Course Proposal Applied Machine Learning
A PPLIED M ACHINE L EARNING (CS 638)
W EI D ING
O FFICE : S-3-167 S CIENCE B UILDING
O FFICE P HONE : 617.287.6428
F AX : 617.287.6433
E MAIL : DING @ CS . U MB . EDU
Target Students:
Undergraduate and graduate students who want to learn hands-on skills applying machine learning
algorithms in real-world applications.
Pre-requisite:
CS 310 Advanced Data Structures and Algorithms or permission of instructor
Class Description:
This course presents the practical side of machine learning for applications, such as pattern
recognition from images or building predictive classifiers. Topics will include linear models
for regression, decision trees, rule based classification, support vector machines, Bayesian
networks, and clustering. The emphasis of the course will be on the hands-on application
of machine learning to a variety of problems. This course does not assume any prior
exposure to machine learning theory or practice.
Text Book:
Data Mining: Practical Machine Learning Tools and Techniques
Ian H. Witten, Eibe Frank, Mark A. Hall
Morgan Kaufmann
ISBN 978-0-12-374856-0
2011
Evaluation:
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Course Proposal Applied Machine Learning
Individual Homework (30% of the total): Students will study, modify, and debug state-of-the-art
machine learning algorithms in homework assignments. Each homework assignment will include a
programming assignment and a written report.
Two Term Projects (40% of the total): Different from the undergraduate students, graduate
students will work on two term projects in the class. Students will work as teams to learn the
practical, hands-on skills to get machine algorithms work well on a real-world application. Each
student will receive an individual score based on his/her on the term project.
Presentation (5% of the total): Each team will have an oral 20-minute presentation to discuss their
project methods, experimental results, experiences, and lessons learned.
One Final Exam (25% of the total): A comprehensive examination for in-depth understanding of the
characteristics of various machine learning algorithms.
91+ = A; 89+ = A-;
87+ = B+; 83+ = B; 80+ = B-;
77+ = C+; 73+ = C; 70+ = C-;
67+ = D+; 63+ = D; 60+ = D-;
0+ = F
Student Conduct
Students are required to adhere to the University Policy on Academic Standards and Cheating, to
the University Statement on Plagiarism and the Documentation of Written Work, and to the Code
of Student Conduct as delineated in the University Catalog and Student Handbook. The Code is
available online at:
http://www.umb.edu/life_on_campus/policies/code
Accommodations
Section 504 of the Americans with Disabilities Act of
1990 offers guidelines for curriculum modifications and adaptations for students with documented
disabilities. If applicable, students may obtain adaptation recommendations from the Ross Center
for Disability Services, Campus Center, UL Room 211, (617-287-7430). The student must present
these recommendations and discuss them with each professor within a reasonable period,
preferably by the end of Drop/Add period.
Class Schedule
A course website will be available for course syllabus and class schedule. The class will also
have a Blackboard website for class forum, lecture notes, assignments, and class
announcement.
1. Week 1: Course Intro/ Weka Intro (Witten & Frank, CH 1, 9-10)
2. Week 2 & Week 3: Input and Output (Witten & Frank, CH 2-3.2)
3. Week 3 & Week 4: Basic Statistical Models and Linear Models (Witten & Frank, Ch 4.2,
4.6)
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Course Proposal Applied Machine Learning
4. Week 5: Applied Machine Learning Process and Evaluation (Witten & Frank, CH 5, CH
12)
5. Week 6 & Week 7: Rule Representations and Basic Algorithms (Witten & Frank, CH 3.34)
6. Week 8 & Week 9 Advanced Tree and Rule Based Learning (Witten & Frank, CH 6.1,
6.2, 6.5)
7. Week 10: Linear Models, Statistical Models, and Clustering (Witten & Frank, CH 6.3,6.4,
6.6, 6.7)
8. Week 11 & Week 12: Feature Selection and Optimization (Witten & Frank, CH 7.1-7.5)
9. Week 13 & Week 14: Semi-Supervised Learning, Machine Learning Extensions (Witten
& Frank, CH 7.6-8)
10. Week 15: Machine Learning Applications
Additional Sources
[1] CS229A Applied Machine Learning, Computer Science Department, Stanford University,
http://cs229a.stanford.edu/
[2] Introductory Applied Machine Learning, School of Informatics, The University of
Edinburgh, http://www.inf.ed.ac.uk/teaching/courses/iaml/
[3] Applied Machine Learning, Master of Information Technology Strategy, Carnegie
Mellon University, http://www.cmu.edu/mits/curriculum/core/05-834.html
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