Advanced Predictive Models and Applications for Business Analytics

IDS 576 (Spring 2019)

Document version: Jan 27 2019


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. Time permitting, we will also look at online and reinforcement learning problems and their role in sequential decision making problem areas such as transportaion and retail.

Previous Editions


Textbook and Materials


Schedule (tentative)

01/16 : Motivating Applications, Machine Learning Pipeline (Data, Models, Loss, Optimization), Backpropagation

01/23 : Feedforward Networks: Nonlinearities, Convolutional Neural Networks: Convolution, Pooling

02/06 : Jumpstarting Convolutional Neural Networks: Visualization, Transfer, Practical Models (VGG, AlexNet)

02/13 : Text and Embeddings: Introduction to NLP, Word Embeddings, Word2Vec

02/20 : Recurrent Neural Networks: Sequence to Sequence Learning, RNNs and LSTMs

02/27 : Unsupervised Deep Learning: Generative Adversarial Networks, Variational Autoencoders

03/13 : Graphical Models: How they complement Deep Learning

03/20 : Graph Convolutional Networks

04/03 : Inference in Graphical Models: Belief Propagation, Markov Chain Monte Carlo

04/10 : Learning Graphical Models: Maximum Likelihood Estimation, EM Algorithm

04/17 : Online Learning: A/B Testing, Multi-armed Bandits, Contextual Bandits

04/24 : Reinforcement Learning: Policies, State-Action Value Functions, Q-Learning

05/01 : Deep Reinforcement Learning: Function Approximation, DQN for Atari Games, AlphaGo Zero


  1. 01/23 : Assignment 1. Due on 02/05. Example template file.
  2. 02/06 : Assignment 2. Due on 02/19
  3. 02/20 : Assignment 3. Due on 03/12
  4. 03/20 : Assignment 4. Due on 04/16

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. Use Blackboard for uploads.


There is a group project component to this course. More information is available here. Final code-bases with documentation are due to on 04/23.


  1. 03/06 : Exam I (same venue as lectures, and during class hours)
  2. 05/08 : 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