CSE 592 Advanced Topics in Computer Science -- Principles of Machine Learning

Fall 2011


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Section Information

Date: Monday and Wednesday
Time: 3:50pm-5:10pm
Room: 2114 Teaching Lab, CS Building

Instructor Information

Instructor: Luis Ortiz
Email: le [last name] at cs followed by .stonybrook and finally .edu
Office: 2313-C, CS Building
Phone: (631) 632-1805
Office Hours: 5:30pm-7pm, Monday and Wednesday; or by appointment (please e-mail me in advance)

Course Description

Roughly speaking, machine learning techniques strive to automatically acquire expertise to effectively perform a task of interest by efficiently processing task-related information, such as a (usually large) data set of examples, and successfully extracting and generalizing knowledge embedded within the available information.

There is growing demand for computer scientists with proficiency in machine learning. For example, the advent of technology for the collection of vast amounts of digital data, such as that generated by an ever expanding population of Internet users, has increased interest in the development of machine learning software applications. Machine-learning-based technology such as driver assistance and voice-activated systems in cars, automatic system personalization and adaptation to individual user preferences and behavior, speech-driven phone systems for customer service, speech-to-text capabilities, recommender systems and e-mail spam filtering, are now commonplace.

Machine learning application areas include marketing, e-commerce, software systems, networking, telecommunications, banking, finance, economics, social science, computer vision, speech recognition, natural-language processing, and robotics. Some problems addressed using machine learning techniques include pattern recognition and classification, knowledge discovery and data mining, anomaly detection, credit/loan approval, credit-card fraud, quantitative trading, automatic categorization of very large collections, such as web pages, documents and images, effective ranking of web search results (e.g., Google's PageRank), face recognition, tracking, machine translation, and more recently, intelligent, adaptive control of virtual player behavior in video games, smart debugging of computer programs and memory management in operating systems. There is also recent interest in creating computationally tractable machine learning tools for recognizing and predicting general trends in individual or group behavior in large populations, such as spending behavior, adoption of new products, technology or habits, sharing in peer-to-peer systems, and predicting the development of online communities within large social networks such as Facebook.

Course Objectives

This course provides an introduction to fundamental concepts and modern techniques in machine learning, and prepare students for future work in the area. The objective is to provide students with basic knowledge and understanding of both the theory and practice of machine learning, and to train students on the use and application of machine learning ideas, paradigms and techniques.

Course Organization

The course format involves formal lectures, discussions and presentations, some led by the students themselves. There is no required textbook for the course; a list of recommended textbooks is provided below. Additional reading material will be taken from a variety of sources, including other ML textbooks, tutorials and literature in the area, as appropriate. The following is a list of some likely sources for reading material; it will be adapted as the instructor see fit based on the final list of topics and the students' backgrounds and interests.

Recommended Textbooks

Course Project

Students complete a project on an application of machine learning to a particular problem, which the students choose in consultation with the instructor. The chosen course project requires instructor's approval. The project should have an experimental component. Ideally, the project will address a new problem and produce a novel application. Students make an oral presentation of their project proposal. To monitor the project's development, students periodically make oral presentations as the project progresses. Students give a final presentation of their project by the end of the course term.

Student Evaluations

Students are evaluated on their performance on homework assignments (15%), a midterm and final exams (30%), their participation in class discussions (5%) and the quality of their project and respective presentations (20%) and reports (30%).

Tentative List of Topics

NOTE: The list of topics, as well as the emphasis on each topic, will likely vary depending on the background and interests of the course participants, among other things.

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