Email: pff (at) brown.edu
Office: Barus & Holley 355
Office hours: Monday 2-3pm
TA email list: cs142tas (at) cs.brown.edu
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)
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.
C. Bishop, Pattern Recognition and Machine Learning, Springer
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.
There are slides from previous year's courses 2013 slides 2012 slides