Deep Structured Learning (IST, Fall 2018)

Summary

Structured prediction is a framework in machine learning which deals with structured and highly interdependent output variables, with applications in natural language processing, computer vision, computational biology, and signal processing. In the last 5 years, several applications in these areas achieved new breakthroughs by replacing the traditional feature-based linear models by more powerful deep learning models based on neural networks, capable of learning internal representations.

In this course, I will describe methods, models, and algorithms for structured prediction, ranging from "shallow" linear models (hidden Markov models, conditional random fields, structured support vector machines) to modern deep learning models (convolutional networks, recurrent neural networks, attention mechanisms, etc.), passing through shallow and deep methods akin to reinforcement learning. Representation learning will also be discussed (PCA, auto-encoders, and various deep generative models). The theoretical concepts taught in this course will be complemented by a strong practical component, by letting students work in group projects where they can solve practical problems by using software suitable for deep learning (e.g., Pytorch, TensorFlow, DyNet).


Course Information


Grading


Project Examples

The course project is an opportunity for you to explore an interesting problem using a real-world dataset. You can either choose one of our suggested projects or pick your own topic (the latter is encouraged). We encourage you to discuss your project with TAs/instructors to get feedback on your ideas.

Team: Projects can be done by a team of 2-4 students. You may use Piazza to find potential team mates.

Milestones: There are 3 deliverables:

All reports should be in NIPS format. There will be a class presentation and (tentatively) a poster session, where you can present your work to the peers, instructors, and other community members who will stop by.

See here for a list of project ideas.


Recommended Bibliography


Schedule

Date Topic Optional Reading
Sep 19 Introduction and Course Description Goodfellow et al. Ch. 1-5
Murphy Ch. 1-2
Sep 26 Linear Classifiers Murphy Ch. 3, 6, 8-9, 14 HW1 is out!
Oct 3 Feedforward Neural Networks Goodfellow et al. Ch. 6
Oct 10 Neural Network Toolkits
Guest lecture: Erick Fonseca
Goodfellow et al. Ch. 7-8 HW1 is due.
HW2 is out!
Oct 17 Linear Sequence Models Smith, Ch. 3-4
Murphy Ch. 17, 19
Project proposal is due.
Oct 24 Representation Learning and Convolutional Neural Networks Goodfellow et al. Ch. 9, 14-15
Oct 31 (rescheduled to Oct 29, rooms V1.17/V1.11!) Structured Prediction and Graphical Models Murphy Ch. 10, 19-22
Goodfellow et al. Ch. 16
David MacKay's book, Ch. 16, 25-26
HW2 is due.
HW3 is out!
Nov 7 Recurrent Neural Networks Goodfellow et al. Ch. 10
Nov 14 (room E5) Sequence-to-Sequence Learning Sutskever et al., Bahdanau et al., Vaswani et al.
Nov 21 (room F8) Attention Mechanisms and Neural Memories
Guest lecture: Vlad Niculae
Learning with Sparse Latent Structure HW3 is due.
HW4 is out!
Nov 28 Deep Reinforcement Learning
Game of Taxi
Guest lecture: Francisco Melo
Midterm report is due.
Dec 5 Deep Generative Models
Goodfellow et al. Ch. 20
Murphy, Ch. 28
NIPS16 tutorial on GANs
Kingma and Welling, 2014
Jan 2 Final report is due.
Jan 9 Final Projects I
Jan 16 Final Projects II