Schedule for In-class Presentations

The schedule for in-class presentations is available at the link.

Date	Topic	Suggested Readings	Reference for Presentations	Homework
1 / 9, 10	Introduction Mathematics Machine Learning Basics	Deep Learning Book Chap. 2 Chap. 3 Chap. 5
1 / 16, 17	Feedforward Neural Networks & Optimization Tricks	Deep Learning Book Chap. 6 Chap. 7 Chap. 8
1 / 23, 24	PyTorch	Python Numpy Tutorial Neural Network from Scratch Dive into Deep Learning
1 / 30, 31	Convolutional Neural Networks & Recurrent Neural Networks	Deep Learning Book Chap. 9 Chap. 10	ResNet GRU DenseNet	Instruction Colab PDF version
2 / 6, 7	Natural Language Processing I	Word2Vec	GloVe
2 / 13, 14	Natural Language Processing II	CNN for sentence classification Seq2Seq	Tree LSTM CNN Multi-task Learning
2 / 20, 21	Q & A for Projects			Instruction Kaggle
2 / 27, 28	*No class*
3 / 5, 6	Natural Language Processing III	Attention Seq2Seq Transformer	Memory Networks
3 / 12, 13	Natural Language Processing IV	BERT	SQuAD Dataset GLUE Benchmark
3 / 19, 20	Graph Representation Learning I	DeepWalk LINE	Node2Vec Metapath2Vec LargeVis
3 / 26, 27	Graph Representation Learning II	Graph Convolutional Networks Graph Attention Networks Neural Message Passing	GraphSAGE PinSAGE HAN	Instruction Colab PDF version
4 / 2, 3	Recommender Systems	Bayesian Personalized Ranking Factorization Machines RNN-based recommendations	NCF AutoInt DGRec