# Machine Learning (CSCI 1950-F/ENGN 2520)

## Spring 2013

Home
Assignments
Lectures
Matlab

## 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

**Instructor**

Pedro Felzenszwalb

Email: pff (at) brown.edu

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.

**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.

**Previous Courses**

Spring 2012 ENGN 2520

Spring 2012 CSCI 1950-F