Syllabus

This is a pair of hands-on courses on AI systems using machine learning, with a particular emphasis on deep neural networks.

The pair is composed of two half-semester courses: CS 375 and CS 376. It is designed so that students who can only take 2 credit hours can take only CS 375 and finish at Spring Break, while students who are able to go in more depth can continue to CS 376. (Although not recommended, it is technically possible to take only CS 376. But we highly recommend taking the two courses in sequence.)

Objectives

CS 375 focuses on fundamentals; CS 376 dives into generative AI. But both courses are organized around the same 4 key pillars of modern AI: neural computation, ML systems, learning, and context/implications.

(Note that most interesting concepts live at the intersection of pillars, so the descriptions below should feel like they overlap somewhat.)

The key questions in each pillar motivate why we should care about studying that topic. The key objectives are specific things we can show that we can do as a result of studying that topic.

Neural Computation

Today’s ML systems are a mashup of two kinds of computational objects: the traditional sequential programming that we’re used to is still usually the “outer loop” of an ML system, but that code is the caretaker for a very different kind of animal: a highly parallel vector computer controlled by billions of parameters. This pillar is about understanding how that parallel vector computer works and how we can control it.

Key questions

Key objectives

After this course, I will be able to:

Optional objectives
The following objectives were planned for the course but did not end up being covered in class. Some students may pursue them in projects, but they are not required for the course.
  • General
    • I can compare and contrast the following neural architectures - CNN, RNN, and Transformer. (Bonus topics - U-Nets, LSTMs, Vision Transformers, state-space models) [NC-Architectures]
    • I can explain the purpose of a convolution layer and how it operates on an image. (Bonus topics - pooling, strides, padding) [NC-Convolution]

Not core objectives, but useful for understanding the field:

  • State-space models
  • Quantization and pruning
  • Key-value caching

ML Systems

Key questions

Key objectives

After this course, I will be able to:

Optional objectives
The following objectives were planned for the course but did not end up being covered in class. Some students may pursue them in projects, but they are not required for the course.
  • APIs and Systems
    • I can create a computational notebook that includes code, execution results, section headings, and formatted textual explanations. [MS-API-Notebook]
    • I can integrate an ML model into a larger application. [MS-API-Integration]
  • Experimentation and Evaluation
    • I can design, run, and analyze empirical experiments to quantify the impact of hyperparameter changes on model performance. [MS-Eval-Experiment]
    • I can make and interpret plots of relevant evaluation metrics. [MS-Eval-Visualize]

Learning Machines

Key questions

Key objectives

After this course, I will be able to:

Optional objectives
The following objectives were planned for the course but did not end up being covered in class. Some students may pursue them in projects, but they are not required for the course.
  • General
    • I can explain how in-context learning can be used to improve test-time performance of a model. [LM-ICL]

Context and Implications

CS 375 and 376 will investigate broader contexts and implications of AI from many lenses.

Key questions

Key objectives

The implications of AI are vast, so we will not attempt to cover everything in this course. Here are the basic objectives that we will aim for:

Beyond the basic objectives, students will have opportunities to explore a variety of types of broader contexts and implications of AI. Students will generally choose two specific areas of depth (for 25SP, we’re only requiring one of these). Areas include:

Specific topics may include:

Optional Topics

I encourage students to research and share material on these and other AI-related topics:

Prerequisites

A background at the level of either CS 212 or DATA 202 will be be generally expected. Beyond that, students should come to this course with some (perhaps rusty) ability to:

Although CS 375 is not a formal prerequisite for CS 376, students who do not have a solid understanding of the objectives of CS 375 should be prepared to proactively identify and fill in those gaps as they arise.

Materials

Policies

How will the course be graded?

I designed the grading scheme of this class to:

CS 375 Grading Scheme

The course grade is determined by the number and breadth of objectives that are fulfilled. The list below gives a tentative mapping of objectives to letter grades; the final mapping will be determined collaboratively at the conclusion of the class and may require exceptions in special cases.

  • A: at most one missing objective in any of the 4 pillars AND at least 2/3 of PPP points
  • B: no fewer than 3/4 of the objectives satisfied in each of the 4 pillars AND at least 1/2 of PPP points
  • C: no fewer than half of the objectives satisfied in each of the 4 pillars AND at least 1/3 of PPP points
  • D: at least one objective satisfied in each of the 4 pillars AND at least 10% of PPP points.

Note: For the Context and Implications pillar, only one “Topics” pillar is required. For example, if you cover none of the Topics objectives but all of the Basic objectives, you can still earn an A. Likewise, if you cover a Topics objective, you can miss a Basic objective and still earn an A.

Note: There was some inconsistency about how we’d verbally discussed the A-level grade in class. If your understanding was different from the above, please discuss that in the self-assessment (described below).

Other requirements (“should” means “strongly encouraged”, but not strictly required):

  • At least one objective in each pillar should be met by a project. (You can meet multiple objectives across several pillars in a single project, so you probably only need one bigger project or a few mini-projects.)
  • At least two project objectives should be met before the final week of class. (i.e., avoid last-minute work).
  • At least one objective in each pillar should be met through a discussion with the instructor.

If most but not all criteria of one grade level are met, a - should be assigned to that grade. If some but not all of the criteria of the next grade level are met, a + should be assigned to the original grade.

At the end of the course you will submit a self-assessment document where you will reflect on your understanding of AI according to the objectives listed above. We will attempt to track completion in the Moodle gradebook, but since Moodle was designed for traditional bucket-of-points grading, it will not track perfectly. So keep track of your progress on your own and include the grade that you have demonstrated in your self-assessment, along with references to items in your portfolio that demonstrate that competency.

For a rough illustrative example (not meant to be prescriptive or override the criteria above), the following might characterize the sort of student who would earn a C in this course:

  • Can use an AI API in contexts very similar to those explored in class (but doesn’t think deeply about whether it’s appropriate)
  • Can discuss superficially how an AI system can learn from training data (but doesn’t draw implications beyond that)
  • Can give a general example of what the input and output of an AI system is and what processing the system generally does (but the descriptions are vague, can’t actually perform the computations by hand or in code)
  • Can match a clearly specified ML task with an appropriate performance metric; doesn’t confuse regression with classification even if the classes happen to be encoded numerically (but doesn’t deeply consider real-world implications of this metric, i.e., what it means practically for a model to perform well by that metric)
  • Applies data splitting techniques (at least train-test) without being explicitly instructed to do so; doesn’t train on the test set.
  • Can list several categories of social and contextual questions (such as bias, privacy, interpretability, etc.), but describes them generically; can’t draw implications in specific situations. Can mention some generic principles from the reformed Christian tradition that might apply to those categories, but doesn’t make specific connections.
CS 376 Grading Scheme
  • The list of objectives is given in the Objectives section above, under headings marked 376.
  • Students can meet objectives at three levels: “progressing” (P), “met” (M), and “excellent” (E).
    • The Progressing (P) level can be met by assignments (such as lab notebooks and discussion forums). The instructor will track these and ensure that there is an assignment corresponding to each assessed objective.
    • The Met (M) level requires either an in-class quiz, an interaction (with the instructor, or perhaps with a chatbot or a peer), or a self-directed project.
    • The E level is given at instructor discretion to work that demonstrates understanding, strategy, or disposition that is likely to generalize robustly beyond this scope of course. As a concrete example, a successful interview for a ML-centered job would demonstrate E-level completion of an objective.
    • The instructor may limit the number of objectives that can become Met in a given week. So students are strongly encouraged to Meet objectives promptly.
    • Although the “basic” CI objective is required, students only need to complete 2 of the CI “Topics” objectives. Any objectives beyond that are extra credit (computed by adding the score achieved to both the numerator and denominator of the final objectives grade).
  • The course grade will be determined by 3 factors:
    • The number of objectives met at each level:
    • The quality of the course project.
    • PPP (completion) credits

Specifically, we propose to compute the final grade as a weighted mean:

  • 10%: PPP points (instructor may adjust the denominator depending on how this category is affecting the course grade)
  • 20%: course project (see that page for detailed specs). Technical work for projects will be counted towards course objectives; this grading section is mostly focused on the quality and coherence of the project.
  • 70%: course objectives, computed as the mean of all objectives scores, where 0 = not addressed, 1 = P achieved one time, 2 = P achieved 2 times, 3 = M achieved, 4 = E achieved. This average (0-4) will then be rescaled to the 0-100 scale such that 1 = D+, 2 = C+, 3 = B+, 4 = 100%.

For example:

An M in all course objectives, a B-level project, and full participation = (.1 * 1.0) + (.2 * .85) + (.7 * .89) = 89.3%, a B+, so any effort to achieve an E will push the grade up to A-.

This grading scheme addresses the following problems with the CS 375 grading scheme:

  1. It was not sufficiently clear what it meant to meet an objective.
  2. Demonstrations of objectives piled up at the end of the course.
  3. No distinction was made between meeting an objective superficially and meeting it deeply.
  4. The Context and Implications section was not well-integrated with the rest of the course.
  5. The breadth requirement (across pillars) added unnecessary complexity.

How do I demonstrate that I’ve met objectives?

You can demonstrate that you’ve met an objective through a reflection in a project, a meeting with the instructor, or a screen recording of a chatbot conversation. See “How to Demonstrate Objectives” for details.

Graduate students taking this course will be graded on the same overall criteria, but with more rigorous expectations for what it means to meet an objective. For example, graduate students should curate a portfolio at a level of completeness and refinement that they could present to a potential employer. Their work should also demonstrate engagement with primary sources and more rigorous evaluation, both quantitative and qualitative, of their own and others’ work.

Preparation, Practice, and Participation (PPP)

As a community, we will undertake many activities that don’t directly demonstrate proficiency but are important for shaping our community and retaining what you’ve learned.

PPP activities are graded by completion, not content. Any legitimate effort by the due date will be awarded a completion credit. Late completion is okay (but frowned on) for solo activities but not for community activities. In some cases the same activity will have multiple occasions of engagement (e.g., posting a comment and responding to others’ comments); in that case, each occasion will receive a PPP activity point.

Are Incomplete grades offered?

An incomplete grade (I) will only be given in unusual circumstances, and only if those circumstances have been confirmed by the Student Life office.

Do I have to come to class?

Attendance is not mandatory, but highly encouraged, both for your own learning and as one of the main ways to contribute to other students’ learning. Come to class:

Also note that many in-class activities will earn PPP points, so if you miss many class meetings you may have difficulty earning a high grade.

I have some special needs; will you accommodate them?

Disabilities: Calvin University is committed to providing access to all students. If you are as student with a documented disability, please notify a disability coordinator in the Center for Student Success (located in Spoelhof University Center 360). If you have an accommodation memo, please come talk to me in the first two weeks of class. If something comes up mid-semester, like an injury, please reach out to the disability coordinator and me.

How do I demonstrate academic integrity in this class?

The primary purpose of exercises in this class is to help you learn the material. The primary purpose of assessments are to help you retain the material. Academic integrity entails using course materials for the purposes that they were designed, not bypassing those purposes in an attempt to obtain answers without effort or demonstrate performance without learning.

Moreover, your work in this class should demonstrate gratitude and respect to those whose work enables yours. It should demonstrate the integrity necessary to produce work that your future employer can legally use. And it should demonstrate an active embrace of the often-necessary struggle of figuring things out yourself. So I expect you to credit the people who help you, be they classmates or StackOverflow strangers, and heed the license terms under which they offer their code.

Solutions to exercises are easy to find. You are expected not to refer to them until after you have submitted your work. If you do refer to them, you are required to clearly indicate that you have done so within the assignment.

If you realize that your actions have violated academic integrity principles, please let the instructor know as soon as possible.

Etiquette: We expect you to treat students and instructors for this with respect by adopting courteous communication practices throughout the course. No personal attacks, trolling, bad language will be tolerated.

How should we use AI in this course?

Thoughtful use of all types of AI is highly encouraged in this class. However, you should be capable of fulfilling most of the class objectives without AI assistance.

Encouraged Uses

You are encouraged to use AI tools to support your learning process by:

Use a variety of technologies for different purposes: LLMs (ChatGPT, Claude, Gemini, …), search, speech interactions, image/video/diagram generation.

You are encouraged to use these tools collaboratively with other students and to discuss and share your strategies.

Cautions and Guardrails for AI Use

It is crucial that you practice evaluating AI outputs criticaly, since they will sometimes be incorrect, distracting, misguided. Dialogue LLMs like ChatGPT are trained to give you answers that feel correct and feel like they help your understanding.

Avoid using AI to bypass your own thinking and learning. For example, don’t use AI to generate first drafts for short-answer questions or discussions. Instead, write your thoughts first and ask for AI feedback. Prompts might include “what is unclear or incorrect about my answer?” or “please list phrases in my writing that might be extraneous”. Honor your readers’ time and attention.

If you do at any point include any AI-generated content in something you submit, please make a reasonable attempt to mark what sections are AI-generated and to include what prompts you used. (Your prompts are often more interesting than the outputs!)

Diversity and Inclusion

I came to Calvin because I wanted to explore what our Christian calling to “act justly” means in the context of AI, data, and the technologies that we use with it. Engaging that question wholeheartedly requires that each of us, me included, engage respectfully with perspectives very different from our own. For example, we must question those who abuse data for selfish gain, but we also must question the perspectives of those who challenge those abuses on purely secular grounds.

I intend for this class to be an environment where we equally respect people of every ethnicity, gender, socioeconomic background, political learning, religious background, etc. I will try to create that community by having us read diverse voices, engage with issues of importance to people unlike ourselves, and structure discussions that require students to engage respectfully with perspectives different from their own. I invite your help.

We will not always do this well. If you or someone else in this class is hurt by something I say or do in class, I would like to work to remedy it. I will welcome this feedback in whatever way is comfortable for you: in public, in private, via another person (such as our TA or my department chair, Keith VanderLinden), or via a report to Safer Spaces or the provost’s office.

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