|Time/place||MW 12:30pm-1:45pm 1404 Siebel / Catalog|
|Web forum||Piazza » (sign up!)|
The goal of Machine Learning is to build computer systems that can adapt and learn from their experience. This course will study the theory and application of learning methods that have proved valuable and successful in practical applications. We review the theory of machine learning in order to get a good understanding of the basic issues in this area, and present the main paradigms and techniques needed to obtain successful performance in application areas such as natural language and text understanding, speech recognition, computer vision, data mining, adaptive computer systems and others. The main body of the course will review several supervised and (semi/un)supervised learning approaches. These include methods for learning linear representations, Bayesian / Probabilistic methods, decision-tree methods, kernel based methods and neural networks, as well as clustering and dimensionality reduction techniques. We will also discuss how to model machine learning problems and discuss some open problems.
Students are expected to have taken a class in linear algebra, probability and statistics and a basic class in theory of computation and algorithms. Students are expected to be familiar with the python programming language.
Lecture notes, course handouts, pointers to relevant papers, and other materials will be available as HTML and PDF documents on Relate and Piazza
|name and role||contact (illinois.edu)||office hours||location|
|Sanmi Koyejo | Instructor||sanmi||Friday 1pm - 2pm, or by appointment||3314 SC|
|Haroun Habeeb | TA||hhabeeb2||Wednesday 2pm - 4pm||Whiteboard outside of 3407 SC|
|Sameer Manchanda | TA||manchan2||Tuesday 9am - 11am||Whiteboard outside of 3301 SC|
|Xiaoyan Wang | TA||xiaoyan5||Thursday 6pm - 8pm||Whiteboard outside of 3407 SC|
|Simon Xie | TA||cx2||Monday 4pm - 6pm||Whiteboard outside of 3407 SC|
|Kabir Manghnani | CA||kabirm2||Friday 2pm - 4pm||Whiteboard outside of 3310 SC|
|Duke Vijitbenjaronk | CA||wdv2||Friday 2pm - 4pm||Whiteboard outside of 3310 SC|
|Qingran Wang | CA||qwang78||N/A||N/A|
|Zecheng Zhang | CA||zzhan147||N/A||N/A|
Exam will be in class exam with 75 minutes. Please find information on our upcoming exams in the corresponding section of the class calendar.
(Notice that unlike homework (which will be auto-collected), you will need to manually submit your lecture scribe by clicking the submit button on the top right corner.)
Scribe submission has already ended. You can view the final version of the signup sheet here
Please use the LaTeX template. Scribing expectations are further elaborated upon in the template.
Here is an example lecture scribe note.
Feel free to discuss the assignment with each other in general terms, and to search the Web for general guidance (not for complete solutions). All solutions should be written up individually. If you make substantial use of some information from outside sources, make sure you acknowledge the sources in your solution. In particular, make sure you acknowledge all other students you worked with on the homework/projects. Failure to do this will result in a zero grade. We will follow the departmental honor code policy here: https://cs.illinois.edu/academics/honor-code
We will be using Python with the libraries numpy, scipy and matplotlib for assignments. No other languages are permitted. Python has a very gentle learning curve, so you should feel at home even if you've never done any work in Python.