Schedule

Teachers

Name	Office Hours	Email (please ask most questions in person)
Gillett, John (Lecturer)	Tu 3:00-3:50, Th 2:25-3:15 in 1221 Medical Sciences Center	jgillett@wisc.edu
Yee, Ryan (TA for lecture 001/002)	MoWeTh 1:15-2:05 in 1210 Medical Sciences Center	ryee2@wisc.edu
Zhao, Jitian (TA for lecture 003/004)	Mo 12:25-1:15, TuFr 1:00-1:50 in 1130A Medical Sciences Center	jzhao326@wisc.edu
	Or, sorted by time:
	Mo 12:25-1:15 Jitian 1130A
	Mo 1:15-2:05 Ryan 1210
	Tu 1:00-1:50 Jitian 1130A
	Tu 3:00-3:50 John 1221
	We 1:15-2:05 Ryan 1210
	Th 1:15-2:05 Ryan 1210
	Th 2:20-3:10 John 1221
	Fr 1:00-1:50 Jitian 1130A

Class Times

LEC 451-001, -002: TuTh 8:00-9:15 in 104 Van Hise

LEC 451-003, -004: TuTh 11:00-12:15 in 2241 Chamberlin

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 Lab 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
Classroom instruction

• 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 orange 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 classroom lectures guided by the notes and code examples.
  • 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 is an in-class midterm exam and an in-class final exam.
• There is a semeseter project to be completed in groups of 5 students each.
• There are two ways to get help:
  • Ask questions in class.
  • Ask questions in teacher and TA office hours.
• For each day in the schedule, attend lecture and complete the things in the "Homework Due" column before 11:59 p.m. (I recommend starting early so you can receive help.)

Regular and Substantive Student-Instructor Interaction
The regular and substantive student-instructor interaction requirement is met through in-person lectures, regular weekly office hours, 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 by Sebastian Raschka, Yuxi Liu, and Vahid Mirjalili)

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 trouble with Anaconda, an alternative is Google Colab (FAQ).

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

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 a midterm exam October 29 in class
  
•   75 for a final exam 7:45-9:45 a.m. December 17
  
•   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)
  • 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 (25 points) in class (for in-person courses) or via video (for online courses)
  • Watch presentations (8 points spread over two or three days) and give peer feedback
  • Report (10 points) of no more than 600 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 HW01-HW05 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 1% 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.

Privacy of Student Records & the Use of Audio Recorded Lectures Statement
Lecture materials and recordings for this course are protected intellectual property at UW-Madison. Students in this course may use the materials and recordings for their personal use related to participation in this class. Students may also take notes solely for their personal use. If a lecture is not already recorded, you are not authorized to record my lectures without my permission unless you are considered by the university to be a qualified student with a disability requiring accommodation. [Regent Policy Document 4-1] Students may not copy or have lecture materials and recordings outside of class, including posting on internet sites or selling to commercial entities. Students are also prohibited from providing or selling their personal notes to anyone else or being paid for taking notes by any person or commercial firm without the instructor's express written permission. Unauthorized use of these copyrighted lecture materials and recordings constitutes copyright infringement and may be addressed under the university's policies, UWS Chapters 14 and 17, governing student academic and non-academic misconduct. View more information about FERPA here: https://registrar.wisc.edu/ferpa-facstaff/

Teaching & Learning Data Transparency Statement
The privacy and security of faculty, staff and students' personal information is a top priority for UW-Madison. The university carefully evaluates and vets all campus-supported digital tools used to support teaching and learning, to help suppot success through learning analytics (https://teachlearn.provost.wisc.edu/learning-analytics/), and to enable proctoring capabilities. View the university's full teaching and learning data transparency statement here: https://teachlearn.provost.wisc.edu/teaching-and-learning-data-transparency-statement/.

Course Evaluations
Students will be provided with an opportunity to evaluate their enrolled courses and their learning experience. Most instructors use 'HelioCampus Assessment and Credentialling (formerly AEFIS)', a digital course evaluation survey tool. In most instances, students receive an official email two weeks prior to the end of the semester, notifying them that anonymous course evaluations are available. Student participation is an integral component of course development, and confidential feedback is important. UW-Madison strongly encourages student participation in course evaluations.

Students' Rules, Rights, & Responsibilities
See: https://guide.wisc.edu/undergraduate/#rulesrightsandresponsibilitiestext

Diversity & Inclusion Statement
Diversity is a source of strength, creativity, and innovation for UW-Madison. We value the contributions of each person and respect the profound ways their identity, culture, background, experience, status, abilities, and opinion enrich the university community. We commit ourselves to the pursuit of excellence in teaching, research, outreach, and diversity as inextricably linked goals. The University of Wisconsin-Madison fulfills its public mission by creating a welcoming and inclusive community for people from every background – people who as students, faculty, and staff serve Wisconsin and the world.

Mental Health and Well-Being Statement
Students often experience stressors that can impact both their academic experience and personal well-being. These may include mental health concerns, substance misuse, sexual or relationship violence, family circumstances, campus climate, financial matters, among others. Students are encouraged to learn about and utilize UW-Madison's mental health services and/or other resources as needed. Visit uhs.wisc.edu or call University Health Services at (608) 265-5600 to learn more.

Statement on the Use of ChatGPT and other AI Language Models
While the Statistics Department recognizes the potential benefits of AI language models, their use in academic work can be problematic. In this course, two rules regarding the use of ChatGPT and other AI language models will be enforced: (1) Passing off AI-generated responses as original student work constitutes plagiarism and is strictly prohibited. Any students found to be engaging in this practice will be cited for academic misconduct. (2) Unless explicitly authorized by the instructor to do so, any form of attribution or citation to AI-generated responses as sources is prohibited.

Academic Integrity
By virtue of enrollment, each student agrees to uphold the high academic standards of the University of WisconsinMadison; academic misconduct is behavior that negatively impacts the integrity of the institution. Cheating, fabrication, plagiarism, unauthorized collaboration, and helping others commit these previously listed acts are examples of misconduct which may result in disciplinary action. Examples of disciplinary sanctions include, but are not limited to, failure on the assignment/course, written reprimand, disciplinary probation, suspension, or expulsion.

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 standards of teaching, data, and research. They expect their students to uphold the same standards of ethical conduct. 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 a manner 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 the integrity or reliability of the analysis. Obviously, never modify or falsify data.
• Protect the privacy and confidentiality of research subjects and data concerning them, whether obtained from the subjects directly, other persons, or existing records.

Netiquette on Piazza and Online Communication
See https://kb.wisc.edu/50548 for a general netiquette. Specifically:

• Any comment or answers must be on topic, concise, polite, and respectful of others.
• Assume the best intentions of others in the class and be forgiving when you think that the tone of someone's post is offensive. It is easy to misread the tone of someone's written communication. If in doubt, ask an open, honest question about what the person meant so that you can clarify before making assumptions that damage your perception of your colleague.
• Students must not post answers to homework problems.

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 Cecile Ane (cecile.ane@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 and Diversity Committee, Professor Karl Rohe (karl.rohe@wisc.edu). You may also use the University's bias incident reporting system, which you can reach at: https://doso.students.wisc.edu/report-an-issue/bias-or-hate-reporting/.

Overlapping Course Time Statement
The Department of Statistics strongly discourages students from enrolling in any courses whose regular class meeting dates and times overlap with each other. This policy is in alignment with the College of Letters and Sciences Course Attendance Policy. It is also consistent with the Class Attendance Policy for Students at UW-Madison (https://kb.wisc.edu/ls/24628), whose first sentence reads, "It is expected that every student will be present at all classes." Statistics instructors may opt not to make any alternative arrangements in the event any conflict arises due to a student taking a course with class meetings that overlap with a Statistics course, including a conflict between two Statistics courses. Note that final exams occasionally are scheduled simultaneously for courses which meet at different times; in this situation, please contact your instructor well before the exam date about potential accommodations.

Accommodations for Students with Disabilities
The University of Wisconsin-Madison supports the right of all enrolled students to a full and equal educational opportunity. The Americans with Disabilities Act (ADA), Wisconsin State Statute (36.12), and UW-Madison policy (UW-855) require the university to provide reasonable accommodations to students with disabilities to access and participate in its academic programs and educational services. Faculty and students share responsibility in the accommodation process. Students are expected to inform me of their need for instructional accommodations during the beginning of the semester, or as soon as possible after being approved for accommodations. I will work either directly with you or in coordination with the McBurney Center to provide reasonable instructional and course-related accommodations. Disability information, including instructional accommodations as part of a student's educational record, is confidential and protected under FERPA. (See: https://mcburney.wisc.edu/).

Academic Calendar & Religious Observances
See https://secfac.wisc.edu/academic-calendar/.
Establishment of the academic calendar for the University of Wisconsin-Madison falls within the authority of the faculty as set forth in Faculty Policies and Procedures (https://policy.wisc.edu/library/UW-801#Pol801_ 1_20). Construction of the academic calendar is subject to various rules and laws prescribed by the Board of Regents, the Faculty Senate, State of Wisconsin and the federal government. For additional dates and deadlines for students, see the Office of the Registrar's pages (https://registrar.wisc.edu/dates/). Students are responsible for notifying instructors within the first two weeks of classes about any need for flexibility due to religious observances (https://policy.wisc.edu/library/UW-880).

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.