Difference between revisions of "Machine Learning 10-601 in Fall 2013"
(Created page with '== Instructor and Venue == * Instructors: [http://www.cs.cmu.edu/~wcohen William Cohen] and [http://www.cs.cmu.edu/~epxing Eric Xing], Machine Learning Dept and LTI * Course se…') |
|||
| Line 18: | Line 18: | ||
== Description == | == Description == | ||
| − | . | + | Machine Learning (ML) asks "how can we design programs that automatically improve their performance through experience?" This includes learning to perform many types of tasks based on many types of experience, e.g. spotting high-risk medical patients, recognizing speech, classifying text documents, detecting credit card fraud, or driving autonomous robots. |
| + | |||
| + | Topics covered in 10-601 include concept learning, version spaces, decision trees, neural networks, computational learning theory, active learning, estimation & the bias-variance tradeoff, hypothesis testing, Bayesian learning, the Minimum Description Length principle, the Gibbs classifier, Naïve Bayes classifier, Bayes Nets & Graphical Models, the EM algorithm, Hidden Markov Models, K-Nearest-Neighbors and nonparametric learning, reinforcement learning, genetic algorithms, bagging and boosting. | ||
| + | |||
| + | 10-601 focuses on the mathematical, statistical and computational foundations of the field. It emphasizes the role of assumptions in machine learning. As we introduce different ML techniques, we work out together what assumptions are implicit in them. We use the Socratic method whenever possible, and student participation is expected. Grading is based on written assignments, programming assignments, and a final exams. | ||
| + | |||
| + | 10-601 focuses on understanding what makes machine learning work. If your interest is primarily in learning the process of applying ML effectively, and in the practical side of ML for applications, you should consider Machine Learning in Practice (11-344/05-834). | ||
| + | |||
| + | 10-601 is open to all but is recommended for CS Seniors & Juniors, Quantitative Masters students, and non-SCS PhD students. | ||
== Syllabus == | == Syllabus == | ||
Revision as of 16:07, 29 May 2013
Instructor and Venue
- Instructors: William Cohen and Eric Xing, Machine Learning Dept and LTI
- Course secretary: Sharon Cavlovich, sharonw+@cs.cmu.edu, 412-268-5196
- When/where: M/W 1:30-2:50, Doherty Hall 2315
- Course Number: ML 10-601
- Prerequisites:
- to be listed
- TAs:
- Syllabus: Syllabus for Machine Learning 10-601 in Fall 2013.
- Office hours:
- To be announced
- Email and forum:
- To be announced
Description
Machine Learning (ML) asks "how can we design programs that automatically improve their performance through experience?" This includes learning to perform many types of tasks based on many types of experience, e.g. spotting high-risk medical patients, recognizing speech, classifying text documents, detecting credit card fraud, or driving autonomous robots.
Topics covered in 10-601 include concept learning, version spaces, decision trees, neural networks, computational learning theory, active learning, estimation & the bias-variance tradeoff, hypothesis testing, Bayesian learning, the Minimum Description Length principle, the Gibbs classifier, Naïve Bayes classifier, Bayes Nets & Graphical Models, the EM algorithm, Hidden Markov Models, K-Nearest-Neighbors and nonparametric learning, reinforcement learning, genetic algorithms, bagging and boosting.
10-601 focuses on the mathematical, statistical and computational foundations of the field. It emphasizes the role of assumptions in machine learning. As we introduce different ML techniques, we work out together what assumptions are implicit in them. We use the Socratic method whenever possible, and student participation is expected. Grading is based on written assignments, programming assignments, and a final exams.
10-601 focuses on understanding what makes machine learning work. If your interest is primarily in learning the process of applying ML effectively, and in the practical side of ML for applications, you should consider Machine Learning in Practice (11-344/05-834).
10-601 is open to all but is recommended for CS Seniors & Juniors, Quantitative Masters students, and non-SCS PhD students.
Syllabus
Previous syllabus, for the historically-minded:
- ...
Prerequisites
...
Projects
...
