Instructor
Pedro Felzenszwalb
Email: pff (at) brown.edu
Office: Barus & Holley 355
Office hours: Monday 2-3pm
TA email list: cs142tas (at) cs.brown.edu
TAs
Kilho Son
Yue Zhang
Anson Rosenthal
Jerome DeNijs
Christopher Grimm
Michael Lazos
Richen Zhang
Office hours:
Monday 2pm-3pm B&H 355 (pff)
Monday 6pm-8pm CIT Fishbowl (cg)
Tuesday 12pm-2pm B&H 317 (ks)
Wednesday 4pm-6pm CIT Fishbowl (jd)
Wednesday 6pm-8pm CIT Fishbowl (rz)
Wednesday 8pm-10pm CIT Fishbowl (yz)
Thursday 5pm-7pm CIT 219 (ar)
Thursday 7pm-9pm CIT 219 (mz)
Course description
This course covers fundamental topics in pattern recognition and machine learning. We will consider applications in computer vision, signal processing, speech recognition 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: basic probability, 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.
Notes
There are slides from previous year's courses
2013 slides
2012 slides
Previous Courses
Spring 2013
Fall 2013