cse-158-fall-2022's Introduction

CSE 158

Couse: UCSD CSE 158

Quarter: Fall 2022

Instructor: Julian McAuley

Course Information

CSE 158: Recommender Systems and Web Mining (4 units)

Current methods for data mining and predictive analytics. Emphasis is on studying real-world data sets, building working systems, and putting current ideas from machine learning research into practice. Prerequisites: CSE 12 or DSC 40B and CSE 15L or DSC 80 and BENG 100 or BENG 134 or COGS 118D or CSE 103 or ECE 109 or ECON 120A or MATH 180A or MATH 181A or MATH 183 or MATH 186;

Link to Course Website

Homeworks

	Requirement	Code	Answers	Score
HW1	Requirement	py	Answers	8/8
HW2	Requirement	ipynb and py	Answers	8/8
HW3	Requirement	ipynb and py	Answers	8/8
HW4	Requirement	ipynb and py	Answers	8/8

Assignments

	Style	Requirement	Code	Writeup/Report	Score
Assignment 1	Individually	Requirement	ipynb and py	Writeup, Predictions Category, and Predictions Read	25/25
Assignment 2	Group of 4 people	Requirement	ipynb for prediction and ipynb for analysis dataset	Report	22.5/25

Midterm

	Requirement	Code	Answers	Score
Midterm	Requirement	ipynb and py	Answers	26/26

Note for Lecture

Week	Subject	Note
Week 0/1	Supervised Learning: Regression	Chapter 2 (website)
Week 1/2	Supervised Learning: Classification	Chapter 3 (website)
Week 3/4/5	Recommender Systems	Chapter 4 (website) and Chapter 5 (website)
Week 6	Midterm	--
Week 6/7	Text Mining	Chapter 8 (website)
Week 7	Content and Structure in Recommender Systems	Chapter 6 (website)
Week 8	Modeling Temporal and Sequence Data	Chapter 7 (website)
Week 9	Visual Recommendation	Chapter 9 (website)
Week 9/10	Ethics and Fairness	Chapter 10 (website)

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