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 and Applied Mathematics

• 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 computation in neuroimaging/epidemiology
• Bayesian deep learning for image classification
• Efficient MCMC for high dimensional regression
• 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/or 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

• (MML) Mathematics for Machine Learning, by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong. Publisher: Cambridge University Press.

• College level mathematics for machine learning

• Mathematical concepts behind models and algorithms

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-s25.github.io/website/

Learning Management System (D2L)

• 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
  • Midterm mini-project presentations: 300 pts
  • Final project: 200 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.

Mini-Project Presentation (300 pts)

• There will be 2 in-class mini-project presentations (150 pts each).

• Learn from each other by presenting and discussing the assigned topics.

• More details about the activity will be released later.

Final Project (200 pts)

• The final project is submitted as a paper (and some relevant work?)

• The project submission deadline is Thursday, 5/8, 10 AM.

• More details about the project will be released later.

Which Programming Language?

Use any language you prefer!

Generative AI and Sharing/Reusing Code Policy

• You are responsible for the content of all work submitted for this course.

• You may use generative AI tools such as ChatGPT or DALL-E to generate a first draft of text for your assignments, provided that this use is appropriately documented and cited.

• Learn how to cite the use of AI in MLA and APA format, and more

Sharing/Reusing Code

• Unless explicitly stated otherwise, you may make use of any online resources, but you must explicitly cite where you obtained any code you directly use or use as inspiration in your solutions.

• Any recycled code that is discovered and is not explicitly cited will be treated as plagiarism, regardless of source.