Machine Learning (CSCI 1950-F/ENGN 2520)

Spring 2013

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Take-home Midterm due Thursday April 4 at 8pm

Final exam: Friday May 10 at 9am in MacMillan 115

Lecture: Tue/Thu 2:30pm-3:50pm in MacMillan 115

HW Questions:

Pedro Felzenszwalb
Email: pff (at)
Office: Barus & Holley 355
Office hours: Friday 10am-noon

Graduate TA
Sobhan Naderi Parizi

Undergraduate TAs
Ethan Richman (HTA)
Zachary Kahn
Tala Huhe

TA Office hours:
CIT 219, Tuesday 9-11pm (zk)
CIT 219, Wednesday 7-9pm (th)
CIT 219, Wednesday 9-11pm (er)
CIT 367, Thursday 4-6pm (snp)

Notes: We don't have notes but there are great slides from last year's lectures available here

(ENGN 2520) 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 some 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.

Previous Courses
Spring 2012 ENGN 2520
Spring 2012 CSCI 1950-F