Schedule

Teachers

Name	Zoom Q&A (Office Hours)	Email (please ask most questions via Zoom Q&A or piazza)
Gillett, John (Lecturer)	8:30-9:00 a.m. MoWe	jgillett@wisc.edu
Zhao, Zhihao (TA)	8:30-9:00 p.m.: - before 6/30/25: Th - after 6/30/25: WeTh	zzhao357@wisc.edu

Class Times
This online course meets during session DHH (6/17-8/11/25) from June 16 to August 7, 2025.
Live online Q&A help is available from the teacher and TA via Zoom at the times listed above. To attend a Zoom web conference, visit Canvas's Zoom tab.

Course Description
Introduction to machine learning for pattern classification, regression analysis, clustering, and dimensionality reduction. For each category, fundamental algorithms, as well as selections of contemporary, current state-of-the-art algorithms, are being discussed. The evaluation of machine learning models using statistical methods is a particular focus of this course. Statistical pattern classification approaches, including maximum likelihood estimation and Bayesian decision theory, are compared and contrasted to algorithmic and nonparametric approaches. While fundamental mathematical concepts underlying machine learning and pattern classification algorithms are being taught, the practical use of machine learning algorithms using open source libraries from the Python programming ecosystem will be of equal focus in this course.

Learning Outcomes
After completing this course, a successful student will:

• Understand machine learning concepts:
  • Supervised learning:
    • Support vector machine
    • Linear regression
    • Logistic regression
    • Kernel regression
    • Decision tree classification and regression
    • k-NN classification and regression
  • Unsupervised learning:
    • Density estimation
    • Clustering
    • Dimensionality reduction
    • Outlier detection
  • Ensemble learning:
    • Boosting
    • Bagging
    • Gradient boosting
  • Basic practices:
    • Optimizing a cost function with gradient descent
    • Feature engineering
    • Splitting data into training, validataion, and test sets
    • Underfitting, overfitting, bias, variance, regularization
    • Hyperparameter tuning
    • Cross-validation
    • Algorithmic efficiency and model selection
    • Assessing model performance
• Use Python and its pandas, NumPy, Matplotlib, and SciKit-Learn libraries to apply machine learning to real data:
  • Use Jupyter Notebook to write reports integrating text, data, code, and its output.
  • Manipulate data to create lists, arrays, and data frames.
  • Produce graphics.
  • Write functions, conditional expressions, and loops.
  • Extract data from text via regular expressions

Requisites
MATH 320, 321, 340, 341, graduate/professional standing, or member of the Statistics Visiting International Scholars program

Designations and Attributes
Level: Advanced
Breadth: Natural Science
L&S Credit Type: Counts as Liberal Arts and Science credit (L&S)

Credit Information
This course is 3-credits. The class meets for two 75-minute in-person lectures each week and carries the expectation that students will work on course learning activities (readings, quizzes, homeworks, studying, etc.) for about 3 hours out of the classroom for every lecture period.

Instructional Mode
Online:

• See this schedule.
• There are nine machine learning topics in the schedule labeled "01 Introduction" through "09 Unsupervised Learning". There are also eight Python topics labeled "01 Python as a Calculator" through "08 Conditional Expressions". (The Python topics are colored green to distinguish them.) For each topic, there are links to these types of course materials:
  • There are lecture notes that also serve as reference sheets consisting of ".pdf" files.
  • There are ".ipynb" (Jupyter Notebook) code example files, along with their compiled ".html" versions.
  • There are videos of lectures that discuss the notes and code examples. (These were made by lecture-capture this past spring. Note that I corrected a few typos and clarified a few paragraphs in the ".pdf" and ".ipynb" files after lectures. Please ask questions if this causes any confusion.)
  • For each Python topic, there is an online quiz to provide low-stakes practice. You may repeat it many times. Your best score before its deadline counts. Each quiz includes 5 to 18 questions. Most questions can be answered by writing a single line of Python code.
• There are five homework assignments, each requiring up to about 100 lines of code.
• There are two online exams.
• There is a semeseter project to be completed in groups of 4-5 students.
• There are two ways to get help:
  • Ask questions (or just listen in) during Zoom Q&A.
  • Ask a qustion via our asynchronous piazza Q&A site. Feel free to answer other students' questions.
• For each day in the schedule, watch the day's video and complete the things in the "Homework Due" column before 11:59 p.m. (I recommend starting early so you can receive help via Q&A or piazza.)

Regular and Substantive Student-Instructor Interaction
The regular and substantive student-instructor interaction requirement is met through online lectures, daily Zoom Q&A with teacher and TA, piazza Q&A, and homework and exam feedback from teacher and TA.

Online materials
Course materials including online quizzes are posted in the schedule (linked above). Canvas is used to collect homework and as a gradebook.

Textbook

There is no required textbook.

Optional Reference Books and Videos

The Hundred-Page Machine Learning Book by Andriy Burkov

Machine Learning with PyTorch and Scikit-Learn (paper; eBook only (ask for 20% discount code in class)) by Sebastian Raschka, Yuxi Liu, and Vahid Mirjalili (notice our TA on its authors pages)

Python Machine Learning 3e by Sebastian Raschka and Vahid Mirjalili

Introduction to Machine Learning videos and notes by Sebastian Rashka

Think Python 2e by Allen Downey

Python for Beginners videos by Microsoft

Computing
A laptop with the free program Anaconda installed is required in class.

In case of computer trouble, UW's InfoLabs offer loaner laptops and desktop computer labs that have Anaconda on their Windows machines.

Or you could use Google Colab, which runs in your browser, and for which you have an account through the UW.

Grades

Grades are based on 400 points allocated to these tasks:

•   75 points for 9 quizzes.
  • The first, Q00, asks about your background.
  • The other eight, Q01-Q08, are designed to help you learn the basics of Python so that you can use it for machine learning. You may repeat quizzes. Your best score before the deadline counts.
• 100 for five machine learning software homework exercises, HW01-HW05. Your homework solutions should be submitted via file upload to Canvas.
• 100 for an online Exam1 Tu July 15
•   75 for an online Exam2 Th July 31
•   50 for a group machine learning project including a presentation:
  • Form a group (0 points) of 4-5 students (or be randomly assigned to a group) and choose a data set
  • Proposal (4 points) including code to read data, descriptions of the variables and machine learning methods you will use
  • Meet with teacher and TA to discuss proposal (3 points)
  • Presentation slides (5 points)
  • Presentation (20 points) in class (for in-person courses) or via video (for online courses)
  • Watch presentations (8 points, 4 per day for two days) and give peer feedback
  • Report (10 points) of no more than 750 words with supporting graphics

We will assign grades according to the percentage scale, A = [92,100], AB = [88,92), B = [82,88), BC = [78,82), C = [70,78), D = [60,70), F = [0,60) (92% of points ⇒ A); and according to the percentile scale A = 60, AB = 40, B = 20, BC = 8, C = 4, D = 2, F = 0 (which yields 40% A grades, 20% AB, 20% B, 12% BC, 4% C, 2% D, 2% F). Your grade will be the higher of these two grades.

If you anticipate religious or other conflicts with course requirements, or if you require accommodation due to disability, you must notify me during the first three weeks of class. You may not make up missed quizzes, homework, or exams, except in the rare case of a documented, serious problem beyond your control. Regarding late work:

• The quizzes are auto-graded by my web-form program. I will copy their scores to Canvas one day after their deadline; that is, you get one free late day for each quiz. (I recommend solving the quizzes on time, as if you do not, you are falling behind.)
• I have set Canvas to allow late submission of HW1-HW5 with a 1% per hour penalty. For example:
  • 1 second to 1 hour late gets a 1% penalty.
  • 12 hours late gets a 12% penalty.
  • 50 hours late gets a 50% penalty.
  (Note that this 2% per hour penalty will not excuse late project components, as late project work would delay presentations and peer feedback.)
• The exam and project deadlines are real, with no late submission permitted.

How to Succeed in This Course
The successful student will attend lectures; submit quizzes, homeworks, and project components on time; attend exams well-prepared; and ask questions when things are unclear.

Standards of Ethical Conduct in Data Analysis and Data Privacy
The members of the faculty of the Department of Statistics at UW-Madison uphold the highest ethical stan-dards of teaching, data, and research. They expect their students to uphold the same standards of ethicalconduct. Standards of ethical conduct in data analysis and data privacy are detailed on the ASA website(https://www.amstat.org/ASA/Your-Career/Ethical-Guidelines-for-Statistical-Practice.aspx), and include:

• Use methodology and data that are relevant and appropriate; without favoritism or prejudice; and in amanner intended to produce valid, interpretable, and reproducible results.
• Be candid about any known or suspected limitations, defects, or biases in the data that may affect theintegrity or reliability of the analysis. Obviously, never modify or falsify data.
• Protect the privacy and confidentiality of research subjects and data concerning them, whether obtainedfrom the subjects directly, other persons, or existing records.

Complaints
If you have a complaint about a TA or course instructor, you should feel free to discuss the matter directly with the TA or instructor. If the complaint is about the TA and you do not feel comfortable discussing it with him or her, you should discuss it with the course instructor. Complaints about mistakes in grading should be resolved with the instructor in the great majority of cases. If the complaint is about the instructor (other than ordinary grading questions) and you do not feel comfortable discussing it with him or her, contact the Director of Undergraduate Studies, Professor Wei-Yin Loh, loh@stat.wisc.edu, or the Director of Graduate Studies, Professor Bret Larget, bret.larget@wisc.edu. If your complaint concerns sexual harassment, please see campus resources listed at https://compliance.wisc.edu/titleix/resources/. In particular, there are a number of options to speak to someone confidentially. If you have concerns about climate or bias in this class, or if you wish to report an incident of bias or hate that has occurred in class, you may contact the Chair of the Statistics Department Climate & Diversity Committee, Professor Karl Rohe (karlrohe@stat.wisc.edu). You may also use the University's bias incident reporting system, which you can reach at https://doso.students.wisc.edu/services/bias-reporting-process/.

COVID-19
Information on COVID-19 is constantly changing. Students should be attentive to University communications regarding COVID-19 that may alter instruction and supersede parts of this syllabus.

Academic Policies and Statements

• Teaching and Learning Data Transparency Statement
• Privacy of Student Records and the Use of Audio Recorded Lectures Statement
• Campus Resources for Academic Success
• Course Evaluations and Digital Course Evaluations
• Students' Rules, Rights and Responsibilities
• Diversity and Inclusion Statement
• Academic Integrity Statement
• Accommodations for Students with Disabilities
• Academic Calendar and Religious Observances