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Machine Learning in Practice/ Applied Machine Learning 11-344,05-834/05-434 Time


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Machine Learning in Practice/ Applied Machine Learning

11-344,05-834/05-434
Time: Tu/Th 1:30-2:50

Location: NSH 3002
Instructor: Dr. Carolyn P. Rosé, cprose@cs.cmu.edu

Office Hours: NSH 4531, By appointment
TA: Kishore Prahallad, skishore@cs.cmu.edu

Office Hours: Thursdays 3-4, NSH 4632
Course Cross-listed in: HCII, LTI

Note: Blackboard link says Applied Machine Learning

Units: 12 (PhD/Master’s/Undergrad level)
Books:

Witten, I. H. & Frank, E. (2005). Data Mining: Practical Machine Learning Tools and Techniques, second edition, Elsevier: San Francisco, ISBN 0-12-088407-0


Prerequisites: Some Java programming experience is desirable, but not necessary.
Course Description:

Machine Learning is concerned with computer programs that enable the behavior of a computer to be learned from examples or experience rather than dictated through rules written by hand. It has practical value in many application areas of computer science such as on-line communities and digital libraries. This class is meant to teach the practical side of machine learning for applications, such as mining newsgroup data or building adaptive user interfaces. The emphasis will be on learning the process of applying machine learning effectively to a variety of problems rather than emphasizing an understanding of the theory behind what makes machine learning work. This course does not assume any prior exposure to machine learning theory or practice.

We will cover a wide range of learning algorithms that can be applied to a variety of problems. In particular, we will cover topics such as decision trees, rule based classification, support vector machines, Bayesian networks, and clustering. In addition to readings from the course textbook, we will have additional readings from research articles that will be announced ahead of time and distributed on Blackboard.

Grades will be based on weekly assignments and quizzes, 2 take-home midterms, and a course project.

Assignments will include readings and experiments using the Weka toolkit (http://www.cs.waikato.ac.nz/ml/weka/) and the TagHelper tools toolkit (http://www.cs.cmu.edu/~cprose/TagHelper.html). Assignments will be distributed in class on Tuesday each week and will be due the following Tuesday before class. You will just get credit for doing these.

Quizzes will be given at the beginning of class each Tuesday. You will just get credit for doing these. These are meant to help you assess your level of understanding.

Take home mid-terms will be distributed at the end of class on a Tuesday or Thursday, and will be due 24 hours later.

The term project will involve applying machine learning to a substantial problem of the student’s choice. Several options are found in the Projects subfolder of the Course Documents folder on blackboard. Students may select one of these projects or may propose one of their own design. Students who wish to design their own project should check in about their plans with the instructor as early as possible in the semester.



Grading Criteria

Quizzes (10%)

Assignments (20% total)

Mid-terms (10% each)

Course project (50%)
On-line video versions of all lectures will be available as optional supplementary material.

Course Schedule

[Aug 28, 30]



Week 1 Course Intro/ Weka Intro (Witten & Frank, CH 1, 9-10)
[Sep 4, 6]

Week 2 Input and Output (Witten & Frank, CH 2-3.2, Kwiatkowska et al., 2005)
[Sep 11, 13, 18, 20]

Week 3-4 Basic Statistical Models and Linear Models (Witten & Frank, Ch 4.2, 4.6)
Course Project Proposals due on Thursday
[Sep 25, 27]

Week 5 Applied Machine Learning Process and Evaluation (Witten & Frank, CH 5, CH 12)
[Oct 2, 4, 9, 11]

Week 6-7 Working with Text/TagHelper (Jackson & Moulinier, CH 1,3)
[Oct 16, 18]

Week 8 Rule Representations and Basic Algorithms (Witten & Frank, CH 3.3-4)
Take Home Mid-term 1 on Thursday, no assignment this week
[Oct 23, 25]

Week 9 Advanced Tree and Rule Based Learning (Witten & Frank, CH 6.1, 6.2, 6.5)
[Oct 30, Nov 1]

Week 10 Linear Models, Statistical Models, and Clustering (Witten & Frank, CH 6.3,6.4, 6.6, 6.7)
[Nov 6, 8]

Week 11 Feature Selection and Optimization (Witten & Frank, CH 7.1-7.5)
[Nov 13, 15]

Week 12 Semi-Supervised Learning, Machine Learning Extensions (Witten & Frank, CH 7.6-8)
Take Home Mid-term 2 on Tuesday, no assignment this week
[Nov 20, 27, 29]

Week 13-14 More Machine Learning Applications (Readings TBA)
[Dec 4, 6]

Week 15 Wrap-up and Poster Session
Final paper due no later than Dec 14


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