Syllabus

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

• Instructor: Ken Arnold, Calvin University, North Hall NH298
• Meeting Time: MWF 11:00-11:50, NH 253 or SB 354 (Gold Lab).
• Lab Hours: Generally Monday and Thursday afternoons; see my Calendar for details and exceptions. Or message me directly; time permitting, I’d love to chat.

Objective

By the end of this course, you will demonstrate growth in your ability to:

• Apply machine learning technology to solve real-world problems. You should be able to:
  • Select an appropriate ML approach to solve a problem.
  • Translate a problem statement into a system design.
  • Identify necessary characteristics of the data required for an ML system.
  • Design and implement data preprocessing processes to encode data into a format appropriate for machine learning modeling.
  • Evaluate how well an ML system performs on a task.
• Explain and debug a machine learning system. You should be able to:
  • Describe the basic components of modern ML systems.
  • Write the computations of simple models (linear regression, logistic regression, and a one-layer MLP) in math and code.
  • Select appropriate loss functions and metrics for a given task.
  • Identify hyperparameters that can be adjusted to improve the performance of a model.
  • Identify and recognize problems in model training, such as overfitting, underfitting, divergence, poor initialization, and poor hyperparameter choices.
  • Use automatic differentiation to train a model using stochastic gradient descent.
• Analyze the history and ongoing development of using AI and ML techniques in situations affecting people. You should be able to:
  • Identify and describe specific historical and contemporary examples of ethical and social issues that arise around ML systems, both in terms of risks and opportunities.
  • Explain machine learning concepts in a way that is understandable to non-technical audiences.
  • Apply Reformed Christian concepts such as shalom, humanity in the image of God, and the creation-fall-redemption-restoration narrative to both critique existing systems and envision new ones.

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:

• Read and write Python code (or be willing to invest significant energy the first few weeks picking it up)
• Think systematically, generate and test hypotheses to explain observations, and communicate that thinking in precise language
• Manage time, individually and in small groups
• Collaborate to solve problems

Materials

• Moodle contains links to all of the resources used in this course. It will also be where you engage in discussion forums and submit assignments.

• We will be following the fast.ai Practical Deep Learning for Coders course for the first third or so of this course. They provide the following materials that will be useful to us:

  • The book (Deep Learning for Coders with Fastai and PyTorch) is available in the Calvin bookstore and also freely available online (links provided in the corresponding reading assignments)
  • Links will also be provided to the author’s video lectures corresponding to each section, where applicable.
  • You may find it helpful to browse their forum, or even post questions there.

• After the fast.ai material we’ll switch to the Natural Language Processing with Transformers Book, which is also available in the bookstore. (If cost is a barrier for you, talk with me.)

• We will be using Ed Discussions for Q&A. Many students have found that Ed works well for getting you help fast and efficiently from classmates and myself. Rather than emailing questions to the teaching staff, I encourage you to post your questions on Ed. Access Ed by clicking on the link in Moodle.

• The lab machines are configured with the software needed in Linux. We’ll generally use the Gold lab because its GPUs are slightly better, but Maroon should usually work too. For times when you need or want additional computing resources, you may use Borg, Google Colab, Paperspace, or other services. The Guacamole remote access machines have older GPUs, so see notes in the Lab 1 and 2 instructions for tips on making things run there (or just use Google Colab).

Policies

How will the course be graded?

The final course grade will be composed of 2 elements:

• Outcome: did you achieve the learning objectives?
• Process: did you put wise effort into this class?

The Moodle gradebook will compute an approximate suggested grade based on weighting both elements equally. However, in the closing weeks of the semester you’ll have a one-on-one meeting with the instructor to discuss your final grade. You will propose a final grade and justify it using your current work and write a contract of what you will achieve by the end of the semester (primarily the final project).

How are Process grades computed?

Most Process points are awarded for satisfactory effort on selected assignments (not announced in advance) by the due date, to incentivize staying on track in the course. Other activities, such as reflections and peer feedback, will also be graded for Process.

Process points vary based on type of assignment:

• Fundamentals Notebooks, Homework and Reflections (see below) will be considered satisfactory if an attempt has been made to answer all questions by the due date. Each activity will be allocated 5 Process points. On-time submissions receive all 5 points; late turn-ins are penalized by 1 point each business day (i.e., excluding weekends).
• Discussion Forums (see details) will be awarded 2 Process points if an original post with intellectual substance is made by the due date. Many discussion assignments will also include one or two replies to other students, which will count for 1 Process point each. Late posts may receive half credit.
• Quizzes (both preparation and review) can earn up to 5 Process points.

Can I turn in things late?

Everyone will receive 5 free Process points, i.e., you get five grace days. Please reserve these for cases of personal hardship. Exemptions per assignment will be granted in limited circumstances.

How are Outcome grades computed?

Outcome grades will be computed by an equal weighting between Homework, Tests (3 midterms, no final) and the Final Project.

How do I revise assignments?

Revisions are expected and highly encouraged. You may submit up to one revision per week. To submit a revision:

• Submit a complete, standalone solution on Moodle. (Clean up anything from your past errors.)
• Email the instructor with the following:
  • What assignment you’re revising (you may revise any past assignment that was graded for Mastery)
  • A concise and specific explanation of the original error(s) and how you addressed them. This should convince the instructor that you have not only achieved a correct result but have learned through the process.

Can we work in pairs?

Everything you turn in in this class should be your own work. However, there will be structured opportunities to provide help and feedback to other students; these opportunities will be discussed in class.

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:

• to ask the questions that you think everyone else already knows the answer to (but in fact they nod in agreement because they were wondering that too).
• to help your fellow students figure out that thing that just clicked for you yesterday.
• or just because you want to discuss AI!

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.

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.

Typical Weekly Expectations

• Weekend: read (actively!) a textbook chapter and/or watch a video lecture; complete a prep quiz before class on Monday.
• Wednesday: discussion forum due by class time, come to class with insights to share
• Friday 5pm: homework and/or reflection due

Reflections

A Reflection is a summary of your learning the past two weeks. They typically include:

• A response to each of the learning objectives for each of the two weeks.
• Feedback to the instructor about how things are going

Details are given in the specific assignments.

Note: We have not yet finalized whether or not to do reflections this year.