đź“– Syllabus
Table of contents
- Overview
- Getting Started
- Communication
- Course Components
- Exams
- Grades
- Student Support and Well-Being
- Acknowledgements
- Disclaimer
Note about Accessibility
It’s important to us that all students are able to fully engage with our course content. We’re working to ensure that all digital materials – the website, course notes, homework and lab PDFs, supplemental videos, etc. – meet WCAG 2.1 AA accessibility standards, as mandated by the federal government of all public universities in the United States. If you find any accessibility issues (hard-to-read colors, missing alt text, videos with missing captions, or anything else that makes the content difficult to access), please let Suraj know as soon as possible at rampure@umich.edu.
Overview
Instructor
The instructor is Suraj Rampure. If you need to get in touch, please reach out on Slack or email rampure@umich.edu.
Content
Linear algebra, calculus, and probability form the basis of modern machine learning and artificial intelligence. This course will introduce linear algebra from scratch by focusing on methods and examples from machine learning. It will give students strong intuition for how linear algebra, calculus and probability are used in machine learning. While the course is primarily theoretical, we’ll look at practical applications involving real data in Python each week, so that students are able to apply what they’ve learned.
This course aims to provide:
- Enough rigor to ensure students can succeed in more theoretical ULCS courses (like EECS 445).
- Enough context, motivation, and applications for students to see why linear algebra is useful in machine learning, throughout the entire course.
Credit
This course is worth 4 credits, and is currently approved to count towards the linear algebra requirement for:
- Computer Science majors (CS-Eng and CS-LSA*).
- Data Science majors (DS-Eng and DS-LSA).
- Statistics majors.
- Artificial Intelligence minors.
And the linear algebra prerequisite for:
- EECS 442, EECS 445, EECS 448, EECS 474, EECS 476, and CSE 576.
- Any 400-level STATS or DATASCI course with a linear algebra prerequisite.
*CS-LSA students don’t have a linear algebra requirement, but this course fits in the linear algebra “bucket” for the second math course; see here for details.
Who should take this course?
You should take this course if you:
- Have not already taken a linear algebra course (if you've already taken Math 214, 217, 417, or 419, this course is not necessary, though you still may gain some perspective from taking it).
- Plan on taking Upper-Level CS courses about machine learning and artificial intelligence (e.g. EECS 445) and/or want to gain exposure to machine learning and Python early in your academic career.
Prerequisites
- EECS 203 or Math 116 (or Math 215, 216, 275, 285, or 295), i.e. some math course after Calculus 1.
- Some 100-level programming class (e.g. EECS 183, ENGR 101, ROB 102, or similar).
If you’re not sure if you meet the prerequisites – or if you don’t, but are still interested in taking the course nonetheless – email the instructor.
Getting Started
The course website, eecs245.org, will contain links to all course content. There are also a few things you’ll need to do to get set up.
Computer and Network Recommendations
Make sure you have a laptop consistent with CAEN recommendations.
More details
Test your internet connection with the UM Custom Speedtest website and make sure it meets the minimum requirements for any UM service. You'll need more bandwidth if there will be multiple simultaneous users in your household.
Resources for help with computing equipment:
- Information and Technology Services (ITS) Laptop loaner program
- College of Engineering (CoE) Office of Student Affairs, email requests to coe-studentaffairs@umich.edu
Websites
You’ll need to make accounts on the following sites:
Slack: We’ll be using Slack as our course message and discussion board. More details are in the Communication section below. You should receive an invitation to the course Slack workspace; if not, please let us know as soon as possible. This direct link should work, once you’ve received an invite.
Gradescope: You’ll submit all assignments to Gradescope, and this is where all of your grades will live as well. All homeworks must be handwritten, scanned into a PDF, and submitted to Gradescope. Some homeworks may also involve submitting code to an autograder on Gradescope; we will not be using the EECS department-specific autograder. You should have received an email invitation for Gradescope, but if not please let us know as soon as possible.
At some point in the term, we may switch from Gradescope to a different submission system: Pensive. Pensive is like Gradescope, but it allows for AI grading of handwritten work, with human review and the ability to ask for a regrade. Not only does this enable faster and more accurate grading, but it also frees up instructor time to spend working with students one-on-one, which AI cannot do. Pensive is currently being reviewed by the College of Engineering to ensure that it meets the necessary privacy and accessibility standards; rest assured, Pensive does not train AI models on your work. If and when we switch to Pensive, you are free to let the instructor know if you would like to opt-out of AI-based grading.
Note that we will not be using Canvas for anything this semester (so please don’t try and send us messages on Canvas!).
Programming Environment
Some labs and homeworks will involve writing Python code in Jupyter Notebooks. As soon as you can, follow the steps in the Environment Setup page to set up your programming environment locally on your computer. Alternatively, we’ve created a Jupyter Notebook server that will allow you to access and write code directly from your browser, but it can be unreliable, so you should only use it as a last resort.
Forms
Please fill out the required Welcome Survey to tell us a bit more about your background and whether you need alternate exams no later than Sunday, May 10th.
Communication
This semester, we’ll be using Slack as our course message board. Since the course is relatively small, Slack should be better suited than Ed to the kinds of smaller group discussions we expect to have.
If you have a question about course logistics, feel free to DM the instructor directly. If you have a conceptual question – if you’re stuck on a problem, didn’t understand something from lecture, or want to ask a general question about machine learning – please post in #general so that other students can chime in and see the response. Explaining something is a great way to solidify your understanding of it!
We’ll use #announcements to announce the release of new assignments and upcoming events, such as exams. You are responsible for keeping up with messages posted there.
Course Components
Lectures
Lectures will be held in-person on Tuesdays and Thursdays from 1-4PM in 1690 BBB. Because the spring term is compressed, each lecture will cover roughly two lectures’ worth of material from the regular semester. Attendance is not strictly required, though each lecture you attend earns you 1 engagement point, and engagement points are worth 5% of your overall course grade. It is possible to earn 100% in the course without attending lecture (by coming to office hours instead) but this part of the grade is designed to encourage you to come.
A recording will be posted after each lecture, either from the spring or from a previous semester. (Since we only have a few students in the class, in-person lectures may feel a bit more casual and personalized to the students present than in a regular semester, hence the posting of previous semester recordings.) We will do our best to make lectures interactive and well worth your time.
As part of your participation in this course, you may be recorded (e.g. if you answer a question). If you do not wish to be recorded, please email the instructor to discuss alternate arrangements.
Course notes have been written specifically for EECS 245, and will be your main resource in this class. These can be found at notes.eecs245.org.
These notes aren’t optional readings, they’re mandatory. It is nearly impossible to succeed in this course without reading them regularly. See the Advice section of the notes for testimonies from previous students.
If you find any typos or ways to improve the notes, please fill out this form.
Live lectures will closely follow the notes. Instead of presenting using slides in lecture, we will start with a blank document on a tablet, and write out the lecture content in real time, allowing us to move at a pace that is appropriate for the class and to focus on the high-level ideas (details can be found in the notes). Anything we write in live lectures will be posted as a PDF on the course website after lecture.
Labs
In a traditional semester, labs are held in person for 2 hours each week. Since our course is relatively small, we do not have any TAs, and we already have a high number of contact hours, we will not hold physical lab sections this term.
Instead, we will post two lab worksheets per week, each one posted the day before it is due:
- A Monday worksheet, containing problems from the previous Thursday’s lecture (due Monday 11:59PM)
- A Wednesday worksheet, containing problems from the previous Tuesday’s lecture (due Wednesday 11:59PM)
These worksheets are what students in a standard semester of EECS 245 would complete in lab, and are meant to provide hands-on practice with the recent lecture material and preparation for the upcoming homeworks and exams. They may even involve some programming.
We ask that you spend roughly 2 hours working on each worksheet, asking questions on Slack or in office hours as needed, and then submit your best attempt to Gradescope, where it will be graded for completion (not correctness). Don’t feel the need to answer every single question if you run out of time.
You do not need to print the worksheet itself (though you can); it is perfectly fine towrite your answers on a separate piece of paper or on a tablet and upload your work as a single PDF. As we discuss in the Homeworks section, you cannot use LaTeX or any other digital document creation tools in this class.
There will be 13 lab worksheets in total. Each lab worksheet you submit will earn you 1 lab point, up to a maximum of 10 lab points. Your lab score will be the number of lab points you earn out of 10. This means you can skip up to 3 labs for any reason (late add, extenuating circumstances, etc.) and still earn a full lab score. Details can be found in the Grades section below.
After the worksheet is due, solutions will be posted.
Homeworks
This class will have regular homework assignments throughout the compressed term, which are to be completed individually. Homework due dates may vary throughout the term; see the course homepage for the most up-to-date deadline schedule. We will try to give you ~one week to work on each homework.
Homeworks must be handwritten by each student individually, scanned into a single PDF, and submitted to Gradescope. That is, no typing and no LaTeX, unless you have accommodations specifying otherwise. In previous terms, we used to provide students with an Overleaf template. We’re intentionally not doing that this term, since we believe that handwriting the material helps you learn it better, better prepares you for exams, and is harder to cheat with AI. That said, using LaTeX and Overleaf is an important skill, and we may have one designated homework that requires everyone to practice with these tools once. In general though, you should expect to handwrite your homework (writing on a tablet is fine).
Collaboration Policy
The majority of homework problems will involve writing solutions to math problems, but some will involve writing code in a Jupyter Notebook. The point of a homework problem is not really to “find the answer” – we know the answers, and ChatGPT can easily provide answers to any homework problem we could ask you. Rather, the point of a homework problem is to help you deeply understand the material, apply it to new problems, and build problem-solving skills along the way that will pay dividends in future courses and in your life as a graduate of the University of Michigan.
Think of it like this: driving 10 miles is a lot easier than running 10 miles, and in many cases, driving makes total sense. But, if you’re trying to exercise, driving 10 miles won’t really help you achieve your goals. If you buy that analogy, just think of driving as “copying the answers from someone else” and running as “thinking through problems yourself” – both may earn you the same score on homeworks, but only the latter will help you in the long run.
With that in mind, you can – and are actually encouraged to! – talk to other students in the class about the problems and discuss solution strategies. Talking and debating about challenging ideas is a great way to solidify your own understanding of the material. That said, you should not share any written communication. You can tell someone how to approach a homework problem if they’re stuck, but you cannot show them how to do it. One way to tell if you are respecting this boundary is to ask yourself whether your collaboration could take place over the phone. Additionally, the content of your verbal communication should involve the problem-solving strategy and approach, and you should not directly compare answers with classmates.
Similarly, you can ask generative AI tools like ChatGPT for help with concepts, because these tools can be genuinely useful. Just be cautious that they can also lead you astray. If you find a particularly useful explanation online, either on a website or from a generative AI tool, please let us know! You cannot ask generative AI tools to solve homework problems for you or to verify your answers. Doing so is only cheating yourself. Remember you will not be able to use these tools during the exams, which are worth 70% of your overall grade.
If we suspect that your homework answers are not the result of your own work, we reserve the right to interview you in-person and ask you to answer and explain similar questions, and submit a case to the Honor Council.
You may post homework-related questions on Slack, though your questions and answers should be about approaches, not answers to specific problems. If your question includes some or all of an answer (even if you’re not sure it’s right), please DM the instructor instead of posting it publicly. We are not able to tell you whether your answer is correct.
Late Policy, Slip Days, and Drops
All homeworks must be submitted by 11:59PM Ann Arbor time on the due date to be considered on time. If you make a submission after the deadline, your assignment will be counted as late.
Every student has 5 slip days by default. A slip day extends the deadline of a homework by 24 hours, and slip days will be applied automatically to late homework submissions. You may use a maximum of 1 slip day per homework, so that we can release homework solutions shortly after the deadline. Slip days do not apply to labs.
Slip days are designed to give you flexibility to work on your homework at your own pace, and to help you catch up if you fall behind. If you find yourself needing more than 5 slip days, please meet with the instructor to discuss your situation.
In addition, your lowest 2 homework scores will be dropped from your final grade. This means that you can miss up to 2 homeworks for any reason and still earn a full homework score.
Regrade Requests
Most homework problems will be graded manually. If you believe that the grader has made a mistake in applying the rubric shown to you on Gradescope, you may submit a regrade request directly on Gradescope within one week of the grades being released. If you do not submit a regrade request within one week, your original grade will be final. Part of your grade is clarity, so if your answer was mostly right but unclear you may still not be eligible for full credit.
Some programming problems will be graded automatically by the autograder. If you believe that the autograder has made a mistake in grading your homework, send the instructor an email, again, within one week of the grades being released. Note that it’s rare that something is wrong with the autograder, and if that’s the case, we’ll typically fix the necessary test cases and re-run the autograder for the entire class.
Office Hours
The instructor will usually be free shortly before and shortly after lecture to answer questions. That said, the standard way to attend office hours is to make an appointment using this Google Calendar link. Appointments are 15 minutes long, though they can run longer if needed, and are available both on Zoom and in person. If you’d like to meet with the instructor but no times are available, let them know on Slack.
Each office hours appointment counts as 1 engagement point, up to a maximum of 2 office hours engagement points per week. As is discussed below, engagement points are worth 5% of your overall course grade, and attending office hours is therefore expected any time you miss a lecture.
In a theoretical class such as this one, it’s important to make good use of office hours. Homework assignments will be challenging, so you should plan to attend office hours at least once a week. And even if you’ve mastered the material, still come and say hi – take advantage of the fact that this is a relatively small class.
Exams
This class has two Midterm Exams and one Final Exam, all of which will be administered in-person and on paper.
| Exam | Date and Time | Content |
|---|---|---|
| Midterm 1 | Friday, May 22nd, 1-3PM | Chapters 1-4 of the course notes |
| Midterm 2 | Tuesday, June 9th, 1-3PM | Chapters 5-8 of the course notes |
| Final Exam | Wednesday, June 24th, 8-10AM | 40% content from Chapters 9-10; 30% content from Chapters 1-4; 30% content from Chapters 5-8 |
Midterm 2 is not cumulative. The Final Exam will be cumulative, and broken into three parts:
- Part 1 of the Final Exam will be based on Midterm 1 content. If you score higher on Part 1 than you did on Midterm 1, we will replace your Midterm 1 score with \(\frac{\text{Part 1 score} + \text{Midterm 1 score}}{2}\).
- Part 2 of the Final Exam will be based on Midterm 2 content. If you score higher on Part 2 than you did on Midterm 2, we will replace your Midterm 2 score with \(\frac{\text{Part 2 score} + \text{Midterm 2 score}}{2}\).
- Part 3 of the Final Exam will be based on the content introduced after Midterm 2.
This “redemption policy” for Parts 1 and 2 is designed to help you boost your Midterm 1 and Midterm 2 scores if you didn’t do as well as you’d hoped. This policy can only help your grade; it can’t hurt. To be clear, all three parts of the Final Exam are required and part of your Final Exam score.
If you have conflicts with any of the exams, please let us know on the Welcome Survey. We may provide alternate exam times for students with a valid, documented conflict with a required activity in another course or official university-affiliated activity, or to help students avoid negative academic consequences when their religious obligations conflict with academic requirements.
Exams are to be completed individually, with absolutely no collaboration allowed. Any suspected violations will be reported to the Honor Council.
Grades
Engagement Points
In addition to exams, homeworks, and labs, the course includes an engagement component. For full credit, you must earn at least 14 engagement points (the number of lecture days in the term, not including exams).
You can earn 1 engagement point for each lecture you attend and each office hours appointment you attend, up to a maximum of 2 office hours engagement points per week. There are many more than 14 opportunities to earn engagement points during the term, so you shouldn’t worry about not being able to make it to every lecture.
Weights
| Component | Weight | Notes |
|---|---|---|
| Midterm 1 | 20% | see Exams section above |
| Midterm 2 | 20% | see Exams section above |
| Final Exam | 30% | Â |
| Homeworks | 15% | • Due dates may vary throughout the term • 4 slip days by default; max 1 per homework; ask for more • Lowest 2 scores dropped |
| Labs | 10% | 13 total; 3 lowest scores dropped |
| Engagement | 5% | Earn 1 point for each lecture attended and each office hours appointment attended; 14 points earns full credit |
Letter Grades
Grading for this class is not curved in the sense that the average is set at (say) a B+ and half of the class must receive a grade lower than that. If everyone does well and shows mastery of the material, everyone can receive an A (this would be awesome!). If no one does well (this is unlikely), then everyone can receive a C.
Grading for this class is curved in the sense that we do not have a pre-defined mapping from project and exam scores to a final GPA. There is no pre-determined score (e.g., 90% of all possible points) that earns an A or a B or a C or any other grade. To determine the final grade, we will ask questions like “Did this student master the material?”. With that said, grades will not be any stricter than the standard grading scale (where an A+ is a 97+, A is 93+, A- is 90+, etc). For instance, the threshold for an “A” will never be higher than 93%.
You can see Fall 2025’s letter grade distribution here; Winter 2026’s distribution was similar, and we will aim to achieve a similar distribution this semester, but minor variations are expected. Frankly, given that this is a small class, we are perfectly happy with every single student earning an A, if they truly master the material. We’re here to work with you to make this happen.
Try your best not to worry about grades, and we’ll reciprocate by being fair. We’re in this together ❤️.
Student Support and Well-Being
Accommodations
If you need, or think you might need, an accommodation for a disability, please let us know during the first three weeks of the semester. Some aspects of this course may be modified to facilitate your participation and progress. As soon as you make us aware of your needs, we can work with the Services for Students with Disabilities (SSD) office to help us determine appropriate academic accommodations. SSD (ssd.umich.edu; 734-763-3000) recommends accommodations through a Verified Individualized Services and Accommodations (VISA) form. Any information you provide is private and confidential and will be treated as such.
Diversity and Inclusion
It is our intention that students from all backgrounds and perspectives will be well served by this course, and that the diversity that students bring to this class will be viewed as an asset. We welcome individuals of all ages, backgrounds, beliefs, ethnicities, genders, gender identities, gender expressions, national origins, religious affiliations, sexual orientations, socioeconomic background, family education level, ability - and other visible and nonvisible differences. All members of this class are expected to contribute to a respectful, welcoming, and inclusive environment for every other member of the class. Your suggestions are encouraged and appreciated.
Campus Resources
As a student, you may experience a range of issues that can negatively impact your learning, such as anxiety, depression, interpersonal or sexual violence, difficulty eating or sleeping, loss/grief, and/or alcohol/drug problems. These mental health concerns or stressful events may lead to diminished academic performance and affect your ability to participate in day-to-day activities.
In order to support you during such challenging times, the University of Michigan provides a number of confidential resources to all enrolled students, many of which are listed here. Some particularly useful resources include:
- CoE CARE Center
- Counseling and Psychological Services (CAPS); 734-764-8312 (24/7 line)
- Sexual Assault Prevention and Awareness Center (SAPAC); 734-936-3333 (24/7 line)
- Psychiatric Emergency Services: 734-996-4747
- Services for Students with Disabilities (SSD); 734-763-3000; ssdoffice@umich.edu
CSE Resources
Acknowledgements
This course is being offered for the third time at the University of Michigan. With that said, many of the materials we will use are adopted from content created by countless other instructors for courses at other institutions.
- In particular, some materials are adopted from EECS 398: Practical Data Science, which itself was based on content from DSC 10, DSC 40A, and DSC 80 at the University of California, San Diego; and Data 6 and Data 100 at the University of California, Berkeley.
- Some homework problems have been adapted from various linear algebra courses and textbooks online and at other institutions. Some of these sources are listed at the bottom of the introduction to the course notes.
- Language in this syllabus has been adopted from other courses as well, including EECS 203, EECS 280, EECS 376, and EECS 485 here at the University of Michigan, and CSE 160 at the University of Washington.
Disclaimer
While we try to do our best to plan ahead, unfortunately, sometimes circumstances do arise that necessitate a policy change. When this happens, the change will be announced, and this document will be updated with the new policy.
We appreciate any and all feedback, given that this course is new and evolving. If you’d like to provide us with anonymous feedback at any point, you can do so at this form. Thank you.