Machine Learning

10-701/15-781, Spring 2014

Barnabas Poczos, Aarti Singh

Home People Lectures Recitations Homeworks Project Previous material Table of algorithms


Date and Time: Tuesday and Thursday, 1:30 - 2:50 pm
Location: 7500 Wean Hall

Recitation: Date and Time: Wednesday, 6:00-7:00 pm
Location: 7500 Wean Hall

Office Hours:
  • Aarti Singh, outside 7500 Wean Hall, Tuesday 3-4pm
  • Barnabas Poczos, outside 7500 Wean Hall, Thursday 3-4pm

Course Description:

It is hard to imagine anything more fascinating than automated systems that improve their performance through experience. Examples range from robots learning to better navigate based on experience gained by roaming their environments, medical decision aids that learn to predict which therapies work best for which diseases based on historical health records, and speech recognition systems that lean to better understand your speech based on experience listening to you. Machine learning is concerned with the study and development of techniques that can automatically learn from data. 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, and related discplines and applications.

Prerequisites: Students entering the class are expected to have a pre-existing working knowledge of probability, linear algebra, statistics and algorithms, though the class has been designed to allow students with a strong numerate background to catch up and fully participate. In addition, recitation sessions will be held to revise some basic concepts. Please also take the self-assessment exam to make sure you have the right background.

Recommended Textbooks:
  • Pattern Recognition and Machine Learning, Christopher Bishop.
  • Machine Learning: A probabilistic perspective, Kevin Murphy.
  • Machine Learning, Tom Mitchell.
  • The Elements of Statistical Learning: Data Mining, Inference and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman.
  • Homeworks (40%)
  • Project (30%)
  • Mid term exam (late into the semester) (20%)
  • Peer grading (10%)
No late days will be allowed for homeworks.

We will be following a method of peer grading for homeworks. There will be a web based system which students will use to grade homeworks. Students will be randomly alloted homeworks to grade and each homework will be graded by 3 students. The TAs will supervise this process and handle any discrepancies that may arise.

Auditing: Students are welcome to audit the course, given there are enough seats. We have a large number of students on the waitlist, so we would like to give them first preference.

Please, send the instructors an email and submit the approval form before the deadline.

  • If you are on the waiting list, note that you will be allowed to enroll if there is space and you meet the pre-requisites.
  • All class discussions and announcements will take place on Piazza.
Feedback: Your feedback is highly appreciated. Please leave your anonym comments here.