Spring 2022 Course Syllabus:  Math 678

 

Course Title:

Introduction to Statistical Methods in Data Science

Textbook:

An Introduction to Statistical Learning: with Applications in R,  by Gareth James, et al.; Publisher: Springer, 1st edition (2013); ISBN: 978-1461471370.

Reference book:

The Elements of Statistical Learning: Data Mining, Inference, and Prediction,  by Hastie, Tibshirani, and Friedman; Publisher: Springer, 2nd edition (2009); ISBN: 978-0387848570.

Prerequisites:

Math 661 or Math 663 or permission by instructor.

 

 

Course Outline

Date

Lecture

Sections

Topic

Assignment

Week 1

1/19

1

 

Chapter 1

 

Introduction to Data Science
 

 

Week 2

1/26

2

Chapter 2

Statistical Learning; KNN

Homework 1

Week 3

2/2

3

Chapter 3

 

Linear Regression; R Lab
 

 

Week 4

2/9

4

Chapter 4

Logistic Regression

Homework 2

Week 5

2/16

5

Chapter 4

LDA, QDA; R Lab

  

Week 6

2/23

6

Chapter 5

Cross-Validation and Bootstrap
 

Homework 3

Week 7

3/2

 7

 Chapter 6

Linear Model Selection; R Lab

 

Week 8

3/9

8

Chapter 6

Shrinkage Methods and Dimension Reduction Methods

Homework 4 

Week 9

3/16

 

 

SPRING RECESS (NO CLASSES)

 

 

Week 10

3/23

 

 

MIDTERM EXAM:

Wednesday ~ March 23, 2022

 

 

Week 11

3/30

9

Chapter 7

Nonlinear Modeling; R Lab

Course Project

Week 12

4/6

10

Chapter 8

Tree-Based Methods: Bagging, Random Forests, Boosting

 Homework 5

Week 13

4/13

11

Chapter 9

Support Vector Machines

 

Week 14

4/20

12

Chapter 10

Unsupervised Learning

 Homework 6

Week 15

4/27

 

 

Students’ Project Presentation

 

deadline of the project report

Week 16

5/4

 

 

Reading Day 1

 

Week 17

5/11

 

 

FINAL EXAM:

Wednesday ~ May 11, 2022

 

 

 

 

IMPORTANT DATES

FIRST DAY OF SEMESTER

January 18, 2022

LAST DAY TO WITHDRAW

April 4, 2022

LAST DAY OF CLASSES

May 3, 2022

FINAL EXAM PERIOD

May 6 – 12, 2022

 

 

Grading Policy

 

Assignment Weighting

 

Tentative Grading Scale

Homework

25 %

 

A

90 -- 100

Project

20 %

 

B+

85 -- 89

Midterm Exam

25 %

 

B

80 -- 84

Final Exam

30%

 

C+

75 -- 79

 

 

 

C

70 -- 74

 

 

 

F

0 -- 69

 

 

 

Important Departmental and University Policies

 

Prepared by Prof. Wenge Guo, December 15, 2021