Welcome Aboard 🙌

MSSC 6250 Statistical Machine Learning

Dr. Cheng-Han Yu
Department of Mathematical and Statistical Sciences
Marquette University

Taipei, Taiwan

• Hello everyone, how are you? I hope you have a great winter break. Welcome to 6250 Statistical Machine Learning course. I am your instructor Cheng-Han Yu.

• First thing first. Get to know each other. Let me first introduce my self. I was born and grew up in Taipei, Taiwan, my home country, which is a island right next to China. Taipei is a big city in terms of population. It is about the same population size as Chicago. This building is called Taipei 101, which is the tallest building in Taiwan.

My Journey

• Assistant Professor (2020/08 - )

• Postdoctoral Fellow

• PhD in Statistics

• MA in Economics/PhD program in Statistics

After college, working and doing military service for several years, I came to the US for my PhD degree. Originally I would like to study economics, but then I switched my major to statistics.

• I got my master degree in economics from Indiana University Bloomington, then I transferred to UC Santa Cruz to finish my PhD studies.
• Then I spent two years doing my postdoctoral research at Rice University in Houston, Texas.
• Finally, in fall 2020, I came to Marquette as an assistant professor.
• Midwest/Indiana-West/California-South/Texas-Midwest/Wisconsin
• Been to any one of these universities/cities?
• The most beautiful campus.
• Who are international students? I can totally understand how hard studying and living in another country. Feel free to share your stories or difficulties, and I am more than happy to help you if you have any questions.
• Poor listening and speaking skills. I was shy.
• OK so, this is my background. How about you introducing yourself as well. You can share anything, your major, hobbies, your favorite food, what do you want to do after graduation, anything,
• I have the class list. I’d like to learn your face and remember your name. You know, you all wear a mask. It’s hard to recognize you and connect your name and your face.
• When I call your name, you can say something about yourself. No need to be long, couple of seconds are good.

My Research

• Bayesian spatio-temporal modeling and machine learning algorithms in neuroimaging
• Bayesian Deep Learning for image classification
• Efficient MCMC for high dimensional regression
• Game-based learning for STEM and data science education

fMRI

EEG

How to Reach Me

• Office hours TuTh 4:50 - 5:50 PM and Wed 12 - 1 PM in Cudahy Hall 353.
• 📧 cheng-han.yu@marquette.edu
  • Answer your question within 24 hours.
  • Expect a reply on Monday if shoot me a message on weekends.
  • Start your subject line with [mssc6250] followed by a clear description of your question.

• I will NOT reply your e-mail if … Check the email policy in the syllabus!

Prerequisites

• On bulletin: MATH 4720 (Intro to Statistics), MATH 3100 (Linear Algebra) and MSSC 5780 (Regression Analysis)

• Programming experience (Who does machine learning without coding?)

• Having taken MSSC 5700 (Probability) and MSSC 5710 (Stats Inference) or other math and statistics courses (Stats Computing, etc) is recommended.

Textbook

• (ISL) An Introduction to Statistical Learning, by James et al. Publisher: Springer.
• Discuss all chapters except Chapter 11 (survival analysis) and 13 (multiple testing).
• R and Python code

In the Preface,

… for advanced undergraduates or master’s students in Statistics or related quantitative fields,

… concentrate more on the applications of the methods and less on the mathematical details.

Optional References

• (PMLI) Probabilistic Machine Learning: An Introduction, by Kevin Murphy. Publisher: MIT Press.

• Self-contained with lots of mathematics foundations

• Python code

Optional References

• (PMLA) Probabilistic Machine Learning: Advanced Topics, by Kevin Murphy. Publisher: MIT Press.

• PhD level

• Probabilistic or distributional-based

Optional References

• (ESL) The Elements of Statistical Learning, 2nd edition, by Hastie et. al. Publisher: Springer.

• For PhD students or researchers in mathematical sciences

• Frequentist-based

Course Website - https://mssc6250-s24.github.io/website/

Learning Management System (D2L)

• News

• Submit your homework Assessments > Dropbox.

• Check your grade Assessments > Grades.

Grading Policy ✨

• The grade is earned out of 1000 total points distributed as follows:
  • Homework: 500 pts
  • Class activity: 200 pts
  • Final project presentation and/or written report: 300 pts
• ❌ No extra credit projects/homework/exam to compensate for a poor grade.

Grade-Percentage Conversion

• Your final grade is based on your percentage of pts earned out of 1000 pts.
• [x, y) means greater than or equal to x and less than y.

Grade	Percentage
A	[94, 100]
A-	[90, 94)
B+	[87, 90)
B	[83, 87)
B-	[80, 83)
C+	[77, 80)
C	[70, 77)
F	[0, 70)

Homework (500 pts)

• Assessments > Dropbox and upload your homework in PDF format.

• ❌ No make-up homework.

• Due Friday 11:59 PM (Hard deadline and no late submission).

• You have at least one week to finish your homework.

In-Class Activity (200 pts)

• There will be 3 to 4 in-class activities.

• Students will learn from each other by presenting and discussing the assigned topics.

• More details about the in-class activities will be released later.

Final Project (300 pts)

• The final project includes two parts: written report and/or oral presentation?.

• You have to participate (in-person) in the final presentation in order to pass the course.

• More details about the project will be released later.

Which Programming Language?

Use any language you want!

Academic Integrity

This course expects all students to follow University and College statements on academic integrity.

• Honor Pledge and Honor Code: I recognize the importance of personal integrity in all aspects of life and work. I commit myself to truthfulness, honor, and responsibility, by which I earn the respect of others. I support the development of good character, and commit myself to uphold the highest standards of academic integrity as an important aspect of personal integrity. My commitment obliges me to conduct myself according to the Marquette University Honor Code.

Attendance and COVID-19

• It is your responsibility as a Marquette University student to protect the health and safety of our community in this course.
• Visit What to do if you are exposed to COVID-19 or test positive website for university guidelines on the best course of action.
• Visit guidance on Spring 2024 Class attendance, withdrawal, and grading
  • Students are responsible for contacting instructors prior to the missed class session to indicate absence and the need to make up classwork/assignments.
    
  • Students requesting make up classwork/assignments are required to provide the COVID Cheq “stop sign” to confirm inability to attend class.