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CSE 158: Web Mining and Recommender Systems

Fall 2017, Monday/Wednesday 17:00-18:20, Galbraith Hall

CSE 158 is an undergraduate course devoted to current methods for recommender systems, data mining, and predictive analytics. No previous background in machine learning is required, but all participants should be comfortable with programming (all example code will be in Python), and with basic optimization and linear algebra.

The course meets twice a week on Monday/Wednesday evenings, starting October 2. Meetings are in Galbraith Hall.

There is no textbook for the course, though chapter references will be provided from Pattern Recognition and Machine Learning (Bishop), and from Charles Elkan's 2013 course notes.

Office hours: I'll hold office hours on Tuesdays 9:00-13:00 in CSE 4102. The course TAs will hold additional office hours from 10:00-13:00, Mondays and Fridays in CSE B275. For other discussions see the course's Piazza page.

Grading: All reports will be submitted via gradescope, and (except where the spec states otherwise) are expected to be completed individually. Your lowest (of four) homework scores will be discarded (or you are welcome not to submit one of the homeworks). You are also allowed a single "late day" for any report, i.e., if you submit one report late by one day there will be no penalty, but further late reports will not be graded.

Part 1: Methods

Part 2: Applications