IDS 576 Advanced Predictive Models and Applications for Business Analytics
Edition: Spring 2018
Document version: Jan 14 2017
- Syllabus is tentative and can change.
- Refer to Slack for all announcements.
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.
- Lectures: Wednesdays 6.00 PM to 8.30 PM at Burnham Hall 317
- Optional Recitations: Wednesdays 8.30 PM to 9.30 PM at TBD
- Online communication: Slack
- Offline communication:
- Instructor Office Hours: Wednesdays 3.00 PM - 4.00 PM or schedule by Slack
- TA Office Hours: Tuesdays 4.00 PM - 6.00 PM or schedule by Slack
Textbook and Materials
- There is no preferred textbook. Several references and reading material will be provided instead.
- Materials (lecture notes, assignments and project details) will be posted on Blackboard.
- 01/17 : Classification: Machine Learning Pipeline (Data, Models, Loss, Optimization), Backpropagation
- 01/24 : Feedforward Networks: Justifying Deep Learning, Python tooling, Nonlinearities
- 01/31 : Intro to Convolutional Neural Networks: Convolution, Pooling
- 02/07 : Jumpstarting Convolutional Neural Networks: Visualization, Transfer, Practical Models (VGG, AlexNet)
- 02/14 : Text and Recurrent Neural Networks I: Introduction to NLP, Word Embeddings
- 02/21 : Text and Recurrent Neural Networks II: Word2Vec, Sequence to Sequence Learning, RNNs
- 02/28 : Review of Deep Learning
- 03/14 : Graphical Models: Representation: Directed and Undirected Graphical Models, Conditional Independence, D-separation, Local Markov Property
- 03/21 : Graphical Models: Inference: Variable Elimination, Belief Propagation, Markov Chain Monte Carlo
- 04/04 : Graphical Models: Learning: Maximum Likelihood Estimation, Expectation Maximization
- 04/11 : Online Learning: A/B Testing, Multi-armed Bandits, Contextual Bandits
- 04/18 : Reinforcement Learning: State-Action Value Functions, Policy, Q-Learning
- 04/25 : Deep Reinforcement Learning: Function Approximation, DQN for Atari Games, AlphaGo
- 05/02 : Project Presentations
- 01/31: Assignment 1 out. Due on 02/20
- 04/04: Assignment 2 out. Due on 04/17
- 03/07: Exam I (same venue as lectures, and during class hours)
- 05/09: Exam II (same venue as lectures, and during class hours)
- 03/20: Project Report I due
- 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.
- Assignments (2): 10% + 10%
- Exams (2): 20% (Exam I) + 25% (Exam II)
- Project (2): 10% (Report I) + 25% (Report II)
- Always mention sources in your assignment solutions and project writeups.
- Late submissions will have an automatic 20% penalty per day.
- These are closed book, but one 8.5x11-inch cheatsheet is allowed.
- No computers and communication devices are allowed.
- This involves working on and documenting a machine learning solution on a dataset of your choice (e.g., reimplementing and verifying the results of any research paper appearing in recent machine learning and data mining conferences). See details on Blackboard.
- This is a 4 credit graduate level course with CRN 38063, offered by the Information and Decision Sciences department at UIC.
- The semester runs from Jan 16, 2018 - May 04, 2018 (academic calendar).
- Students who wish to observe their religious holidays (http://oae.uic.edu/religious-calendar/) shoud notify the instructor by Jan 20.
- Please contact the instructor at the earliest, if you require accommodations for access to and/or participation in this course.
- Please refer to the academic integrity guidelines set by the university.