**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