Spring 2020 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

1/22

1

 

Chapter 1

 

Introduction to Data Science
 

 

Week 2

1/29

2

Chapter 2

Statistical Learning; KNN

Homework 1

Week 3

2/5

3

Chapter 3

 

Linear Regression; R Lab
 

 

Week 4

2/12

4

Chapter 3

Linear Regression (Cont.)

Homework 2

Week 5

2/19

5

Chapter 4

Logistic Regression

  

Week 6

2/26

6

Chapter 4

LDA, QDA; R Lab
 

Homework 3

Week 7

3/4

 7

 Chapter 5

Cross-Validation and Bootstrap

 

Week 8

3/11

 

 

MIDTERM EXAM:

Wednesday ~ March 11, 2020

 

Week 9

3/18

 

 

SPRING RECESS (NO CLASSES)

 

 

Week 10

3/25

8

Chapter 6

Linear Model Selection; R Lab

Homework 4

 

Week 11

4/1

9

Chapter 6

Shrinkage Methods and Dimension Reduction Methods

Course Project

Week 12

4/8

10

Chapter 7

Nonlinear Modeling; R Lab

 Homework 5

Week 13

4/15

11

Chapter 8

Tree-Based Methods: Bagging, Random Forests, Boosting

 

Week 14

4/22

12

Chapter 9

Support Vector Machines

 Homework 6

Week 15

4/29

13

Chapter 10

Unsupervised Learning

 

Week 16

5/6

 

 

Reading Day 1

 deadline of the project report

Week 17

5/13

 

 

FINAL EXAM:

Wednesday ~ May 13, 2020

 

 

 

 

IMPORTANT DATES

FIRST DAY OF SEMESTER

January 21, 2020

LAST DAY TO WITHDRAW

April 6, 2020

LAST DAY OF CLASSES

May 5, 2020

FINAL EXAM PERIOD

May 8 – 14, 2020

 

 

Grading Policy

 

Assignment Weighting

 

Tentative Grading Scale

Homework

25 %

 

A

90 -- 100

Project

15 %

 

B+

85 -- 89

Midterm Exam

25 %

 

B

80 -- 84

Final Exam

35%

 

C+

75 -- 79

 

 

 

C

70 -- 74

 

 

 

F

0 -- 69

 

 

 

Important Departmental and University Policies

 

Prepared by Prof. Wenge Guo, January 14, 2020