Module 1 · Foundations
ML and AI foundations for practical decisions
Frame an AI task, trace a rule, and save your first changed run.
Core Python environment
Explore the modules
Choose a learning route before selecting modules. Every module now links a guided construction and mastery check; advanced depth follows the common core evidence.
All 14 modules support direct entry with prerequisites, shared-code examples, original practice, public feedback, optional depth, and instructor guidance. Implementation includes recorded automated execution; learner piloting and manual accessibility review remain necessary.
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Module 2 · Foundations
Python for reproducible ML workflows
Read files, reuse functions, and debug a repeatable Python workflow.
Core Python environment
Module 3 · Foundations
Data processing and trustworthy inputs
Validate event records, reconcile rejections, and preserve missing values.
Core Python environment
Module 4 · Foundations
Exploration and visual communication
Explore distributions and patterns with charts, tables, and clear explanations.
Core Python environment
Module 5 · Classical ML
Regression and reliable evaluation
Forecast real bike demand with time-aware features and honest baselines.
Core Python environment
Module 6 · Classical ML
Classification and decision trees
Compare classifiers and decision trees without selecting on final-test data.
Core Python environment
Module 7 · Classical ML
Clustering and learning paradigms
Interpret real-data clusters while keeping labels out of unsupervised choices.
Core Python environment
Module 8 · Neural and language models
Neural networks with TensorFlow
Train and reload a TensorFlow model; compare it with a simpler reference.
Framework environment
Module 9 · Neural and language models
Neural NLP for real messages
Learn from real messages and diagnose what accuracy hides under imbalance.
Framework environment
Module 10 · Neural and language models
LLMs and evaluated generative workflows
Run a local language model and evaluate actual output-contract failures.
Framework environment
Module 11 · Neural and language models
Fine-tuning language models responsibly
Make real adapter updates and distinguish tuning mechanics from useful behavior.
Framework environment
Module 12 · Responsible systems
Deployment and monitoring
Connect checked inference to a local app and monitoring evidence.
Framework environment
Module 13 · Responsible systems
Ethics and fairness across the lifecycle
Audit group denominators, historical labels, and the consequences of decisions.
Core Python environment
Module 14 · Responsible systems
Bounded agents and AI workflows
Inspect a model-selected plan bounded by read-only tools and explicit budgets.
Framework environment
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Alignment conventions
CLO1 concerns learning paradigms; CLO2 applies ML/AI to IT operations; CLO3 interprets network data; CLO4 uses frameworks/evaluation; CLO5 integrates GenAI/LLMs; CLO6 covers deployment/monitoring; CLO7 data representations; CLO8 communication. Domain-transfer activities support IT outcomes but do not by themselves prove application to genuine IT records.
The supplied local module and syllabus sources were reread during the October 25 enhancement; private institutional text remains excluded from publication. See alignment authority and adoption mapping before formal assessment use.
Advisory A1/A2/A3/A4 labels in instructor notes identify analysis, solution evaluation, communication, and professional/ethical judgment opportunities under the planning register. They do not prove master's-level attainment or ABET compliance. Individual work does not establish teamwork/leadership. Ethics explicitly extends the original CLO list's identified gap.