IDS 576 Advanced Predictive Models and Applications for Business Analytics

Edition: Spring 2017

Document version: Feb 22, 2017


The goal of this class is to cover advanced machine learning techniques not covered in IDS 572. Broadly, we will cover topics spanning graphical models and deep learning. Graphical models are a very useful tool set 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.




  1. 01/11 : Classification: Machine Learning Pipeline (Data,Models,Loss,Optimization), Backpropagation
  2. 01/18 : Feedforward Networks: Justifying Deep Learning, Python tooling, Nonlinearities
  3. 01/25 : Intro to Convolutional Neural Networks: Convolution, Pooling
  4. 02/01 : Jumpstarting Convolutional Neural Networks: Visualization, Transfer, Practical Models (VGG, AlexNet)
  5. 02/08 : Text and Recurrent Neural Networks I: Introduction to NLP, Word Embeddings
  6. 02/15 : Text and Recurrent Neural Networks II: Word2Vec, Sequence to Sequence Learning, RNNs
  7. 02/22 : Graphical Models: Representation: Directed and Undirected Graphical Models, Conditional Independence, D-separation, Local Markov Property
  8. 03/01 : Graphical Models: Inference: Variable Elimination, Belief Propagation, Markov Chain Monte Carlo
  9. 03/08 : Graphical Models: Learning: Maximum Likelihood Estimation, Expectation Maximization
  10. 03/15 : Online Learning: A/B Testing, Multi-armed Bandits, Contextual Bandits
  11. 03/22 : (Spring Break)
  12. 03/29 : Review of Deep Learning, Graphical Models and Online Learning
  13. 04/05 : In-class Exam
  14. 04/12 : Reinforcement Learning: State-Action Value Functions, Policy, Q-Learning
  15. 04/19 : Deep Reinforcement Learning: Function Approximation, DQN for Atari Games, AlphaGo
  16. 04/26 : Project Presentations





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