Spring 2018: Machine Learning [CS782] 

• Professor: Carlotta Domeniconi, carlotta\AT\cs.gmu.edu 
• Office hours: Tue 2-4PM, Rm 4424 ENG 

• Prerequisites: CS 580 or CS 584 or permission of instructor. Programming experience is expected. Students must be familiar with basic probability and statistics concepts, linear algebra, optimization, and multivariate calculus. 

• Location and Time: We meet in the Art and Design Building L008, T 4:30pm - 7:10pm 

• Textbook:
• C. M. Bishop Pattern Recognition and Machine Learning, Springer, 2006.
  Book's companion website
  
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• Useful material:
• Overview on Linear Algebra
  
• Andrew Moore's Tutorials: Collection of tutorials on topics of interest for this class
  
• Datasets
  
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• Course Web Page
  

General Description and Preliminary List of Topics: 

Machine learning studies computer algorithms for learning to do things. For example, we might be interested in learning to complete a task, or to make accurate predictions, or to navigate in an unexplored environment. The learning that is being done is always based on some sort of observations or data, such as examples (the most common case in this course), direct experience, or instruction. So in general, machine learning is about learning to do better in the future based on what was experienced in the past. The emphasis of machine learning is on automatic methods. In other words, the goal is to devise learning algorithms that do the learning automatically without human intervention or assistance. 

The machine learning paradigm can be viewed as “programming by example.” Often we have a specific task in mind, such as recognizing handwritten digits on an envelope to perform automated mail dispatching. But rather than program the computer with rules to solve the task directly, in machine learning, we seek methods by which the computer will come up with its own program based on examples that we provide. 

The course covers key algorithms and theory at the core of machine learning. Particular emphasis will be given to the statistical learning aspects of the field. Topics include: decision theory, Bayesian theory, curse of dimensionality, linear and non-linear dimensionality reduction techniques, classification, clustering, kernel methods, mixture models and EM, ensemble methods, deep learning (if time permits). 

Course Format: 

Lectures by the instructor. Besides material from the textbook, topics not discussed in the book may also be covered. Research papers and handouts of material not covered in the book will be made available. Grading will be based on quizzes, exams, and a project. Homework assignments will be given and discussed in class, but not graded. Exams must be done on an individual basis. Any deviation from this policy will be considered a violation of the GMU Honor Code. 

Grading Policy: 

Quizzes: 20% 

Participation: 5% 

Midterm: 35%  

Project: 35% (Proposal 10%; Presentation 10%; Paper 15%)