Machine Learning (CSCI 1420/ENGN 2520)

Spring 2017

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

TA email list: cs1420tas (at)

The TAs have set up a page on Piazza for questions and discussions.
Piazza page

Tyler Dae Devlin (
Hong Jun Choi (
Gabe Hope (
Huan Lin (
Valentin Perez (
Akash Suresh (
Keshav Vemuri (
Daniel Xiang (

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.

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.

The breakdown of the final grades will be approximatelly as follows:
Homeworks 60%
Midterm 20%
Final 20%

Midterm: Thursday April 6 in class.

Previous Courses
Fall 2015 Spring 2015