Time: Mon. & Wed. 5:00 PM - 6:20 PM (Location: Javigs 109)
Virtual?: I am not planning to record lectures this semester.
Key online resources: Brightspace (most official), Piazza (basically official), this website (least official)
Syllabus: here
Instructor: Yifan Sun
TA: tbd
Auditors: Welcome to join and access all resources on this website. (Sign up for piazza)
Please email me if you cannot resolve any technical issues: ysun at cs dot stonybrook dot edu
Prerequisite: CSE 316 or CSE 351; CSE or DAS major
Pre- or Co-requisite: AMS 310 or AMS 311 or AMS 412
Homework 0: Here Please review this to see if you have the necessary preparation for this course.
Gain a general familiarity over a wide range of ML tools
Understand why different ML tools thrive for different tasks, by implementing a handful of them from scratch
Understand the "big ML questions", e.g. the role of ML in technology, the use of data representations, the need for generalization, the quality of "good data", etc
Toolset 1: nearest neighbor, decision tree, naive bayes, graphical models / hmm
Toolset 2: linear regression, optimization, logistic regression, SVMs, multiclass classification
Advanced topics: Kmeans, GMMs, bagging methods, boosted methods
5 lecture homeworks (40 points), 2 midterms (30 pts), 3 coding assignments (30 pts),