### Announcements

Instructor's office Hours extended.

### Time and Place

Tue, Thu: 11:40am to 12:55pm

Place: Hollister 110.

### Instructor

Ashutosh Saxena, asaxena @ cs.cornell.edu, 4159 Upson Hall.

Office Hours: Wed 4pm-5:30pm (4159 Upson) or by appointment using email.

TA: Yun Jiang. yunjiang @ cs dot cornell.edu

Office Hours: Mon 4pm-5pm (328 B).

Jean-Baptiste Jeannin. jeannin @ cs.cornell dor edu

Office Hours: Tue 5pm-6pm. (Location: Upson 4142).

### Syllabus

This course gives a graduate-level introduction to machine learning, and then gives an in-depth coverage of new and advanced methods in machine learning. It will make an emphasis on approaches with practical relevance, and discusses a number of recent applications of machine learning, such as to robotics, data mining, computer vision, text and web data processing. An open research project is a major part of the course. Topics include:

- supervised learning (generative/discriminative learning, exponential family, convex problems, kernels)
- unsupervised learning (clustering, EM, PCA/ICA, dimensionality reduction)
- probabilistic graphical models (HMM, MRF, Bayesian Networks, Inference)

Details on the lectures are available here.

### Pre-requisites

Students are expected to have the following background:

- Knowledge of basic computer science principles and skills, at a level sufficient to write a reasonably non-trivial computer program. (E.g., CS 3110 or equivalent.)
- Familiarity with the basic probability theory. (E.g., CS 2800.)
- Strong in basic linear algebra. (E.g., MATH 2210.)

If you're not clear if you satisfy the pre-reqs, please contact the instructor.

### Grading

This is a 4-credit course (letter/S-U). There will be four written
**homeworks**, one **midterm**, and one major open-ended **term
project**. The homeworks will contain written questions and questions that
require some programming. In the term project (details here), you will investigate some
interesting aspect of machine learning or apply machine learning to a problem
that interests you.

- 40%: Homeworks
- 15%: Midterm
- 45%: Major term project
- Upto 5% bonus credit may be awarded for class participation.

All homeworks are due at the beginning of class on the due date. You will have a total of 4 late days, and for each homework a maximum of 2 late days can be used.

Requirements for S-U grade are same as for letter option.

### Course Materials

There is no required text for this course. Notes will be posted periodically on the course website. The following books are recommended as optional reading.

Covers supervised learning well, and also graphical models. This one will cover large parts of the material in the course.

Pattern Recognition and Machine Learning. Christopher Bishop, Springer, 2006.

Additional reading from statistical point of view:

The Elements of Statistical Learning (2nd ed). Hastie, Tibshirani, Friedman, Springer, 2008.

Additional reading for probabilistic graphical models:

Probabilistic Graphical Models. Koller and Friedman, MIT Press, 2009.

Course handouts and other materials can be downloaded
from here.

The course website is: http://www.cs.cornell.edu/Courses/CS6780/

- Academic Integrity
- This course follows the Cornell University Code of Academic Integrity. Each student in this course is expected to abide by the Cornell University Code of Academic Integrity. Any work submitted by a student in this course for academic credit will be the student's own work. Violations of the rules (e.g., cheating, copying, non-approved collaborations) will not be tolerated.