1. Lectures
  2. Important Dates


The slides and resources for each lecture will be updated after the class. The schedule below is tentative and both the order and the content may change as the semester goes along.

Date Lecture
7-Jan Introduction
9-Jan Review: Supervised learning and loss minimization
14-Jan Multiclass classification
16-Jan Neural Networks and computation graphs
21-Jan MLK Day. No class.
23-Jan A short introduction to Tensorflow
28-Jan Word Embeddings
30-Jan Word Embeddings (continued)
4-Feb Predicting Sequences: Recurrent Networks
6-Feb Snow day. No class!
11-Feb Recurrent networks (continued)
13-Feb Recurrent networks (continued)
18-Feb Presidents’ Day. No class.
20-Feb Long short-term memory networks and variants
25-Feb LSTMs continued
  Vanishing gradient revisited: Highway/Residual connections
27-Feb Application: Semantic Roles
4-Mar Application: Language models and more
6-Mar ELMo and BERT: Contextual word embeddings
11-Mar Spring Break. No class
13-Mar Spring Break. No class
18-Mar Convolutional Neural Networks for NLP
20-Mar Project discussions
25-Mar Project discussions (continued)
27-Mar Sequence-to-sequence networks
1-Apr Applications: Machine Translation
3-Apr Modeling attention
8-Apr Attention (continued)
10-Apr Applications: Reading Comprehension and Textual Entailment
15-Apr Transformer Networks
17-Apr Structures and neural networks
22-Apr Looking back and forward: Final words
26-Apr Project presentations during final exam (10:30 AM - 12:30 PM)

Important dates

Date Deadline
27-Jan Project team information due
28-Jan Start signing up for class presentations
13-Feb Project proposals due
20-Feb Assignment 1 due
20-Mar Project intermediate status report due
25-Mar Assignment 2 due
10-Apr Assignment 3 due
22-Apr Project poster email to Chirs
26-Apr Project poster session in class
28-Apr Project final report due