Deep Structured Learning (IST, Fall 2020)


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, we 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


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/instructor 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 NeurIPS 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


Date Topic Optional Reading
Sep 23 Introduction and Course Description Goodfellow et al. Ch. 1-5
Murphy Ch. 1-2
Sep 30 Linear Classifiers Murphy Ch. 3, 6, 8-9, 14
Eisenstein Ch. 2
Oct 7 Feedforward Neural Networks Goodfellow et al. Ch. 6 HW1 is out! Skeleton code: Q2, Q3.
Oct 14 Neural Network Toolkits (Marcos Treviso) Goodfellow et al. Ch. 9, 14-15
Oct 21 Representation Learning and Convolutional Neural Networks Goodfellow et al. Ch. 7-8 Project proposal is due.
Oct 28 Linear Sequence Models Eisenstein, Ch. 6-8
Smith, Ch. 3-4
Murphy Ch. 17, 19
HW1 is due.
HW2 is out! Skeleton code:,,,,,
Nov 4 Deep Reinforcement Learning (Francisco Melo)
Game of Taxi
Nov 11 Recurrent Neural Networks Goodfellow et al. Ch. 10
Nov 18 Probabilistic Graphical Models Murphy Ch. 10, 19-22
Goodfellow et al. Ch. 16
David MacKay's book, Ch. 16, 25-26
Eric Xing's CMU lecture
Stefano Ermon's notes on variable elimination Brady Neal's course on Causal Inference
HW2 is due.
HW3 is out!
Nov 25 Sequence-to-Sequence Learning Eisenstein, Ch. 18
Sutskever et al., Bahdanau et al., Vaswani et al.
Midterm report is due.
Dec 2 Attention Mechanisms (Marcos Treviso) Learning with Sparse Latent Structure
Illustrated Transformer
Dec 16 Deep Generative Models
Goodfellow et al. Ch. 20
Murphy, Ch. 28
NeurIPS16 tutorial on GANs
Kingma and Welling, 2014
HW3 is due (Dec 9).
Jan 8 Final report is due.
Jan 20, 27 Final Projects