**Announcements**

- 4/14:
HW4 is out -- due Apr 30th.
- 3/24:
Note the change (!) of lecture room. We will meet in CS 2129 for the rest of semester.
- 3/24:
HW3 is out -- due Apr 14th.
- 3/2:
Final Exam schedule: May 19 (11:15-13:45) (see link)
- 2/26:
HW2 is out -- due Mar 24th.
- 2/10:
HW1 is out -- due Feb 26th (submit hard copy in class).
- 1/31:
Vivek's office hour changed as Thur 3:00PM - 4:00PM.
- 1/16:
The class is full capacity, therefore auditing/sitting in is not allowed.
- 1/16:
There is a required
textbook for this course. See below.
- 1/16:
TA information is up to date. Please contact Vivek for questions on lectures/HWs.
- 1/16:
Course lectures will be posted on the Blackboard.
- 1/1:
Welcome to the class! Hope you will enjoy it :)

# PEOPLE:

**Instructor:** Leman Akoglu
**Office:** 1425 Computer Science
**Office hours:** Tue 2:30PM - 3:30PM
**Email:** *invert* (cs.stonybrook.edu @ leman)

**Teaching Assistant:** Vivek Kulkarni
**Office hours:** Thur 3:00PM - 4:00PM
**Location:** CS 2110 (TA advising room)
**Email:** *invert* (cs.stonybrook.edu @ vvkulkarni)

# CLASS MEETS:

**Time:** Tue & Thu 5:30PM - 6:50PM

**Place:** ~~EARTH & SPACE SCIENCES 131~~ COMPUTER SCIENCE 2129 (Advanced Programming Lab)

# COURSE DESCRIPTION:

We are drowning in information and starving for knowledge. — John Naisbitt
Machine Learning is centered around automated methods that improve their own performance through learning patterns in data, and then use the uncovered patterns to predict the future and make decisions.
Examples include document/image/handwriting classification, spam filtering, face/speech recognition, medical decision making, robot navigation, to name a few.
See

this for an extended introduction.

This course covers the theory and practical algorithms for machine learning from a variety of perspectives.
The topics include Bayesian networks, decision tree learning, Support Vector Machines, statistical learning methods and unsupervised learning, as well as
theoretical concepts such as the PAC learning framework, margin-based learning, and VC dimension.
Short programming assignments include hands-on experiments with various learning algorithms, and a larger course project gives students a chance to dig into an area of their choice.
See the

syllabus for more.

This course is designed to give a graduate-level student a thorough grounding in the methodologies, technologies, mathematics and algorithms currently needed by people who do research in machine learning.

**TEXTBOOKS:**

The official textbook for the course is listed below. I will post all the lecture notes on

Blackboard.

Readings are posted

here.

Below you can also find a list of other recommended reading.

- Kevin P. Murphy,

"Machine Learning: a Probabilistic Perspective," The MIT Press, 2012. (optional)
- Tom Mitchell,

"Machine Learning," McGraw Hill, 1997. (optional)
- Ethem Alpaydin,

"Introduction to Machine Learning," The MIT Press, 2004. (optional)
- Trevor Hastie, Robert Tibshirani, Jerome Friedman

The Elements of Statistical Learning: Data Mining, Inference, and Prediction," FREE! (optional)

# BULLETIN BOARD and other info

# MISC - FUN:

Fake (ML) protest