• 05/01/2017: Two past final exams have been released with solutions. Previous Final Exams.
• 04/28/2017: Quiz 10 is released. It is due 05/01 11:59pm .
• 04/25/2017: Solution to homework 6 is released.
• 04/20/2017: Homework 7 was released on 04/20. It is due 05/01 . You may not use late hours for this homework.
• 04/17/2017: Homework 6 was released on 04/12. It is due 04/20 .
• 04/14/2017: Quiz 9 is released. It is due 04/17 11:59pm .
• 03/16/2017: Solution to Quiz 7 is released.
• 03/15/2017: Homework 5 is released. It is due 04/11 .
• 03/29/2017: Solution to Midterm is released.
• 03/16/2017: Solution to Quiz 6 is released.
• 03/13/2017: Solution to homework 4 is released.
• 02/28/2017: Homework 4 is released. It is due 03/11 . You may not use late hours for this homework.
• 02/28/2017: Quiz 5 is released. It is due 03/03 11:59pm .
• 02/21/2017: Homework 2 solutions are out.
• 02/17/2017: Quiz 4 is released. It is due 02/20 11:59pm .
• 02/15/2017: Homework 3 is released. It is due 02/27 .
• 02/03/2017: Homework 2 is released. It is due 02/15 .
• 02/03/2017: Quiz 2 is released. It is due 02/06 11:59pm . Only one submission is allowed.
• 1/25/2017: Homework 1 is released. It is due 02/03 . Homework 0 solutions are out.
• 1/24/2017: Quiz 1 has been graded on Compass. For those who are not yet registered for the class you may view the quiz (with solutions) here.
• 1/20/2017: Quiz 1 is released on Compass and is due Monday, 1/23/2017 at 11:59PM. If you are not yet registered for the class you will gain access to it then.
• 1/16/2017: Welcome to CS446, Introduction to Machine Learning.
• 1/16/2017 An important note to students that want to take the class and were not able to register: Start attending the first few meetings of the class as if you are registered. We will maintain a waiting list and, given that some students will drop the class, some space will free up. There is no way to give any guarantees but, in the past, most of the interested students were able to get in.
• 1/16/2017: We will make Homework 0 available soon. This homework will not be graded but we will supply solutions for it. The goal is to give you an idea for the level of mathematical preparation we require in the class. I will discuss it on the first meeting on Tuesday.



• 1/16/2017: We will use Piazza as an online platform to facilitate interaction among students and the course team. Please enroll.

Old announcements

Course Information

	Course Information
	Handouts & Resources
	Schedule
	Lecture Notes
	Problem Sets & Exams

Books

Text: Tom Mitchell, Machine Learning (McGraw Hill 1997) I will not follow the text book closely - a lot of the material covered isn't there, but it is still a useful book to have. Some, but not all of the material covered is in the recommended books below. All the books listed below are reserved in the Engineering Library. If you plan to do research in Machine Learning or related areas you will eventually need these books; otherwise, borrowing a copy is sufficient for now (it's expensive)

Recommended books: Duda and Hart, Pattern Recognition and Scene Analysis (Wiley 1973 - Out of Print) Duda, Hart, and Stork, Pattern Classification (Wiley 2000) J. Shawe-Taylor and N Crisianini, Kernel Methods for Pattern Analysis (Cambridge University Press 2004) C. M. Bishop, Pattern Recognition and Machine Learning (Springer 2006) Kearns and Vazirani, Introduction to Computational Learning Theory (MIT Press 1994) Shalev-Shwartz and Ben-David,, Understanding Machine Learning, From Theory to Algorithms (Cambridge Press 2014) Don't forget to visit the handouts and resources page for other introductory material on linear algebra, probability, and propositional logic.