Syllabus
Macalester College, Spring 2026
- Section 03: M/W/F 02:20-03:20pm, THTR 213
Welcome to STAT 253! This class is an introduction to the exciting world of statistical machine learning. Broadly, statistical machine learning consists of tools and algorithms to learn from data. Insights from machine learning help us take action by enabling us to make predictions and understand uncertainty. Applications of machine learning algorithms are used everywhere from finance to biology, medicine, social sciences, language, and the humanities.
In this course, you will build on the linear and logistic regression modeling techniques covered in STAT 155 to understand tools of regression (Weeks 2–6) and classification (Weeks 7–11) more broadly. You will also learn about unsupervised learning methods (Weeks 12–15) that can help you find underlying structure in data. Along the way, you’ll also improve your skills in computational thinking/programming, communication, collaboration, ethical thinking, and reflection, all of which are vital in any profession.
Learning Objectives
This class is an introduction to the exciting world of statistical machine learning.
The goals of this course are two-fold:
- to gain a working understanding of various machine learning algorithms, and
- to further develop general skills necessary for statistics and data science.
Please see the Learning Goal tab of the “Course Logistics” for more details!
Instructor/Preceptor Team
About your professor
Md Mutasim Billah, PhD
Pronunciation: listen here
Office: Olin-Rice 234
Email: mbillah@macalester.edu
Greetings! You can call me Bill or Professor Billah & I use he/him pronouns. Back when I was an undergrad student, I didn’t have the best experience in Intro Stat—those courses often emphasized formulas over real understanding. That experience has shaped my teaching—I concentrate on illustrating how statistical theories connect and can be applied in the real world. I’m excited to teach STAT 253 and to create a more meaningful experience—one that helps all students feel confident applying it beyond the classroom. My methodological research lies at the intersection of statistical genetics, biostatistics, and genomics. My current research interests include developing novel statistical methods and computationally efficient bioinfor matics tools, leveraging modern machine- and deep-learning approaches analyze high-dimensional next-generation sequencing and multi-omics data to identify genes and regulatory mechanisms underlying complex diseases. Outside of my academic work, I enjoy spending time outdoors with family and friends or cooking variety of foods. If you can’t find me anywhere, I might be busy playing soccer or exploring new worlds on my PS5 Pro!
Office hours:
- Location: My office (OLRI 234) and over Zoom
- Times: M/W: 12:00pm - 12:30pm (in-person), T/TR: 2pm - 3pm (Over zoom, password: 123456)
- By Appointment: I’m also happy to meet one-on-one if my normal drop-in/virtual hours don’t work for you. Shoot me an email and we can arrange it over zoom, password: 123456.
- Email Response Time: I do my best to reply to emails promptly during weekdays. Please note that messages sent after 3:00 pm or on weekends may take longer to receive a response.
About your preceptors
We have four awesome preceptors who will be helping out with STAT 253 this semester. See the calendar on Moodle for up-to-date information about their office hour times and locations.
The role of an MSCS preceptor is to help students with content questions, assist in the navigation of available resources, advise on studying approaches for classes, and assist with concepts, tools, and skills needed for problem sets. Students are accountable for their own learning; as such, preceptors are not allowed to share answers to assignments (unless specifically directed by the instructor), are not expected to immediately know the right approach, or provide assistance outside of office hours. Additional guidelines and expectations on how to interact with preceptors can be found here.
Data and R Support: In addition to our course preceptors, there is support on campus for working with data and R / RStudio. See https://www.macalester.edu/mscs/data-support for more information.
Topics and Tentative Schedule
| Module | Unit | Topics | Weeks |
|---|---|---|---|
| Regression | 1. Model evaluation |
Model assumptions Measuring model strength: RMSE, MAE, CV |
1–6 |
| 2. Model building / selection |
Variable selection Shrinkage & regularization: LASSO |
||
| 3. Flexible models |
Bias-variance trade-off KNN LOESS & splines |
||
| Classification | 4. Model building and evaluation |
Logistic regression Accuracy, sensitivity & specificity, ROC curves |
7–10 |
| 5. Flexible models |
KNN Tree-based methods (decision trees, bagging, forests) |
||
|
Unsupervised Learning |
6. Clustering |
Hierarchical clustering K-means clustering |
11–15 |
| 7. Dimension reduction | Principal component analysis (PCA) |
Course resources
Textbook & Online Course Manual
Our textbook, ISLR, is available free online: An Introduction to Statistical Learning with Applications in R (2nd edition). James et al, 2021.. The ISLR readings are highly encouraged and serve as a nice complement to the videos and in-class activities.
Throughout the course, readings will be assigned from these notes, or other sources.
The online course websitel includes all in-class activities (with solutions).
See the rough schedule of the course, which is subject to change as needed.
Moodle
Moodle includes important announcements, general resources, a broad course calendar, submission links, feedback, and a forum for student questions.
Statistical software
We will use the (completely free and open source) R programming language extensively throughout this course. RStudio (an interface for R) will facilitate our use of R. You may use RStudio in one of two ways:
Desktop version: Download for Windows or Mac at https://posit.co/downloads/. Note: You first need to download and install R on your computer in order to use the desktop version of RStudio
Online: Go to https://rstudio.macalester.edu, and log in with your full Mac email address and your usual Mac password to get access. (Note that this is a shared resource across MSCS, and you may experience performance issues due to high traffic, server outages, etc.)
More detailed instructions on downloading, installing, and getting started with R, and RStudio are available on the R Resources tab.
Office hours (OH) and R Support
OH: Across the instructor and preceptors, there are several office hours each week. Names, times, and locations are on the Moodle course calendar. IMPORTANT: Always check the calendar before attending OH.
Data & R Support: In addition to the course preceptors, there is support on campus for working with data and R / RStudio. This is a great resource for R setup and troubleshooting throughout the semester. See https://www.macalester.edu/mscs/data-support for more information.
Course Structure
Before Class
In order to dedicate our class time to hands-on learning, you will prepare for class by watching short videos, reading from our textbook, and completing short quizzes (checkpoints) to assess your initial understanding of concepts. You can reattempt each checkpoint question multiple times, with a small penalty for incorrect responses.
During Class
During class time, you will engage with each other in exercises and discussions that build upon the pre-class work. Please bring your laptop to class every day. Consistent attendance and active participation in these activities is expected of all students and, most importantly, will be crucial for your learning!
After Class
After class, you will be expected to finish any remaining exercises from the class activity and review/organize your notes. For each unit, you will also complete homework assignments designed to help you practice and synthesize material and provide an opportunity to receive feedback to further guide your learning.
Major Learning Activities & Assessments
Group Assignments
hese assignments will give you an opportunity to practice collaboration, communication, and application of core statistical machine learning concepts in a more open-ended setting. They will also provide an opportunity to review and synthesize key concepts prior to each quiz. We will reserve class time at the end of each module—Regression (Units 1–3), Classification (Units 4–5), and Unsupervised Learning (Units 6–7)—to work on these assignments. Deadlines are posted in the Stat 253 Google Calendar on Moodle.
Quizzes
The primary assessment of your understanding of core statistical machine learning concepts and R code will come in the form of three in-class quizzes:
- Quiz 1: TBD
- Quiz 2: TBD
- Quiz 3: Thurs. 5/7, 1:30pm-3:30pm in class
- All quizzes will have the following format:
- Taken individually, using pen/pencil & paper
- Closed notes, but you may use a 3x5 index card with writing on both sides. These can be handwritten or typed, but you may not include screenshots or share note cards. Making your own card is important to the review process- as you are required to submit the index card along with the answer paper.
- Quiz corrections:
After Quizzes 1 and 2, you will have the opportunity modify your quiz grade by reviewing feedback, completing a short reflection, and revising your answers. Note: Quiz 3 corrections are not allowed due to time constraints at the end of the semester (I’ll take this into consideration when grading!)
Learning Reflections
Throughout the semester, I want you to practice reflecting on your progress and learning. I will provide a structured set of reflection questions for you to complete roughly once a month. More details (prompts, deadlines, grading, etc.) will be provided in class!
Course Policies
Flexibility
I provide transparent accommodations to all students. It helps reduce stress and the “hidden curriculum” (not everybody feels comfortable asking for flexibility).
- Missed Class: You are warmly invited, encouraged, and expected to attend and participate in all class meetings. Participation means coming to class prepared, actively discussing material, engaging in the activities, and asking questions. This will be important not only for your own learning, but also for our ability to build a community and maintain a sense of connection and commitment to one another during the semester. That being said, it’s okay to miss class in the case of an emergency.
📧 Send me a quick email. You do not need to share a reason for your absence, especially if it’s personal. It’s just a simple courtesy & keeps communication lines open.
📅 Check the Course Schedule in the online manual for what is happening in class that day.
📝 Complete the in-class activity on your own & check the solutions posted in the online manual.
💬 Ask any follow-up questions on the Moodle forum or in office hours (OH).
Homework: There will be a one hour grace period implemented for each assignment. If you need more time than that, email me to request an extension. Each student will automatically (no reason necessary!) be granted the use of three 3-day homework extensions. Except in rare circumstances, I will not grant more than three (or longer than 3-day) extensions.
Checkpoints: Except in exceptional circumstances, I will NOT grant extensions or accept late submissions for checkpoints. These are important preparation for class.
Major learning activities: I cannot guarantee that I will be able to accommodate late work or extensions for group assignments, quizzes, or reflections, but I will do my best to work with you in exceptional circumstances. Send extension requests for any of these major assignments at least one week in advance for full consideration.
Artificial Intelligence (AI)
Using AI tools is an emerging skill. You may use AI (ChatGPT, Gemini, Grok, etc), with some caveats & limitations:
AI is often wrong, thus is not a good resource on topics for which you don’t yet have expertise. Relatedly, though AI can be helpful with parts of a statistical analysis (eg: getting unstuck on code, checking grammar), you have to guide that process (eg: what questions are we trying to answer? what’s a reasonable approach?).
Work on an exercise for at least 30 minutes before even thinking about AI. You will learn very little if you overly rely on AI, hence be unprepared for other interactions with the material (eg: in-class discussions, quizzes, future courses that build upon 253, etc). Learning comes from you doing the puzzling, not from you producing a correct answer.
Whether or not you use AI, you must be able to defend/explain any code/discussion you hand in. You cannot simply use AI to bypass your own learning.
You may not use AI to generate entire arguments or discussions. Putting code and discussions into your own words is critical for your own deeper learning, independent thinking, and creativity. (For example, imagine how little you’d learn in a language course if you simply used AI to translate all text for you!!)
Any use of AI must be cited, just like any other resource.
Community & Academic Integrity
MSCS strives to provide a learning environment that is equitable, inclusive, welcoming, mutually respectful, and free of discrimination. You’re expected to follow the MSCS Community Guidelines. You’re also required to be familiar with & follow the college’s academic integrity & other academic policies. In addition to the examples listed there, academic violations in this course include but are not limited to the following:
- Using any materials from any past STAT 253 course, at Mac or elsewhere. Relatedly, you should not provide any materials to any future 253 students.
- Gaining access to, using, or distributing solution sets.
- Passing off others’ work as your own. You must be able to defend / explain all work you hand in.
- Using AI without citation, to generate entire discussions / code blocks, or without being able to defend the results. Policy violations will result in a score of 0 on the work & be reported to the Asst. Dean of Academic Programs & Advising.
Calculating Final Grades
Grading system
This course uses a grading system designed to combat some of the (many!) problems with “traditional” grades link. My hope is that this alternative grading system will provide space to make and learn from mistakes, engage in feedback loops link, and encourage self-reflection. You will receive qualitative feedback and marks (e.g., PASS, ATTEMPT), rather than points, on most assignments. I will then translate that feedback into an overall letter grade according to the table to the right. Adjusting to this system may take time. I’ll provide feedback and resources to help you track your progress throughout the semester. If you are ever concerned about your learning (or grade), please set up an appointment with me to discuss!
|
Passing (C) |
Progressing (B) |
Exemplary (A) |
|
|---|---|---|---|
| Checkpoints | ATTEMPT ≥ 10 | PASS ≥ 12 | |
| Homework | ATTEMPT ≥ 5 | PASS ≥ 7 | |
|
Group Assignments |
ATTEMPT ≥ 2 |
ATTEMPT ≥ 3 and PASS ≥ 2 |
PASS ≥ 3 |
| Reflections | ATTEMPT ≥ 2 |
ATTEMPT ≥ 3 and PASS ≥ 2 |
PASS ≥ 3 |
|
Quizzes (after revision) |
≥ 70% | ≥ 80% | ≥ 90% |
If you meet all criteria in a particular column, you will earn that grade. Intermediate grades (e.g., B+, A-) will be given if most requirements in a given column are achieved but some requirements for lower (-) and/or higher (+) grades are met.
Communication & Thriving
Asking questions/communicating
Office Hours
OH are a great place to chat about the course, career planning, life,… Please visit us!!
- OH times & locations are on the Moodle course calendar.
- OH are oriented around group discussion. They are not first come, first served appointments.
- Since it’s not an effective way to deepen your learning, OH are not a place to sit and do assignments with me or preceptors. It’s an opportunity to discuss concepts & specific questions.
Moodle Forum: STAT 253 Discussion Board
This forum is where we’ll communicate outside class. Students can post and answer comments / questions there. This is an informal way to converse, ask questions, share info, & connect. Do not rely on receiving responses outside weekdays between 9am & 5pm.
What to do when you have a question for me?
- If it’s non-private (e.g. about policies, homework (Practice Problems), class activities, etc), you must post it on STAT 253 Discussion Board in Moodle. Remember- collaboration is the KEY!
- If it’s personal (e.g. about an absence), email me.
- It’s good, professional practice to check whether your question is already answered in the provided resources. For example:
- Info (what to do if you miss class): syllabus
- Due dates: course calendar at the top of Moodle + course schedule in the online manual
- Quiz dates: syllabus + course calendar at the top of Moodle + course schedule in the online manual
- Homework policies & grading: homework policies & grading doc
- Finals week: syllabus + course calendar at the top of Moodle + course schedule in the online manual
Thriving in STAT 253
You should plan to spend ~10-12 hours on any 4-credit course, including class time.1 Stay up-to-date on the course calendar and carve out time for studying & doing homework.
At minimum, thriving in this course requires the completion of some concrete tasks. Complete all assignments, regularly attend & engage in class, complete in-class activities (which might mean completing work outside of class), and check the activity solutions.
If your main focus is on checking off some boxes, you won’t get much out of this course (or college in general). Deeper, enduring learning requires more. Carve out time to rewrite, reflect upon, & review your notes. Summarize concepts in your own words.
Actively participate in the class & take ownership of your learning. PLEASE: Don’t be afraid to ask for help, make mistakes, and ask questions! These skills are critical to your well-being & learning. Finally, have some fun, be curious, and reflect upon what surprises you about the material and yourself
Advice for Success in STAT 253
Attend class.
We’ll use class time to ask and answer questions, review material, and practice concepts in a collaborative environment. To ensure the best learning experience for you and your classmates, come prepared, engage in class, and make full use of the entire class period.
check the course website to see what you missed, review that material and complete the activity on your own, get notes from your classmates, and then (after doing all of the above) come to office hours with any remaining specific questions.
Ask questions.
When you have questions, please stop me during class, ask your neighbor, post on Moodle discussion board, and come to office hours. Saying “I don’t understand” is an important part of learning and it helps others. Office hours are a great time to talk about course material and assignments, study strategies, selecting courses, declaring a major, grad school and/or career planning, or life. You don’t need to have a specific question in order to attend office hours: it can also be a great space to review concepts, talk through examples, or just chat!
Make time.
Performing a thoughtful statistical analysis requires time: to plan, to implement, to interpret, and to revise. Start your assignments early. It is very hard to be creative or to debug R code when you are in a rush. You should expect to spend about 10 hours per week on this class (including the 3 hours we spend together during class). If you’re spending much more (or less!) time than that, please let me know.
Prioritize your well-being.
Taking care of yourself will help you engage more fully in your academic experience. Beyond being a student, you are a human being carrying your own experiences, thoughts, emotions, and identities with you. If you are having difficulties maintaining your well-being, I encourage you to contact me and/or check out these resources.
As part of prioritizing your well-being (and that of others around you), it is important that you stay home if you are feeling sick. This is particularly meaningful to me as someone with a severely immunocompromised family member. I will happily work with you to get you caught up!
Communicate.
I will do my best to clearly and promptly communicate any changes to expectations, deadlines, office hours, or class meetings. Please make sure to check your email (and Moodle announcement) so you don’t miss any important announcements. I know that you may also have issues come up: if so, please get in touch with me to discuss solutions.
Other policies
Religious Observance
Students may wish to take part in religious observances that occur during the semester. If you have a religious observance/practice that conflicts with your participation in the course, please contact me before the end of the second week of the semester to discuss appropriate accommodations.
In an effort to respect religious diversity, I request that students who plan to observe a religious holiday during scheduled class meetings/class requirements talk to me about reasonable consideration by the end of the second week of the course.
Well-being
I want you to succeed. Both here at Macalester and beyond. To help make this happen, I am committed to the following.
Respect: Everyone comes from a different path through life, and it is our moral duty as human beings to listen to each other without judgment and to respect one another. I have no tolerance for discrimination of any kind, in and out of the classroom. If you are seeking campus resources regarding discrimination, the Department of Multicultural Life and the Center for Religious and Spiritual Life are wonderful resources. We will also respect the MSCS Community Guidelines.
Sensitive Topics: Applications in this course span issues in science, policy, and society. As such, we may sometimes address sensitive topics. I will try to announce in class if an assignment or activity involves a potentially sensitive topic. If you have reservations about a particular topic, please come talk to me to discuss possible options.
Accommodations: If you need accommodations for any reason, please contact Disability Services to discuss your needs, and speak with me as soon as possible afterwards so that we can discuss your accommodation plan. If you already have official accommodations, please discuss these with me within the first week of class so that you get off to a great start. Contact me if you have other special circumstances. I will find resources for you.
Title IX: You deserve a community free from discrimination, sexual harassment, hostility, sexual assault, domestic violence, dating violence, and stalking. If you or anyone you know has experienced harassment or discrimination, know that you are not alone. Macalester provides staff and resources to help you find support. Please be aware that as a faculty member, it is my responsibility to report disclosure about sexual harassment, sexual misconduct, relationship violence, and stalking to the Title IX Office. The purpose of this report is to ensure that anyone experiencing harm receives the resources and support they need. I will keep this information private, and it will not be shared beyond this required report.
You may also contact Macalester’s Title IX Coordinator directly (phone: 651-696-6258; e-mail: titleixcordinator@macalester.edu); they will provide you with supportive measures, resources, and referrals. Additional information about how to file a report (including anonymously) is available on the Title IX website.
General Health and Well-being: I care that you prioritize your well-being in this semester and beyond. Investing time into taking care of yourself will have profound impacts on all aspects of your life. Remember that beyond being a student, you are a human being carrying your own experiences, thoughts, emotions, and identities. It is important to acknowledge any stressors you may be facing, which can be mental, emotional, physical, cultural, financial, etc., and how they can have an impact on you. I encourage you to remember that you have a body with needs. In the classroom, eat when you are hungry, drink water, use the restroom, and step out if you are upset and need some air. Please do what is necessary so long as it does not impede your or others’ ability to be mentally and emotionally present in the course. Outside of the classroom, sleeping well, moving your body, and connecting with others can be strategies can help nourish you. If you are having difficulties maintaining your well-being, please don’t hesitate to contact me and/or find support from physical and mental health resources here, here, and here.
Want ‘A’ in this course? Read, Read, Read…
When you have questions, please stop me during class, ask your neighbor, email me and come to office hours. Saying “I don’t understand” is an important part of learning and it helps others around you.
Office hours are a great time to talk about course material, study strategies, selecting courses, declaring a major, grad school and/or career planning, or life in general. You don’t need to have a specific question in order to attend: it can also be a space to review concepts, talk through examples, or just chat!
Performing a thoughtful statistical analysis requires time: to plan, to implement, to interpret, and to revise.
- Start your assignments early. It is very hard to be creative or to debug R code when you are in a rush.
- In addition to the 3 hours we spend together during class, expect to spend about 10-12 hours per week on this class.
- Don’t miss any Checkpoint quizzes—they are designed to give you a core understanding of the subject matter (due at 02:00 pm, if assigned)!
- If you’re spending much more (or less!) time than that, please let me know.
Active participation in this class will be key to your learning. We’ll use class time to ask and answer questions, review material, and practice concepts in a collaborative environment.
To ensure the best learning experience for you and your classmates:
- Come prepared
- Engage in class
- Make full use of the entire class period
If you can’t attend:
- Check Moodle and Course Website to see what you missed
- Review the material
- Complete the in-class activity on your own
- Get notes from classmates
- (After doing all of the above) come to office hours with specific questions.
Investing time into taking care of yourself will help you engage more fully in your academic experience. Remember: beyond being a student, you are a human being carrying your own experiences, thoughts, emotions, and identities with you. If you are having difficulties maintaining your well-being, please contact me and/or check out available campus resources (mentioned in the ‘Other Policies’ part of the syllabus. As part of prioritizing your well-being (and others’), please stay home if you are feeling sick. Follow the recommendations above (Attend Class) and below (Communicate) if you miss class or need extra time on an assignment.
I will do my best to clearly communicate changes to expectations, deadlines, office hours, or class meetings due to instructor illness or unexpected life issues.
Please check Moodle regularly so you don’t miss announcements.
I also ask that you check in with me as soon as possible if:
- You need to miss multiple classes in a row
- You have a conflict (e.g., athletic competition, religious observance) with a scheduled quiz
- You need accommodation(s)
- You are worried about meeting a deadline
- Something about the class is not working for you
- “Take notes on the assigned readings/videos! It’s super helpful for exam review”
- “The course website is very very useful to find notes and example code.”
- “Try to pay attention to what the code is actually doing instead of just copy/pasting (although copy/pasting is useful!), especially the functions we use most frequently, because it will make understanding and adapting code much easier. Also, it can be helpful to step back and make sure you have a conceptual understanding of what you’re trying to do before going straight to coding.”
- “Review the class notes!!!”
- “Have fun!”
Note
This syllabus is subject to change at any time! Announcements of changes will be made in class or via email.