Instructor
Pedro Felzenszwalb
Email: pff (at) brown.edu
Office: Barus & Holley 355
Office hours: Monday 2-3pm
TA email list: cs1420tas (at) lists.brown.edu
The TAs have set up a page on Piazza for questions and discussions.
Piazza page
TAs
Tyler Dae Devlin (tyler_devlin@brown.edu)
Hong Jun Choi (hong_jun_choi@brown.edu)
Gabe Hope (john_hope@brown.edu)
Huan Lin (huan_lin@brown.edu)
Valentin Perez (valentin_perez@brown.edu)
Akash Suresh (akash_suresh@brown.edu)
Keshav Vemuri (keshav_vemuri@brown.edu)
Daniel Xiang (daniel_xiang@brown.edu)
Office hours:
Monday 6pm-8pm (CIT 165) tyler
Monday 8pm-10pm (CIT 165) dan
Tuesday 10am-noon (CIT 207) jun
Tuesday 6pm-8pm (CIT 165) gabe
Tuesday 8pm-10pm (CIT 165) huan + akash
Sunday 8pm-10pm (CIT 227) valentin
Sunday 10am-noon (CIT 207) keshav
Course description
This course covers fundamental topics in pattern recognition and machine learning. We will consider applications in computer vision, signal processing, and information retrieval. Topics include: decision theory, parametric and non-parametric learning, dimensionality reduction, graphical models, exact and approximate inference, semi-supervised learning, generalization bounds and support vector machines.
Prerequisites: probability and statistics, linear algebra, calculus and programming experience.
Textbook
C. Bishop, Pattern Recognition and Machine Learning, Springer
Grading
Grading will be based on regular homework assignments and two
exams. Homework will involve both mathematical exercises and
programming assignments in Matlab.
Students may discuss and work on homework problems in groups. However, each student must write down the solutions independently. Each student should write on the problem set the set of people with whom s/he collaborated.
The breakdown of the final grades will be approximatelly as follows:
Homeworks 60%
Midterm 20%
Final 20%
IMPORTANT DATES:
Midterm: April 6 in class.
Final exam: May 18 at 2pm in Solomon DECI.
Previous Courses
Fall 2015
Spring 2015