Time: Tuesday, Thursday
2:30 am-3:45 am
Location: Sennott Square, Room 5313
Instructor: Milos
Hauskrecht
Computer Science Department
5329 Sennott Square
phone: x4-8845
e-mail: milos_at_cs_pitt_edu
office hours: by appointment
Course description
Lectures
TBA
Paper presentations
Projects
The goal of the field of machine learning is to build computer systems that learn from experience and that are capable to adapt to their environments. Learning techniques and methods developed by researchers in this field have been successfully applied to a variety of learning tasks in a broad range of areas, including, for example, text classification, gene discovery, financial forecasting, credit card fraud detection, collaborative filtering, design of adaptive web agents and others.
The objective of the Advances Machine Learning course is to expand on the material covered in the introductory Machine Learning course (CS2750), and focus on the most recent advances in the ML field such as, latent variable and component models, kernel and variational methods. The course will consist of a mix of lectures, presentations and discussions. Students will be evaluated based on their participation in discussions, presentations and projects.
Prerequisites
CS 2750 Machine Learning , or the permission of the instructor.
We will use readings from:
In addition we will use conference and journal paper readings that will be distributed electronically or in a hardcopy form. Here is a list of papers sorted by the topics we plan to cover during the course. Not all readings on this list will be used in the class; they are provided in the case you want to explore some topics in greater depth.
Other (useful) books
Course webpage for CS2750, the introductory Machine Learning course from Spring 2011. It is the prerequisite of CS3750.
Readings will be assigned before the class at which the discussion on the topic takes place. Most of the readings will be electronic, however, some readings will be in the paper form or from the books. See a summary list of Readings for different topics
Every student is expected to be in charge of at least one topic, present it,
and lead the discussion on the topic during the class.
The readings for each topic will be distributed electronically. The
assignement of the papers will be discussed during the first two week of the
course.
There are no homeworks in this course. However, students will be asked to prepare, submit and present two projects. The first project will be assigned and due in the middle of the semester. The final project (due at the end of the semester) and is more flexible: a student can choose his/her own topic to investigate. You will need to submit a short (one page) proposal for the purpose of approval and feedback for the final project. The final project must have a distinctive and non-trivial learning or adaptive component.