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Machine Learning (CS 446) Fall 2018

What Where
Time/place MW 12:30pm-1:45pm 1404 Siebel / Catalog
Class URL https://relate.cs.illinois.edu/course/cs446-fa18
Recorded Lecture https://echo360.com/
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Course Description

The goal of Machine Learning is to build computer systems that can adapt and learn from their experience. This course will study the theory and application of learning methods that have proved valuable and successful in practical applications. We review the theory of machine learning in order to get a good understanding of the basic issues in this area, and present the main paradigms and techniques needed to obtain successful performance in application areas such as natural language and text understanding, speech recognition, computer vision, data mining, adaptive computer systems and others. The main body of the course will review several supervised and (semi/un)supervised learning approaches. These include methods for learning linear representations, Bayesian / Probabilistic methods, decision-tree methods, kernel based methods and neural networks, as well as clustering and dimensionality reduction techniques. We will also discuss how to model machine learning problems and discuss some open problems.

Topics to be covered include:

  • Linear/Logistic Regression
  • Variable Selection / Sparsity
  • Optimization - Gradient Descent
  • Support Vector Machines
  • Convolutional/Recurrent Neural Networks
  • Clustering
  • Graphical Models
  • Expectation Maximization
  • Variational Inference
  • Generative Adversarial Networks
  • Multilabel Classification
  • Structured Prediction

Prerequisites

Students are expected to have taken a class in linear algebra, probability and statistics and a basic class in theory of computation and algorithms. Students are expected to be familiar with the python programming language.

Course Materials

Required Text

None

Optional books with relevant material:

  • Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
  • Shai Shalev-Shwartz and Shai Ben-David, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press
  • Trevor Hastie Robert Tibshirani & Jerome Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer Science & Business Media
  • C.M. Bishop, Pattern Recognition and Machine Learning, Springer
  • Tom Mitchell, Machine Learning, McGraw Hill
  • Ian Goodfellow, Yoshua Bengio & Aaron Courville, Deep Learning, The MIT Press

Lecture notes, course handouts, pointers to relevant papers, and other materials will be available as HTML and PDF documents on Relate and Piazza

Course Staff

name and role contact (illinois.edu) office hours location
Sanmi Koyejo | Instructor sanmi Friday 1pm - 2pm, or by appointment 3314 SC
Haroun Habeeb | TA hhabeeb2 Wednesday 2pm - 4pm Whiteboard outside of 3407 SC
Sameer Manchanda | TA manchan2 Tuesday 9am - 11am Whiteboard outside of 3301 SC
Xiaoyan Wang | TA xiaoyan5 Thursday 6pm - 8pm Whiteboard outside of 3407 SC
Simon Xie | TA cx2 Monday 4pm - 6pm Whiteboard outside of 3407 SC
Kabir Manghnani | CA kabirm2 Friday 2pm - 4pm Whiteboard outside of 3310 SC
Duke Vijitbenjaronk | CA wdv2 Friday 2pm - 4pm Whiteboard outside of 3310 SC
Qingran Wang | CA qwang78 N/A N/A
Zecheng Zhang | CA zzhan147 N/A N/A

Exams

Exam will be in class exam with 75 minutes. Please find information on our upcoming exams in the corresponding section of the class calendar.

Homework

Please see the class calendar for homework deadlines. We encourage you to use the homework template for LaTeX.

  • Homework 0 (Note: HW0 will be graded but will not be counted as part of your final grade)
  • Homework 1 We encourage you to use the homework template for LaTeX on overleaf (Note: You need to copy the project to your own repository on overleaf. And you need to submit separated pdf files for different problems.)
  • Homework 2
  • Homework 3
  • Homework 4 You can use the template for LaTex on overleaf (Note: You need to copy the project to your own repository on overleaf. And you need to submit separated pdf files for different problems.)
  • Homework 5 Templates available on Overleaf GAN HMM (Note: You need to copy the project to your own repository on overleaf. And you need to submit separated pdf files for different problems.)

Lecture Scribing

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(Notice that unlike homework (which will be auto-collected), you will need to manually submit your lecture scribe by clicking the submit button on the top right corner.)

  • Scribe submission has already ended. You can view the final version of the signup sheet here

  • Please use the LaTeX template. Scribing expectations are further elaborated upon in the template.

  • Here is an example lecture scribe note.

Grading Policies

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Academic integrity

Feel free to discuss the assignment with each other in general terms, and to search the Web for general guidance (not for complete solutions). All solutions should be written up individually. If you make substantial use of some information from outside sources, make sure you acknowledge the sources in your solution. In particular, make sure you acknowledge all other students you worked with on the homework/projects. Failure to do this will result in a zero grade. We will follow the departmental honor code policy here: https://cs.illinois.edu/academics/honor-code

What to do in an emergency

See this handout

Computing

We will be using Python with the libraries numpy, scipy and matplotlib for assignments. No other languages are permitted. Python has a very gentle learning curve, so you should feel at home even if you've never done any work in Python.

Project

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Schedule

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Python Help

Numpy Help

Linear Algebra

Statistics