Fall 2023 Course Syllabus: Math 680

Course Title:	Advanced Statistical Learning
Textbook:	The Elements of Statistical Learning: Data Mining, Inference, and Prediction, by Hastie, Tibshirani, and Friedman; Publisher: Springer; 2nd edition, 2009; ISBN: 978-0387848570.
Reference Books:	An Introduction to Statistical Learning: with Applications in R, by James, Witten, Hastie, and Tibshirani (2021), 2nd edition, Springer. An Introduction to Statistical Learning: with Applications in Python, by James, Witten, Hastie, and Tibshirani (2023), 1st edition, Springer.
Prerequisites:	Math 478 or Math 678, or permission by instructor

Course Outline
Date	Lecture	Chapter	Topic	Assignment
Week 1 9/5	1	Chapter 1	Overview and Introduction, Application Examples.
Week 2 9/12	2	Chapter 2	Overview of Supervised Learning: Statistical Decision Theory
Week 3 9/19	3	Chapter 4	Binary Classification (I): Basics Binary Classification (II): Logistic Regression, Discriminant Analysis	Homework 1
Week 4 9/26	4	Chapter 2	Multiclass Classifications
Week 5 10/3	5	Chapter 4	Nonlinear Classification Methods: K-nearest neighbor (KNN) methods
Week 6 10/10	6	Chapter 4	Nonlinear Discriminant Analysis: QDA and RDA	Homework 2
Week 7 10/17	7	Chapter 14.5 & 3	Linear Regression Models
Week 8 10/24		Chapter 3	Variable Selection for Linear Regression  Shrinkage Methods by LASSO
Week 9 10/31	8	Chapter 3 & 7	Model Selection and Assessment	Homework 3
Week 10 11/7	9	Chapter 4.5 & 12	Support Vector Machines
Week 11 11/14	10	Chapter 9 & 8.7	Tree-based Methods
Week 12 11/21	11		Tuesday, Nov 21, 2023 (Thursday Classes Meet) FINAL EXAM: Wednesday, Nov 22, 2023  (Friday Classes Meet)    Friday, Nov 24: Thanksgiving Recess	Homework 4
Week 13 11/28	12	Lecture notes	Uncertainty Quantification (I): Standard conformal prediction
Week 14 12/5	13	Lecture notes	Uncertainty Quantification (II): Conformal prediction under distribution shift
Week 15 12/12			Students’ Project Presentation

IMPORTANT DATES
FIRST DAY OF SEMESTER	September 5, 2023
LAST DAY TO WITHDRAW	November 13, 2023
LAST DAY OF CLASSES	December 13, 2023
FINAL EXAM PERIOD	December 17 – 23, 2023

Grading Policy

Assignment Weighting		Tentative Grading Scale
Homework	20 %	A	90 -- 100
Project	40 %	B+	85 -- 90
Final Exam	40 %	B	80 -- 85
		C+	75 -- 80
		C	70 -- 75
		F	0 -- 70

Important Departmental and University Policies

• Academic Integrity Code is Strictly Enforced
• Prerequisites Requirements are Enforced
• Attendance is Required in Lower-Division Courses
• Exam Policies (No Make Up Exams and More)
• Cell Phone and Pager Use Prohibited in Class
• Drop Date (Nov. 13, 2023) is Strictly Observed
• Complete DMS Course Policies (math.njit.edu/students/undergraduate/policies_math)

Prepared by Prof. Wenge Guo, September 1, 2023