IDS 576 Advanced Predictive Models and Applications for Business Analytics

Edition: Spring 2018

Document version: Mar 19 2018


The goal of this class is to cover advanced machine learning techniques not covered in IDS 572 and IDS 575. Broadly, we will cover topics spanning graphical models and deep learning. Graphical models are a set of very useful techniques for inferring outcomes and making predictions conditional on preceding 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 many other applications. Similarly, we will study the basics of deep learning architectures, their design choices and how they are trained using gradient methods. We will also study recurrent and convolutional architectures which achieve state of the art in challenging prediction tasks in computer vision and text applications. Time permitting, we will also look at online and reinforcement learning problems.


Textbook and Materials



Lectures (tentative)

  1. 01/17 : Motivating Applications, Machine Learning Pipeline (Data, Models, Loss, Optimization), Backpropagation
  2. 01/24 : Feedforward Networks: Nonlinearities, Convolutional Neural Networks: Convolution, Pooling
  3. 01/31 : Jumpstarting Convolutional Neural Networks: Visualization, Transfer, Practical Models (VGG, AlexNet)
  4. 02/07 : Text and Embeddings: Introduction to NLP, Word Embeddings, Word2Vec
  5. 02/14 : Recurrent Neural Networks: Sequence to Sequence Learning, RNNs and LSTMs
  6. 02/21 : Unsupervised Deep Learning: Generative Adversarial Networks, Variational Autoencoders
  7. 02/28 : Review of Deep Learning and Recent Advances
  8. 03/14 : Graphical Models: Representation: Directed and Undirected Graphical Models, Conditional Independence, D-separation, Local Markov Property
  9. 03/21 : Graphical Models: Inference: Variable Elimination, Belief Propagation, Markov Chain Monte Carlo
  10. 04/04 : Graphical Models: Learning: Maximum Likelihood Estimation, Expectation Maximization
  11. 04/11 : Online Learning: A/B Testing, Multi-armed Bandits, Contextual Bandits
  12. 04/18 : Reinforcement Learning: Policies, State-Action Value Functions, Q-Learning
  13. 04/25 : Deep Reinforcement Learning: Function Approximation, DQN for Atari Games, AlphaGo Zero
  14. 05/02 : Project Presentations

(A concatenated set of slides are available here)


  1. 01/31: Assignment 1 (lengthy!) out. Due on 02/27
  2. 02/28: Assignment 2 out. Due on 03/13
  3. 03/21: Assignment 3 out. Due on 04/10
  4. 04/11: Assignment 4 out. Due on 04/24


  1. 03/07: Exam I (same venue as lectures, and during class hours)
  2. 05/09: Exam II (same venue as lectures, and during class hours)


  1. 03/20: Project Report I due
  2. 05/01: Project Report II due

Note: Submission deadline for assignments and project reports is BEFORE 2359hrs on the concerned day. Use Blackboard for uploads.





Miscellaneous Information

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