CSCI 1950-F

Summer 2011

Brown University

Course Information

See the syllabus, and the schedule for details on homework, quizzes, lectures, and readings.

Instructor

Jeff Miller

Daily schedule

The following schedule will be followed each weekday (Monday through Friday):

9:00 - 10:30 (CIT room 143) - This time is available for students to (independently or collaboratively) work on homework, watch instructional videos, and/or read.

10:30 - 11:30 (CIT room 345) - The class will meet at this time for lecture/discussion, homework submission, and quizzes.

11:30 - 12:00 (CIT room 345 or 143) - The instructor will be available to answer further questions at this time. Students may also use this time for homework, videos, or reading.

Videos

Lectures will focus on intuition and understanding, while detailed mathematical treatment will be presented through instructional videos to be watched outside of lecture time. This allows you to absorb concepts at your own pace, and frees up valuable face-to-face lecture time for interactive discussion. The Probability Primer series provides a review of probability, and the rest of the material is in the Machine Learning series.

Textbook

The primary course textbook is Machine Learning: A Probabilistic Perspective, in preparation by Prof. Kevin Murphy. Printed copies are available (for purchase) from the Metcalf Copy Center. The first chapter is freely available online.

Homework

Each day there will be a short homework assignment. Homework assignments combine mathematical derivations with programming exercises in Matlab. Some introductory video tutorials for Matlab are available here. (Here are links to the ones I would recommend: 1, 2, 3, 4, 5, 6.)

You are encouraged to collaborate with your classmates on the assignments, however, each student must submit his/her own solutions. Your solutions should be submitted in hard copy (not electronically) in class. For programming exercises, submit a print out of the code you wrote. For other questions, your answers can be handwritten (they do not need to be typed).

Quizzes

Each day there will be a short quiz to assess your understanding of the material. (The quiz will typically be on the same topic as the homework that was due on the previous day of class.)

Previous Courses

Spring 2011: CSCI 1950-F: Introduction to Machine Learning, Erik Sudderth.

Fall 2009: CSCI 1950-F: Introduction to Machine Learning, Mark Johnson and Erik Sudderth.

Fall 2006: CS 195-5: Introduction to Machine Learning, Greg Shakhnarovich.

Announcements

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