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Course Information
Topics Covered
Supervised Learning, unsupervised learning, graphical models and advanced topics, as time permits. Linear models, non-linear models, neural networks, kernel methods, graphical models, instance-based methods, mixture models, methods for sequential and structured data.
ACKNOWLEDGEMENTS: The material in this course is a synthesis of materials from many sources, including: Hal Daume III, Mark Drezde, Carlos Guestrin, Andrew Ng, Ben Taskar, Eric Xing, and others. I am very grateful for their generous sharing of insights and materials.
Textbooks
Required:
Christopher Bishop,
Pattern Recognition and Machine Learning. ISBN 0387310738.
Additional Resources:
- Machine Learning by Tom Mitchell (ISBN 0070428077)
- Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani and Jerome Friedman (ISBN 0387952845)
- Information Theory, Inference and Learning Algorithms by David MacKay (ISBN 0521642981)
- An Introduction to Computational Learning Theory by Michael Kearns and Umesh Vazirani (ISBN 0262111934)
Grading
| Area | Description / Due Date | Percentage |
| Class Participation | participation in discussion and exercises | 5% |
| Homework | 5 assignments | 30% |
| Project | Final term project | 20% |
| Midterm Exam | Tuesday, October 19, during class | 20% |
| Final Exam | Saturday, Dec. 18, 10:30am-12:30pm | 25% |
Workload
There will be reading assignments. Students are expected to (and are assumed to have) read the material before class. There will be an in-class midterm and final. There will be homework assignments which include written and programming questions. There will be a course project.
Course Participation
Students are expected to attend lectures.
Laptop and cell phone usage is prohibited during lecture. It is distracting for the students around you. Given the late hour of the class, we will all need to be on our toes to keep things lively!
Programming
Programming exercises will be done in MATLAB. The university holds a site license for MATLAB. To access the GUI application from your CS account, simply type "matlab" at the terminal prompt.
Homeworks
There will be around six written homeworks.
Students may discuss the homework to understand the problem and reach a solution.
However, each student must write down the solution
Important note on the honor code: It is an honor code violation to use, without proper citation, or reference, any materials from the web.
Late Homeworks
Unless otherwise stated, homeworks and projects are due electronically on their due date. Due dates and times will be specified for each project. A grading penalty will be applied to late homeworks and projects. Recognizing that students may face unusual circumstances and require some flexibility in the course of the quarter, each student will have a total of five free late (calendar) days to use as s/he sees fit. No additional individual extensions will be given. Once these late days are exhausted, any homework turned in late will be penalized at the rate of 25% per late day (or fraction thereof). Under no circumstances will a homework or project be accepted more than five days after its due date.
Re-Grading Issues
The majority of the grading will be done by the TA. If you think there has been a mistake in grading your homework or exam, please submit a regrade request explaining in writing, precisely and concisely, the grading error that has occurred, to the TA. Such request must be made no later than 1 week after the material in question was returned to the class. Any request to have an assignment regraded may result in the entire assignment in question being regraded, possibly resulting in a loss of points.
Academic Integrity
In this course you are responsible for both the University's Code of Academic Integrity and the University of Maryland Guidelines for Acceptable Use of Computing Resources. Any evidence of unacceptable use of computer accounts or unauthorized cooperation on tests, quizzes, or projects will be submitted to the Student Honor Council, which could result in an XF for the course, suspension, or expulsion from the University.
Excused Absences & Disability Accomodation
Students claiming an excused absence for an exam must apply in writing and furnish documentary support (such as from a health care professional who treated the student). No make-up exams are given. Excused absences do not extend your 7 late day budget.
Any student eligible for and requesting reasonable academic accommodations due to a disability is requested to provide, to the instructor in office hours, a letter of accommodation from the Office of Disability Support Services (DSS) within the first two weeks of the semester.
Any student who must miss a class due to religious holidays should also notify the instructor during the first two weeks of class.