Fall 2019 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 (2013), Springer.
Prerequisites:	Math 478 or Math 678, or permission by instructor

Course Outline
Date	Lecture	Chapter	Topic	Assignment
Week 1 9/3	1	Chapter 1	Overview and Introduction, Application Examples.
Week 2 9/10	2	Chapter 2	Overview of Supervised Learning: Statistical Decision Theory	Homework 1
Week 3 9/17	3	Chapter 4	Binary Classification (I): Basics Binary Classification (II): Logistic Regression, Discriminant Analysis
Week 4 9/24	4	Chapter 18 & 2	Binary Classification (III): Extension to High Dimensional Classification Problems Nonlinear Classification Methods: K-nearest neighbor (Knn) methods.	Homework 2
Week 5 10/1	5	Chapter 4	Multiclass Classifications
Week 6 10/8	6	Chapter 4	Nonlinear Discriminant Analysis (I): QDA and RDA	Homework 3
Week 7 10/15	7	Chapter 14.5 & 3	Nonlinear Discriminant Analysis (II):  PCA  Linear Regression Models
Week 8 10/22		Chapter 3	Variable Selection for Linear Regression  Shrinkage Methods by LASSO
Week 9 10/29	8	Chapter 3 & 7	Beyond LASSO Model Selection and Assessment	Statistical Learning Project Homework 4
Week 10 11/5	9	Chapter 4.5 & 12	Modern Classification vis Separating Hyperplanes Support Vector Machines
Week 11 11/12	10	Chapter 12	Multiclass Support Vector Machines  Optimization Programming	Homework 5
Week 12 11/19	11	Chapter 9 & 8.7	Tree-based Methods: Classification and Regression Trees Bagging
Week 13 11/26	12	Chapter 10	Boosting and Additive Trees Friday, Nov 29: Thanksgiving Recess	Homework 6
Week 14 12/3	13	Chapter 14	Cluster Analysis
Week 15 12/10			Students’ Project Presentation Friday, Dec. 13: Reading Day 2
Week 16 12/17			FINAL EXAM: Tuesday ~ December 17, 2019

IMPORTANT DATES
FIRST DAY OF SEMESTER	September 3, 2019
LAST DAY TO WITHDRAW	November 11, 2019
LAST DAY OF CLASSES	December 11, 2019
FINAL EXAM PERIOD	December 14 – 20, 2019

Grading Policy

Assignment Weighting		Tentative Grading Scale
Homework	30 %	A	90 -- 100
Project	35 %	B+	85 -- 90
Final Exam	35 %	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. 11, 2019) is Strictly Observed
• Complete DMS Course Policies (math.njit.edu/students/undergraduate/policies_math)

Prepared by Prof. Wenge Guo, August 8, 2019