Syllabus: AINS6002 Machine Learning & Predictive Modeling

Course Identity

Field	Value
Course	AINS6002 Machine Learning & Predictive Modeling
Program	Aurnova Master of Science in Artificial Intelligence
Track	Core
Credits	3 graduate credits
Format	8-week online graduate course with weekly instructor interaction
Primary environment	Google Colab for first-run labs; GitHub Codespaces for repository-based or instructor-guided work

Catalog Description

Covers supervised, unsupervised, and operational predictive modeling with reproducible evaluation.

Student Audience and Prerequisites

AINS6001 or equivalent AI foundations exposure is recommended. Prior Python experience is helpful but not required; labs are scaffolded for first-run Colab use. The course is designed for graduate students preparing to lead, evaluate, manage, or apply AI work in professional settings. Students are not expected to be computer science majors. Technical work is scaffolded so students can run a notebook, observe output, change one controlled variable, and explain the evidence in professional language.

Course-Level Learning Outcomes

By the end of this course, students will be able to:

• CO1: Analyze professional problems in Machine Learning & Predictive Modeling and formulate AI use cases with explicit stakeholders, decision boundaries, data assumptions, and success criteria.

• CO2: Execute or interpret reproducible notebook-based investigations that demonstrate core Machine Learning & Predictive Modeling methods using guided Python/Colab workflows.

• CO3: Evaluate model, workflow, or governance evidence for accuracy, validity, uncertainty, bias, security, privacy, and operational limitations appropriate to core MSAI contexts.

• CO4: Produce professional artifacts for Machine Learning & Predictive Modeling, including briefs, model cards, risk registers, evaluation memos, implementation plans, or executive recommendations.

• CO5: Apply responsible AI, academic integrity, data stewardship, accessibility, and human-oversight expectations to course work and proposed deployments.

• CO6: Communicate AI findings, limitations, tradeoffs, and next-step recommendations to technical and nontechnical stakeholders using clear graduate-level evidence.

Credit-Hour and Contact-Hour Rationale

This 3-credit accelerated graduate course expects approximately 135 total student learning hours across the 8-week term. The course design allocates approximately 45 hours to instructor-led lecture, narrated content, guided lab demonstration, discussion, feedback, and synchronous or asynchronous interaction. Approximately 90 additional hours are allocated to reading, notebook practice, applied assignments, revision, and the final portfolio artifact. Each module contains enough slide and narration material for a 90-minute class session, plus labs and assignments that extend learning beyond direct instruction.

Weekly Schedule and Major Deliverables

Week	Module Topic	Essential Question	Primary Deliverable
1	Prediction tasks and data framing	What decision is the model actually supporting?	Module 1 Assignment: Prediction tasks and data framing
2	Data preparation and feature pipelines	How do preprocessing choices shape model behavior?	Module 2 Assignment: Data preparation and feature pipelines
3	Linear and tree-based baselines	Why do strong baselines matter before complex models?	Module 3 Assignment: Linear and tree-based baselines
4	Model selection and validation	How do we choose models without overfitting our evidence?	Module 4 Assignment: Model selection and validation
5	Unsupervised learning and structure discovery	How can models reveal patterns without labels?	Module 5 Assignment: Unsupervised learning and structure discovery
6	Time, drift, and monitoring	What changes after a model leaves the notebook?	Module 6 Assignment: Time, drift, and monitoring
7	Interpretability and stakeholder explanation	What explanations are appropriate for different audiences?	Module 7 Assignment: Interpretability and stakeholder explanation
8	Predictive modeling portfolio	What evidence supports a production recommendation?	Module 8 Assignment: Predictive modeling portfolio

Assessment Plan

For Populi implementation, Modules 1–3 and 5–7 supply the six module-applied-assignment scores; all eight labs supply the lab/reflection scores; the Module 4 assignment plus its Modules 1–3 synthesis is the mid-course brief; and the Module 8 model card and reproducibility package is the final portfolio artifact. Modules 4 and 8 must not be counted again as generic module assignments.

Component	Weight	Evidence
Module applied assignments	35%	Six professional artifacts from Modules 1–3 and 5–7, scored with module rubrics; Modules 4 and 8 are assessed in the signature categories below.
Notebook labs and reflections	20%	Guided Colab/Codespaces labs with before-after interpretation and limitation statements.
Mid-course synthesis brief	15%	Integrative memo or design artifact covering Modules 1-4.
Final applied portfolio artifact	20%	Cumulative artifact demonstrating evidence, risk reasoning, and stakeholder communication.
Participation and professional engagement	10%	Discussion, peer review, instructor check-ins, and revision responsiveness.

Passing performance requires both cumulative course performance of 70% or higher and submission of the final applied portfolio artifact. Graduate-level performance requires accurate vocabulary, evidence-based reasoning, responsible limitation statements, and professional communication. Students may revise selected artifacts when the instructor determines that revision supports learning outcomes and academic integrity.

Grading Scale

Grade	Range	Interpretation
A	93-100	Excellent graduate performance; evidence is rigorous, well communicated, and professionally defensible.
A-	90-92	Strong graduate performance with minor gaps in depth, precision, or integration.
B+	87-89	Good performance; work is complete and evidence-based but not consistently advanced.
B	83-86	Satisfactory graduate performance; meets core expectations with some limitations.
B-	80-82	Marginal graduate performance; recurring gaps require attention.
C	70-79	Minimal passing performance; significant weaknesses in evidence, accuracy, or communication.
F	Below 70	Does not demonstrate required course outcomes.

Required Readings, Standards, and Professional Resources

The instructor may update editions, links, or equivalent institutional resources before the term begins. Readings combine scholarly foundations, professional standards, and applied guidance.

Before the course opens, the instructor or program designee verifies library access, link currency, accessible formats, and any approved substitutions. Populi identifies the authoritative assigned edition or link for the live offering; students should use that version rather than an older saved copy.

Module	Topic	Required Reading or Standard	Applied Professional Resource
1	Prediction tasks and data framing	James et al., An Introduction to Statistical Learning, supervised learning and resampling.	Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning, model assessment.
2	Data preparation and feature pipelines	Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning, model assessment.	Kohavi, A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.
3	Linear and tree-based baselines	Kohavi, A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection.	Breiman, Random Forests, Machine Learning, 2001.
4	Model selection and validation	Breiman, Random Forests, Machine Learning, 2001.	NIST AI RMF 1.0, Measure function.
5	Unsupervised learning and structure discovery	NIST AI RMF 1.0, Measure function.	Molnar, Interpretable Machine Learning, selected chapters.
6	Time, drift, and monitoring	Molnar, Interpretable Machine Learning, selected chapters.	Sculley et al., Hidden Technical Debt in Machine Learning Systems.
7	Interpretability and stakeholder explanation	Sculley et al., Hidden Technical Debt in Machine Learning Systems.	Mitchell, Machine Learning, evaluation and inductive bias.
8	Predictive modeling portfolio	Mitchell, Machine Learning, evaluation and inductive bias.	James et al., An Introduction to Statistical Learning, supervised learning and resampling.

Learning Technology and Beginner Support

Students should start notebook labs in Google Colab unless the instructor specifies Codespaces. Colab lowers setup burden and supports students who are new to Python. Students should first run the notebook unchanged, then change one small value and explain what changed. Codespaces is used when an activity requires the full repository, multiple files, tests, or GitHub workflow evidence. The course assesses interpretation, evidence, limitations, and professional judgment; it does not assume that every student enters as a programmer.

Before launch, the instructor or program designee tests Colab and Codespaces access, equivalent activity paths, assistive-technology concerns, and the support escalation route. Populi provides the current accessibility, accommodation, and technical-support contacts for the live offering.

Academic Integrity and Permitted AI Assistance

Students must submit their own analysis, decisions, explanations, and reflections. Use of generative AI tools is permitted for brainstorming, debugging, summarizing public documentation, and improving prose when allowed by the instructor, but students must disclose material AI assistance and remain responsible for factual accuracy, citations, code behavior, and final claims. Fabricated citations, hidden AI-generated submissions, unauthorized collaboration, or submission of work the student cannot explain may be treated as academic misconduct.

Accessibility and Accommodations

Aurnova is expected to provide reasonable accommodations consistent with institutional policy and applicable law. Students who need accommodations should contact the designated institutional office and notify the instructor as early as possible. Course materials should be provided in accessible formats when feasible, including readable HTML pages, downloadable notebooks, text-based instructions, and alternatives for activities that require specific hardware or external accounts.

Data Privacy, Safety, and Responsible Use

Unless explicitly approved by the instructor and institution, students must not upload confidential, regulated, proprietary, patient, student, financial, security-sensitive, or personally identifiable information into Colab, Codespaces, public repositories, or third-party AI tools. Labs use synthetic or public proxy data. Students must document data assumptions, privacy risks, and operational limits before recommending real-world use.

Late Work, Participation, and Instructor Interaction

Students are expected to engage weekly with readings, lecture material, labs, and applied deliverables. Late work may receive reduced credit unless prior arrangements are approved or institutional policy applies. The instructor should provide regular substantive interaction through announcements, discussion, feedback, office hours or appointments, assignment feedback, and timely grading. Participation is evaluated by evidence of preparation, constructive discussion, peer feedback, and revision effort rather than by technical bravado.

Accreditation Evidence Location

Aurnova maintains program-outcome mappings, assessment records, and continuous-improvement evidence separately from the public learner site. Those records are not learner coursework or a substitute for the syllabus, rubric, or Populi gradebook.