Instructor office hours: Whichever prof teaches that Wednesday will have office hours at 2:50 (i.e. right after the second class ends, including walking back with anyone who wants to chat while walking, and then office hours will conclude in that profesor's office). The hour will be 2:50-3:50. Students can go straight to the office and wait for the professor to return. Professor Clune's office is X863. Professor Ding's is ICCS X541.
Tutorials (beginning January 15):
Midterm information
TBD
Synopsis: We introduce basic principles and techniques in the fields of data mining and machine learning. These techniques are now running behind the scenes to discover patterns and make predictions in various applications in our daily lives. We will focus on many of the core data mining and machine learning technologies, with motivating applications from a variety of disciplines.
Registration: Undergraduate and graduate students from any department are welcome to take the course. Undergraduate students should enroll in CPSC 340 while graduate students should enroll in CPSC 540 (when it is offered; CPSC 540 also has an extra small project component). Below are more details on registration for each course:
Prerequisites:
Textbook: There is no required textbook for the class. A introductory book that covers many (but not all) the topics we will discuss is the Artificial Intelligence book of Rusell and Norvig (AI:AMA) or the Artificial Intelligence book of Poole and Mackworth (you may need these for other classes). More advanced books include The Elements of Statistical Learning (ESL) by Hastie et al., Murphy's Machine Learning: A Probabilistic Perspective (ML:APP) which can be accessed through the library here, and Bishop's Pattern Recognition and Machine Learning (PRML). For books with a bigger focus on data mining, see Introduction to Data Mining (IDM) and Mining of Massive DataSets.
Related Courses: The most related course is CPSC 330: Applied Machine Learning. This course has fewer prerequisities and covers some of the same material, but focuses more on applications rather than understanding ML ideas in depth. A discussion on the difference between CPSC 340 and similar courses in statistics written by a former student (Geoff Roeder) is available here (this was written in 2016 so may be out of date).
Grading (tentative):
Assignments: There are a total of 6 written assignments for this course. Please follow the instructions linked here to submit your assignments.