Advanced Predictive Models and Applications for Business Analytics

IDS 576

Informal name: Deep Learning, Graphical Models and Reinforcement Learning


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


Textbook and Materials


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, ResNet)

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

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

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

10/17 : Project progress check-in with the TA: hand in a 1-page proposal sheet during class hours

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 : Representing Graphical Models: Conditional Independences and 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


  1. 09/05: Assignment 1. Due 09/18. Example template file.
  2. 09/19: Assignment 2. Due 10/02
  3. 10/03: Assignment 3. Due 10/16
  4. 11/14: Assignment 4. Due 11/27

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.


There is a group project component to this course. Additional details are provided here. An intermediate check point (to hand in a 1-page proposal in class) is 10/17. The final deadline to submit is 12/01.


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

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


Miscellaneous Information