Machine Learning (CSCI 1420/ENGN 2520)

Spring 2015

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Pedro Felzenszwalb
Email: pff (at)
Office: Barus & Holley 355
Office hours: Monday 2-3pm

TA email list: cs142tas (at)

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.

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

Previous Courses
Spring 2013
Fall 2013