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Pitt CS 2750 - SYLLABUS

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1Machine Learning CS 2750 (ISSP 2170) – Spring 2012 Lecture meeting time: Monday, Wednesday: 1:00 PM-2:15 PM Classroom: 5129 Sennott Square (SENSQ) Instructor: Milos Hauskrecht TA: Zitao Liu Office: 5329 Sennott Square Building Office: 5324 Sennott Square Office Hours: TBA Office Hours: TBA Phone: (412) 624–8845 Phone: Email: [email protected] Email: [email protected] Course Web page: http://www.cs.pitt.edu/~milos/courses/cs2750/ Web Page: http://www.cs.pitt.edu/~ztliu/ Course Description: The goal of the field of machine learning is to build computer systems that learn from experience and that are capable to adapt to their environments. Learning techniques and methods developed by researchers in this field have been successfully applied to a variety of learning tasks in a broad range of areas, including, for example, text classification, gene discovery, financial forecasting, credit card fraud detection, collaborative filtering, design of adaptive web agents and others. This introductory machine learning course will give an overview of many models and algorithms used in modern machine learning, including linear models, multi-layer neural networks, support vector machines, density estimation methods, Bayesian belief networks, mixture models, clustering, ensemble methods, and reinforcement learning. The course will give the student the basic ideas and intuition behind these methods, as well as, a more formal understanding of how and why they work. Students will have an opportunity to experiment with machine learning techniques and apply them a selected problem in the context of a term project. Prerequisites: Knowledge of matrices and linear algebra (CS 0280), probability (CS 1151), statistics (CS 1000), programming (CS 1501) or equivalent, or the permission of the instructor. Textbook: Chris Bishop. Pattern recognition and Machine Learning. Springer, 2006 Homework assignments Homework assignments will have mostly a character of projects and will require you to implement some of the learning algorithms covered during lectures. Programming assignments will be implemented in Matlab. Please visit http://technology.pitt.edu/software/browse/matlab.html to obtain a Matlab license for students.2The assignments (both written and programming parts) are due at the beginning of the class on the day specified on the assignment. In general, no extensions will be granted. Collaborations: No collaboration on homework assignments, programs, term projects and exams unless you are specifically instructed to work in groups, is permitted. Grading The final grade for the course will be determined based on homework assignments, exams, the term project and your lecture attendance and activity. The midterm exam will be in March and the final exam will be the week of April 18-22. The term project presentations will be held the week of April 25-30. Policy on Cheating Cheating and any other anti-intellectual behavior, including giving your work to someone else, will be dealt with severely and will result in the Fail (F) grade. If you feel you may have violated the rules speak to us as soon as possible. Please make sure you read, understand and abide by the Academic Integrity Code for the Faculty and College of Arts and Sciences. Students with Disabilities If you have a disability for which you are or may be requesting an accommodation, you are encouraged to contact both your instructor and Disability Resources and Services, 216 William Pitt Union, (412) 648-7890/(412) 383-7355 (TTY), as early as possible in the term. DRS will verify your disability and determine reasonable accommodations for this course. Tentative syllabus: • Machine learning introduction • Density estimation • Supervised learning: • Linear and logistic regression • Generative classification models • Multi-layer neural networks • Support vector machines • Unsupervised learning • Bayesian belief networks (BBNs) • Learning parameters and structure of BBNs • Expectation maximization • Clustering • Dimensionality reduction/feature selection • Feature filtering • Wrapper methods • PCA • Ensemble methods (mixtures of experts, bagging and boosting) • Reinforcement


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