Flyer Announcement

name initial][second name initial][last name] <em>at cs</em> followed
by <em>.sunysb</em> and finally <em>.edu</em>

CSE 391 Special Topics in Computer Science -- Machine Learning

Spring 2012

Syllabus (NEW)


coming soon

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Section Information

Section: 01
Date: Mondays and Wednesdays
Time: 5:20PM - 6:40PM
Room: CS 2120

Instructor Information

Instructor: Luis Ortiz
Email: le [last name] at cs followed by .stonybrook and finally .edu
Office: 2313-c
Phone: (631) 632-1805
Office Hours: TBD; for now, by appointment (please e-mail me in advance)

Course Description

A course on the fundamental concepts behind intelligent systems that autonomously learn to perform a task and improve with experience. The course will cover learning frameworks and problem formulations, standard models, methods, computational tools, algorithms and modern techniques, as well as methodologies to evaluate learning ability and to automatically select optimal models. Simple applications to areas such as computer vision (e.g., character and digit recognition), natural-language processing (e.g., spam filtering) and robotics (e.g., navigating complex environments) will motivate the coursework and material.


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.

Given the broad applicability of machine learning techniques, it is natural to expect the need for computer scientists with machine learning expertise to continue to increase and expand in the years to come.

This course covers the basic computational aspects of machine learning, at an undergraduate level.

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. A list of recommended textbooks follows. Additional reading material will be taken from a variety of sources, including other textbooks in machine learning and related areas, tutorials and research literature in the area, as appropriate for an undergraduate level course.

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 produce (and hand in) a final written report on their project and give a final project presentation by the end of the course term.

Student Evaluations (Tentative)

Students are evaluated on their performance on homework assignments (25%), a midterm (15%) and a final exam (20%), their participation in class discussions (5%) and the quality of their project and respective presentations (35%).

Tentative List of Topics

NOTE: The list of topics, as well as the emphasis on each topic, may vary slightly depending on the background and interests of the course participants.


coming soon!

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