Advanced Introduction to Machine Learning

10-715, Fall 2018

Carnegie Mellon University

Maria-Florina Balcan



Home

People

Lectures

Recitations

Homeworks

Project

Previous material



Time:
Monday and Wednesday from 10:30-11:50am (GHC 4307)

Recitations:
Tuesdays 5-6:30pm (GHC 4215)

Piazza Webpage:
https://piazza.com/cmu/fall2018/10715

Course Description:
The rapid improvement of sensory techniques and processor speed, and the availability of inexpensive massive digital storage, have led to a growing demand for systems that can automatically comprehend and mine massive and complex data from diverse sources. Machine Learning is becoming the primary mechanism by which information is extracted from Big Data, and a primary pillar that Artificial Intelligence is built upon. This course is designed for Ph.D. students whose primary field of study is machine learning, or who intend to make machine learning methodological research a main focus of their thesis. It will give students a thorough grounding in the algorithms, mathematics, theories, and insights needed to do in-depth research and applications in machine learning. The topics of this course will in part parallel those covered in the general graduate machine learning course (10-701), but with a greater emphasis on depth in theory and algorithms. The course will also include additional advanced topics such as additional topics on generalization guarantees, privacy in machine learning, interactive learning, reinforcement learning, and online learning. Students entering the class are expected to have a pre-existing strong working knowledge of algorithms, linear algebra, probability, and statistics. If you are interested in this topic, but do not have the required background or are not planning to work on a PhD thesis with machine learning as the main focus, you might consider the general graduate Machine Learning course (10-701) or the Masters-level Machine Learning course (10-601). ML course comparison: https://goo.gl/mmR2eL

Textbook:
  • Machine Learning, Tom Mitchell.
  • Pattern Recognition and Machine Learning, Christopher Bishop.
  • Understanding Machine Learning: From Theory to Algorithms by Shai Shalev-Shwartz and Shai Ben-David. available online
  • The Elements of Statistical Learning: Data Mining, Inference and Prediction, Trevor Hastie, Robert Tibshirani, Jerome Friedman. available online
  • Machine Learning: A Probabilistic Perspective, Kevin P. Murphy. available online
Grading:
  • Six Homeworks (30%).
  • Midterm (20%)
  • Final (20%)
  • Project (25%)
  • Class Participation (5%)