Advanced Predictive Models and Applications for Business Analytics

IDS 576

Informal name: Deep Learning, Graphical Models and Reinforcement Learning

All announcements will be made on the Forum

Overview

The goal of this class is to cover a subset of advanced machine learning techniques, after students have seen the basics of data mining (such as in in IDS 572) and machine learning (such as in IDS 575). Broadly, we will cover topics spanning graphical models, deep learning and reinforcement learning. Graphical models are useful for inferring outcomes and making predictions conditional on preceding/related events, even when we do not have full information. They have found success in tracking, speech recognition, language modeling (Hidden Markov Models), image segmentation (Markov Random Fields) and other applications. Similarly, we will study popular deep learning architectures, their design choices and how they are trained. We will also study recurrent and convolutional architectures which achieve state of the art in challenging prediction tasks in text and computer vision applications. Finally, we will look at online and reinforcement learning problems and their role in sequential decision making problem areas such as transportation and retail.

Previous Editions

Logistics

Textbook and Materials

Software

Schedule (tentative)

08/29 : Motivating Applications, Machine Learning Pipeline (Data, Models, Loss, Optimization), Backpropagation

09/05 : Feedforward Networks: Nonlinearities, Convolutional Neural Networks: Convolution, Pooling

09/12 : Jumpstarting Convolutional Neural Networks: Visualization, Transfer, Practical Models (VGG, AlexNet)

09/19 : Text and Embeddings: Introduction to NLP, Word Embeddings, Word2Vec

09/26 : Recurrent Neural Networks: Sequence to Sequence Learning, RNNs and LSTMs

10/03 : Unsupervised Deep Learning: Generative Adversarial Networks, Variational Autoencoders

10/17 : Canceled

10/24 : Online Learning: A/B Testing, Multi-armed Bandits, Contextual Bandits

10/31 : Reinforcement Learning: Policies, State-Action Value Functions, Q-Learning

11/07 : Deep Reinforcement Learning: Function Approximation, DQN for Atari Games, AlphaGo Zero

11/14 : Graphical Models: How they complement Deep Learning

11/21 : Inference in Graphical Models: Belief Propagation, Markov Chain Monte Carlo

11/28: Thanksgiving

12/05 : Learning Graphical Models: Maximum Likelihood Estimation, EM Algorithm

Assignments

  1. 09/05: Assignment 1. Due 09/18
  2. 09/19: Assignment 2. Due 10/02
  3. 10/03: Assignment 3. Due 10/16
  4. 10/24: Assignment 4. Due 11/06

These involve reimplementing recent deep-learning techniques and understanding their behavior on interesting datasets. Always mention any sources that were relied on, in your assignment solutions. Submission deadline is BEFORE 11.59 PM on the concerned day. Late submissions will have an automatic 20% penalty per day without exceptions. Use Blackboard for uploads.

Project

There is a group project component to this course. Additional details will be provided very soon.

Exams

  1. 10/10 : Exam I (same venue as lectures, and during class hours)
  2. 12/12 : Exam II (finals week, subject to change)

These are closed book, but one 8.5x11-inch handwritten cheatsheet is allowed. No computers and communication devices are allowed.

Grades

Miscellaneous Information