The course uses Canvas and Gradescope for homework submission, grading, discussion, questions, announcements, and supplementary documents; slides will be posted here; Slack is used for communication around homework and projects; Github is used to coordinate group work. All public course material (assignments, slides, syllabus) can be found in the course’s GitHub repository; announcements and all private material (e.g., grades, passwords) will be shared through Canvas.
Prerequisites: The course does not have formal prerequisites, but we describe background knowledge that will help you be successful in the course. In a nutshell, we expect basic exposure to machine learning and basic programming skills, but do not require software engineering experience.
Machine learning (some experience recommended): We suggest that you have basic familiarity with the process of extracting features, building and evaluating models, and a basic understanding of how and when different kinds of learning techniques work (including, but not just limited to prompting LLMs). Familiarity with Python and Jupyter notebooks is helpful. Courses such as 10-301, 10-315, and 05-434 will prepare you well, but project experience or self-learning from books or online courses will likely be sufficient for our purposes. For example, if you have no prior experience, we recommend the book Hands-On Machine Learning to get practical experience in training and evaluating models prior to taking this course. We have set up a prerequisite knowledge check as a Google Form, where we ask 10 questions on machine learning, which help you assess your background – this is set up as an anonymous and ungraded quiz, where you can compare your knowledge against what we believe is useful for you to be successful in this course (click on “view score” after submitting your answer). After submitting your answers, the system will give specific pointers to readings and exercises that may help you fill gaps in background knowledge.
Programming (basic proficiency required): The course has a substantial programming component, especially in the first assignment and the team project, so basic programming skills will be needed. If you take the course without programming experience or solely rely on LLMs/agents for all your coding, you will significantly struggle and it may cause conflicts within the group project. We expect that you meet the following criteria: (1) basic fluency in a programming language like Python, (2) ability to install and learn libraries in that language, (3) ability to ssh into a Unix machine and perform basic command line operations, and (4) ability to install and learn new tools like Docker. We do not prescribe a programming language, but almost all student teams decide to work primarily in Python. We will provide some introductions and examples for essential tools like Git, Docker, Grafana, and Jenkins in labs, but we expect that you will be able to pick up new tools and libraries on your own. For example, we expect that you will be able, on your own, to learn basic use of a library like Flask to write a web service. Throughout the semester, expect to read lots of documentation and tutorials to learn various libraries and tools on your own. If you are worried whether your technical background is sufficient, we recommend that you look at (or even try) homework I1 before the semester.
Software engineering (no experience required): Many students will have some software engineering experience beyond basic programming skills from software engineering courses, from internships, or from working in industry, for example experience with requirements engineering, software design, software testing, distributed systems, continuous deployment, or managing teams. No such experience is expected as a prerequisite; we will cover these topics in the course.
Email the instructors if you would like to further talk to us about prerequisites.
In-person teaching and lecture recordings: The course will be taught in person. We consider in-class participation an important part of the learning experience. We do make best effort lecture recordings, which will be available in Canvas. We do not provide a synchronous remote option, and we do not record labs. You are welcome to use recordings to review material and to make up missed lectures. However, absent explicit accommodations (see below), viewing the recording will not make up for missed in-class participation.
We regularly use Slack for in-class activities. Please make sure that you have access to Slack on a laptop, tablet, or mobile phone during class.
If you cannot attend class due to a medical issue, family emergency, interview, or other unforeseeable reason, please contact us about possible accommodations. We try to be as flexible as we can, but will handle these cases individually.
Exams: The course has two midterms and a final project presentation in the registrar-assigned timeslot of the final exam (to be announced about halfway through the semester here). The midterms are during the normal class period as per schedule. The second midterm is not comprehensive, and only covers material after the first midterm. Examples of past midterms can be found in the course repository.
Grading: Evaluation will be based on the following distribution: 35% individual assignments, 30% group project, 15% midterms, 5% participation, 10% labs, 5% reading quizzes. No final exam.
We strive to provide clear specifications and clear point breakdowns for all homework to set clear expectations and take the guessing out of homework. We often give you choices to self-direct your learning, deciding what to work on and how to address a problem (e.g., we never prescribe a programming language). Clear specifications and point breakdowns allow you to intentionally decide to skip parts of assignments with clear upfront consequences. All parts will be graded pass/fail for the points indicated, no partial credit. For opportunities to redo work, see resubmissions below. For grading participation and quizzes see below. Some assignments have a small amount of bonus points.
Since we give flexibility to resubmit assignments, we set grade boundaries fairly high. We expect the following grade boundaries:
| Grade | Cutoff |
|---|
| A+ | >99% |
| A | >96% |
| A- | >94% |
| B+ | >91% |
| B | >86% |
| B- | >82% |
| C | >75% |
| D | >60% |
Participation: Design and engineering content requires active engagement with the material and discussions of judgment decisions on specific scenarios and cases. We strongly believe in in-class discussions and in-class exercises and want all students to participate, e.g., answering or asking questions in class, sharing own experiences, presenting results, or participating in in-class votes and surveys. We will give many opportunities for participation in every lecture and lab. We note student engagement with in-class activities to include as a component in grading. We also consider cheating in in-class activities as an academic integrity violation (see below). We will provide feedback at mid-semester so that you can check in on how you’re doing. Again, please talk to us if you need accommodations.
We assign participation grades as follows:
- 100%: Participates actively at least once in most lectures (4 lectures waived, no questions asked)
- 90%: Participates actively at least once in two thirds of the lectures
- 75%: Participates actively at least once in over half of the lectures
- 50%: Participates actively at least once in one quarter of the lectures
- 20%: Participates actively at least once in at least 3 lectures.
- 0%: Participation in less than 3 lectures.
Labs: Labs typically introduce tools and have a task with one or more clear deliverables. Lab assignments are designed to take about 1h of work and can be completed before or during the lab session. Each deliverable is graded pass/fail at any time during that week's lab session (your own section) by showing your work to the TA. Typically showing your work involves showing source code, demoing executions, and (verbally) answering a few questions. The TA may ask a few questions about your implementation to probe that you understand your work.
We intend labs to be very low stakes – this is your first practical engagement with the material and mistakes are a normal part of the learning process. Deliverables are graded pass/fail on whether they meet the stated expectations for the deliverables. If your solution does not meet the expectations you can continue working on it during the lab session until it does. Outside of explicit accommodations (e.g., medical issues) or using tokens (see below), we do not accept lab solutions after the end of your lab session.
We encourage collaboration on labs: You can work together with other students both before the lab session and during the lab session. While we do not recommend it, you may look at other students’ solutions and reference solutions and even copy them. However, you will have to present and explain your solution to the TA on your own.
Textbook, reading assignments, and reading quizzes: We will be using Goeff Hulten's "Building Intelligent Systems: A Guide to Machine Learning Engineering" (ISBN: 1484234316) throughout much of the course. The library provides an electronic copy. In addition, we will provide various additional readings, including blog posts and academic papers, throughout the semester.
We also wrote our own textbook "Machine Learning in Production" that aligns closely with the lecture content -- available for free online and in print through MIT Press or anywhere where books are sold. We will not assign chapters from our own textbook, but we always point to the corresponding chapter for each lecture. You might find these useful to review concepts from the lectures.
We will assign readings for most classes and post a corresponding quiz on Canvas that is due before class. Each quiz contains an open-ended question that relates to the reading. Reading quizzes are intended to be low-stakes assessments and are graded pass/fail for a good-faith effort to engage with the question.
Teamwork: Teamwork is an essential part of this course. The course contains a multi-milestone group project to be done in teams of 3-5 students. Teams will be assigned by the instructor. A TA will serve as a mentor for each team. We will help teams throughout the semester and cover some specific content on teamwork as part of the course. Peer rating will be performed for team assignments with regard to team citizenship (i.e., being active and cooperative members), following a procedure adapted from this article, which we will further explain in an early lecture. Use this form to preview the expected adjustments for peer ratings. The team's mentor will also debrief with the team after every milestone and discuss possible strategies to improve teamwork.
Late work policy and resubmissions: We understand that students will always have competing deadlines, unusual events, interviews for job searches, and other activities that compete with coursework. We therefore build flexibility and a safety net directly into the rubric. If you need additional accommodations, please contact us.
In addition, we expect that the past/fail grading scheme without partial credit, may lead to harsh point deductions for missing small parts of the requirements, so we provide a mechanism to resubmit work with a short reflection to regain lost points.
Every student receives 8 individual tokens that they can spend throughout the semester in the following ways:
- For each token, a student can submit a homework assignment 1 day late (with 2 tokens a student can submit two homeworks one day late each or a single homework up to two days late). If a student runs out of tokens, late individual assignments receive a penalty of 15% per started day.
- For three tokens, a student can improve or redo an individual homework assignment and resubmit together with a short reflection. The earlier submission is discarded and the regraded assignment counts toward the final grade. Resubmissions can be made at any time in the semester up to the final project presentation (see schedule). – Note that this technically allows a student to blow the original deadline (no submission necessary, receiving 0 points initially) and then resubmit the homework arbitrarily late for three tokens.
- For one token, a student can submit a reading quiz late (any time before the final presentation) or resubmit a graded reading quiz.
- For one token, a student can complete a lab late or redo a lab (any time before the final presentation) by showing the work to a TA during office hours.
- Penalties for late/missing team formation survey and late/missing teamwork peer assessment surveys can be waived for one token each.
- Remaining individual tokens at the end of the semester are counted as one participation day each.
Every team independently receives 8 team tokens that they can spend for extensions of any milestone deadline (1 token per day per milestone, except final presentation deadline) or to resubmit any milestone with a reflection (3 tokens each, resubmitted any time before the final presentation). If a team runs out of tokens, late submissions in group assignments receive a penalty of 15% per started day.
Individual tokens and team tokens are entirely separate; it is not possible to use individual tokens for teamwork or vice versa. The team should make collective decisions about how to use team tokens.
In general, late submissions and resubmissions can be done at any point in the semester before the final presentations. Late submissions that are 1-3 days late can be made directly to Gradescope; for everything else see instructions and forms on Canvas.
Exceptions to this policy will be made at the discretion of the instructor in important circumstances, almost always involving a family or medical emergency and an email from your advisor — you can ask your academic advisor or the Dean of Student Affairs requesting the exception on your behalf. Where issues affect teamwork, please communicate proactively with your team.
Communication: We make important announcements on Slack; we recommend to enable Slack notifications. We answer email and monitor Slack, which may all be used for clarifying homework assignments and other interactions. We strongly recommend to ask questions publicly on Slack if others might have similar questions. Email or slack us if you would like to make an appointment.
Auditing: Due to the high demand for this course, we do not allow auditing. If you like to self-study, all course materials are online. We welcome interested students and visitors to sit in for lectures as long as the room capacity allows it.
Time management: This is a 12-unit course, and it is our intention to manage it so that you spend close to 12 hours a week on the course, on average. In general, 3 hours/week will be spent in class, about 1 hour for the labs, 1-2 hours on readings and reading quizzes, and 6-7 hours on assignments. Notice that much homework is done in groups, so please account for the overhead and decreased time flexibility that comes with groupwork. Please give the course staff feedback if the time the course is taking for you differs significantly from our intention.
Writing: Describing tradeoffs among decisions and communication with stakeholders from other backgrounds are key aspects of this class. Many homework assignments have a component that requires discussing issues in written form or reflecting about experiences. To practice writing skills, the Global Communications Center (GCC) offers one-on-one help for students, along with workshops. The instructors are also happy to provide additional guidance if requested.
Use of content generation AI tools and external sources: Given the nature of this course, we are open to using AI tools for completing work. We place no restrictions on the use of content generation tools, such as ChatGPT, Claude, Bard, Co-Pilot, Stable Diffusion, or Cursor. You may also reuse code from external sources, such as StackOverflow or tutorials. In any case, you will be solely responsible for the correctness of the solution. Note that content generation tools often create plausible-looking but incorrect answers, which will not receive credit. Using code generation tools without understanding the generated code will likely create teamwork problems and create challenges of meeting the lab requirements. You are also responsible for complying with any applicable licenses.
Academic honesty and collaboration: The usual policies apply, especially the University Policy on Academic Integrity. Many parts of the work will be done in groups. We expect that group members collaborate with one another, but that groups work independently from other groups, not exchanging results with other groups. Within groups, we expect that you are honest about your contribution to the group's work. This implies not taking credit for others' work and not covering for team members that have not contributed to the team. This also applies to in-class discussions, where indicating working with others who did not participate in the discussion is considered an academic honesty violation. Otherwise, our expectations regarding academic honestly and collaboration for group and pair work are the same as for individual work, substituting elevated to the level of "group."
Beyond that, the key guiding principle of academic honesty in this course is: "You may not copy any part of a solution to a problem that was written by another student (in this or prior iterations of the class), or was developed together with another student, or was delegated to another person. You may not look at another student's solution, even if you have completed your own, nor may you knowingly give your solution to another student or leave your solution where another student can see it." Note that this implies that you cannot publicly post your solutions on GitHub (e.g., as part of a portfolio during job applications). While the use of AI content generation tools is okay (see above) using the work from other students is not. Discussing challenges and solution strategies with others at a high level is okay, sharing code or text is not.
You may collaborate with other students on labs, but not on reading quizzes, homeworks, and exams.
We also expect and respect honesty when communicating with the course staff.
Any violation of this policy is cheating. The minimum penalty for cheating will be a zero grade for the whole assignment. For cheating on participation, the minimum penalty is -100% on the participation grade. Cheating incidents will also be reported through University channels, with possible additional disciplinary action (see the University Policy on Academic Integrity). There is no statute of limitations for violations of the collaboration policy; penalties may be assessed (and referred to the university disciplinary board) after you have completed the course, and some requirements of the collaboration policy (such as restrictions on you posting your solutions) extend beyond your completion of the course.
If you have any question about how this policy applies in a particular situation, ask the instructors for clarification.
Research in this Course: We are conducting academic research in this course. This research will involve analyzing student work of assignment. You will not be asked to do anything above and beyond the normal learning activities and assignments that are part of this course. You are free not to participate in this research, and your participation will have no influence on your grade for this course or your academic career at CMU. If you do not wish to participate, please send an email to Nadia Nahar (nadian@andrew.cmu.edu). Participants will not receive any compensation or extra credit. The data collected as part of this research will not include student grades. All analyses of data from participants’ coursework will be conducted after the course is over and final grades are submitted -- instructors will not know who chooses not to participate before final grades are submitted. All data will be analyzed in de-identified form and presented in the aggregate, without any personal identifiers. If you have questions pertaining to your rights as a research participant, or to report concerns to this study, please contact Nadia Nahar (nadian@andrew.cmu.edu) or the Office of Research Integrity and Compliance at Carnegie Mellon University (irb-review@andrew.cmu.edu; phone: 412-268-4721).
Accommodations for students with disabilities: If you have a disability with an accommodations letter from the Disability Resources office, we encourage you to discuss your accommodations and needs with us as early in the semester as possible. Please come and talk to us and do not rely on automated emails from the Disability Resources office. We will work with you to ensure that accommodations are provided as appropriate. If you suspect that you may have a disability and would benefit from accommodations but are not yet registered with the Office of Disability Resources, we encourage you to contact them at access@andrew.cmu.edu.
A note on self care. Please take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, avoiding drugs and alcohol, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress. All of us benefit from support during times of struggle. You are not alone. There are many helpful resources available on campus and an important part of the college experience is learning how to ask for help. Asking for support sooner rather than later is often helpful.
If you or anyone you know experiences any academic stress, difficult life events, or feelings like anxiety or depression, we strongly encourage you to seek support. Counseling and Psychological Services (CaPS) is here to help: call 412-268-2922 and visit their website at http://www.cmu.edu/counseling/. Consider reaching out to a friend, faculty or family member you trust for help getting connected to the support that can help.
If you are worried about affording food or feeling insecure about food, there are resources on campus that can help, such as the CMU Pantry.
Labor day, no classes
Midterm 1