Modeling and Data Analysis 📊

Lucy Lai

lai@ucsd.edu

Fall 2026

COA 130 ↗

MWF 9:00-9:50am

If you are joining the course late, please complete all assignments by the Friday of Week 3 (there will be no late penalty and you do not need to email us)!

Please note that I have no control over the waitlist. Please email cogsadvising@ucsd.edu or drop in their office hours (your best bet). If you intend to enroll, I would keep up with class while you’re waiting to get off the waitlist!

Discussion Sections

All sections meet on Fridays @ RWAC 0103 ↗.

• F, 3-3:50pm (Jiesen + Natalia)
• F, 4-4:50pm (Zhicheng + Jerry)
• F, 5-5:50pm (Tanvi + Cissy)

Office Hours

Monday	Tuesday	Wednesday	Thursday	Friday
Zhicheng (TA)4–5pm @ CSB Courtyard ↗	Jerry (PLA)2–3pm @ HDSI 155 ↗	Prof. Lai2–3:15pm 1-1s (booked) @ CSB 244 ↗ 3:15–4:30pm open, walk-in @ CSB Courtyard ↗	Cissy (PLA)10–11am @ CSB Courtyard ↗	All TAs & PLAsDuring discussion section
		Tanvi (TA)2–3pm @ HDSI 155 ↗	Natalia (PLA)3pm on Zoom ↗
			Jiesen (TA)5–6pm @ HDSI 155 ↗

Course Calendar

🗓️	Week	Topic / ISLP Reading	Mon Lecture	Wed Lecture	Fri Lecture & Section
Sept	0	Ch 1			Sept 25 Course intro ↗Webclicker 101 ↗ No section this week
	1	Intro Ch 2.1–2.2 Lab: Ch 2.3	Sept 28Lec 1 What is statistical learning? Prediction vs. inference ↗ Pre-course survey Syllabus quiz Homework 1	Sept 30Lec 2 Assessing model accuracy: the bias-variance trade-off ↗	Oct 2Lec 3 Question-driven exploratory data analysis ↗ Section Meet your TAs Establish expectations Week 1 review3pm slides ↗4pm slides ↗5pm slides ↗
Oct	2	Regression Ch 3.1–3.2 Lab: Ch 3.6	Oct 5Lec 4 Simple linear regression ↗ Quiz 1 Homework 1 Homework 2	Oct 7Lec 5 Multiple linear regression ↗	Oct 9Lec 6 Qualitative variables in regression Section Week 2 review
	3	Regression (cont’d) Ch 3.3, Ch 7.1–7.3, Ch 10.1 Lab: Ch 3.6	Oct 12Lec 7 Non-linear features in regression (interactions, polynomial, log) Quiz 2 Homework 2 Project groups survey	Oct 14Lec 8 Diagnosing problems in linear regression	Oct 16Lec 9 Regression neural networks Project groups assigned Section Week 3/Exam 1 review Project group work
	4	Exam 1 & Classification Ch 4.1–4.3.4 Lab: Ch 4.7.1–4.7.2	Oct 19 EXAM 1 Quiz 3 Homework 3	Oct 21Lec 10 Simple logistic regression	Oct 23Lec 11 Multiple logistic regression Last day to drop without a “W” Section Week 4 review Project group work
	5	Classification (cont’d) Ch 4.3.5, Ch 10.1 Resampling Ch 5.1–5.2 Lab: Ch 5.3	Oct 26Lec 12 Multinomial logistic regression & classification neural networks Quiz 4 Homework 4 Project Checkpoint 1*	Oct 28Lec 13 Cross-validation	Oct 30Lec 14 Bootstrap methods Section Week 5 review Project group work
Nov	6	Model selection Ch 6.1–6.2 Lab: Ch 6.5.1–6.5.2	Nov 2Lec 15 Model selection Quiz 5 Homework 5	Nov 4Lec 16 Regularization	Nov 6Lec 17 Predictive modeling workflow Last day to drop with a “W” Section Week 6/Exam 2 review Project group work
	7	Exam 2 & Break	Nov 9 EXAM 2 Quiz 6 Homework 6	Nov 11 NO CLASS Veterans Day holiday	Nov 13 NO CLASS Prof. Lai giving talks in NYC Section Project group work
	8	Tree Methods Ch 8.1–8.2 Lab: Ch 8.3	Nov 16Lec 18 Decision trees Project Checkpoint 2*	Nov 18Lec 19 Bagging, random forests, boosting	Nov 20Lec 20 Modern tree methods (XGBoost, LightGBM, CatBoost) Section Week 8 review Project group work
	9	Unsupervised Learning Ch 12.1–12.2 Lab: Ch 12.5.1	Nov 23Lec 21 Principal components analysis (PCA) Quiz 7 Homework 7	Nov 25Lec 22 PCA (cont’d)	Nov 27 NO CLASS Thanksgiving holiday No section
Dec	10	Unsupervised Learning (cont’d) Ch 12.4, Ch 12.5.3–12.5.4 Exam 3 & Final Project Expo	Nov 30Lec 23 K-means & hierarchical clustering — virtual lecture Quiz 8 Homework 8	Dec 2 EXAM 3	Dec 4 COURSE PROJECT EXPO & course conclusion Dec 5–7 · Project oral exams Project Checkpoint 3 (Final submission)* No section
	11	Finals week	Dec 7	Dec 9 ELECTIVE FINAL EXAM 9–11am @ COA 130 For those who did not complete the course project	Dec 11

All assignments are due by 11:59pm PT. Assignment names link to where you submit them.
* indicates a group submission.