Fundamentals of Machine Learning
Currently catalogued as Machine Learning and Robotics
Machine learning is no longer practiced by hand-coding every model: industry practitioners now specify experiments, direct AI coding agents through data preparation and training loops, and remain accountable for results they did not type. The course keeps its theoretical core intact while making verification, reproducibility, and provenance of machine-generated work into graded skills rather than assumed ones.
Current catalog prerequisites — (CSC 200 or 2000).
Current description → proposed description
As sub-disciplines of artificial intelligence, machine learning and robotics hold much promise for the creation of practical and useful AI systems, but the correct understanding and application of these disciplines requires careful study of their theoretical underpinnings. This course examines the underlying theory behind current machine learning and robotic applications as well as their practical implementations. Students will work with state-of-the-art algorithms for supervised, unsupervised, and reinforcement learning. Students will also complete practical assignments related to robotic construction and navigation. Moral, ethical, and worldview issues related to these topics will be addressed from a Lutheran Christian perspective.
Prerequisites: (CSC 200 or 2000).
As sub-disciplines of artificial intelligence, machine learning holds much promise for the creation of practical and useful AI systems, but the correct understanding and application of these disciplines requires careful study of their theoretical underpinnings. This course examines the underlying theory behind current machine learning and robotic applications as well as their practical implementations. Students will work with state-of-the-art algorithms for supervised, unsupervised, and reinforcement learning. Students will also complete practical assignments related to robotic construction and navigation. Moral, ethical, and worldview issues related to these topics will be addressed from a Lutheran Christian perspective. Because production machine-learning work is now drafted and operated alongside AI coding agents, students will also practice specifying experiments precisely, reviewing machine-generated pipelines for leakage and irreproducibility, and taking accountability for model behavior they did not write by hand.
AppliedAIF2F_Proposal.xlsx), where it appears as “Fundamentals of Machine Learning”.
Workbook lists this as a Course Change BCI (CSC 2400).
What changes
- Retitled to Fundamentals of Machine Learning per the workbook
- Verification of ML pipelines generated by AI coding agents
- Provenance, attribution, and reflection on where the student's judgment deferred to the agent
- Bias, distribution-shift, and unequal-error-cost analysis carried through to concrete mitigations
- Compute and energy footprint treated as a stewardship constraint
6 proposed outcomes, mapped to 13 program outcomes
Each outcome below is written to be observable and assessable, and each is mapped to the program learning outcomes for which it produces evidence.
Students will be able to implement supervised, unsupervised, and reinforcement learning algorithms from provided mathematical formulations, interpreting the resulting held-out metrics to explain what they do and do not establish about the model's behavior in deployment.
PLO 6.1: explaining what held-out metrics do and do not establish about deployment behavior is the interpret-and-explain work this outcome names, not mere computation. PLO 6.2: computing appropriate held-out metrics and drawing conclusions from them is direct quantitative analysis of learned-model behavior.
Students will be able to evaluate machine-learning pipelines generated by AI coding agents for data leakage, unreproducible results, and unsupported claims, documenting the provenance of code and analysis the student did not write and reflecting, within a Christian understanding of personal responsibility for one's work, on where the student's own judgment overrode or deferred to the agent and why.
PLO 4.3: the required reflection on where the student's own judgment overrode or deferred to the agent, framed by a Christian understanding of responsibility for one's work, is self-assessment of the student's own problem-solving inside a faith-informed framework. PLO 5.1: documenting the provenance of code and analysis the student did not write is precisely the transparent, integrity-bearing communication this outcome names.
Students will be able to analyze training data and model outputs for bias, distribution shift, and unequal error costs across the populations a deployed system would affect, specifying concrete mitigations - reweighting, threshold adjustment, or targeted data collection - for the disparities identified.
PLO 4.1: analyzing model outputs for unequal error costs across affected populations is critical analysis of data-driven outcomes together with their societal impact. PLO 2.2: specifying reweighting, threshold adjustment, or targeted data collection to remove identified disparities is the design-side work of building systems that respect varied perspectives and avoid bias, not merely detecting it. PLO 6.1: analyzing model outputs for unequal error costs across the populations a deployed system would affect is interpreting AI data outcomes in terms of their impact across fields, and gives this Developed-level outcome a second source of evidence alongside CLO 1.
Students will be able to design a machine-learning or robotic solution to a documented community or humanitarian need, justifying its data, compute, and energy footprint against the benefit delivered.
PLO 2.1: targeting a documented community or humanitarian need is the applied form of building technology that addresses real human challenges. PLO 2.3: justifying the data, compute, and energy footprint against delivered benefit is explicit stewardship of AI resources. PLO 4.2: designing and then defending that solution is proposing and evaluating an AI-driven response to a complex issue.
Students will be able to construct a working robotic perception-and-navigation system as a member of an interdisciplinary team, negotiating tradeoffs between technical performance and the ethical constraints teammates raise.
PLO 3.3: building the system inside an interdisciplinary team while weighing performance against ethical constraints is exactly the technical-ethical balancing this outcome describes. PLO 3.2: constructing a deployable perception-and-navigation system whose design is shaped by the ethical and communicative constraints teammates raise is applying non-technical disciplinary skill to develop a human-centered application. PLO 5.2: negotiating those tradeoffs against constraints teammates raise exercises interpersonal skill in navigating diverse views within a technical team.
Students will be able to articulate the limits of learned systems, including opacity, brittleness under distribution shift, and the absence of moral agency, drawing on statistics, philosophy, and a Lutheran Christian account of human responsibility.
PLO 3.1: drawing simultaneously on statistics, philosophy, and a Lutheran Christian account of human responsibility to characterize the limits of learned systems is exactly the combination of data science, ethics, and theology this outcome names.
Program outcomes this course reaches
Filled cells are program learning outcomes with at least one supporting course learning outcome in this course. Sparse coverage is expected — no single course carries all eighteen.
The workbook maps this course too
The proposal workbook's assessment map already assigns program learning outcomes to this course. The outcomes above were written to cover it.
| Program outcome | Workbook level | In this draft |
|---|---|---|
| PLO 2.1 Service through Technology | I | covered |
| PLO 2.2 Engagement with Diversity | I | covered |
| PLO 2.3 Stewardship of AI Resources | I | covered |
| PLO 3.1 Interdisciplinary Problem-Solving | I | covered |
| PLO 3.2 Real-World AI Applications | I | covered |
| PLO 3.3 Collaboration in AI Projects | I | covered |
| PLO 4.1 Data and Ethical Analysis | I,D | covered |
| PLO 4.2 Innovative AI Solutions | I | covered |
| PLO 4.3 Reflective Decision-Making | I | covered |
| PLO 5.1 Clear and Ethical Communication | I | covered |
| PLO 5.2 Interpersonal Skills in AI Collaboration | I | covered |
| PLO 6.1 Data Interpretation in AI | I,D | covered |
| PLO 6.2 Quantitative and Qualitative Analysis | I,D | covered |
I = Introduced · D = Developed · AE = Assessed at Exit