Intro to
Applied AI
A high school course from Stevens that takes you from asking what AI actually is, all the way to building an AI system you can explain, test and defend.
Everyone can use AI. Almost nobody can question it. This is where you learn the difference.
Naveen Mathews Renji
At the College of Professional Education, he builds AI-focused, workforce-aligned programs, learning experiences and AI products. His background is in artificial intelligence systems, human-AI interaction and software engineering.
Press play for his short introduction to how it works.
Understand it. Question it. Build with it.
Where AI came from, why it suddenly took off, and why understanding it beats watching from the sidelines.
Full course outline
Phase 01: See AI Clearly (Concepts and Intuition), modules Modules 1 - 4
What is AI, how does it learn, and who is affected?
Build a practical mental model of AI before writing a line of code. You learn what AI is, how examples shape its behavior, how to inspect its mistakes, and why responsible use is part of technical quality.
- Module 01, AI Hidden in Plain Sight. AI versus automation, algorithms and models, machine learning and generative AI, everyday applications, and why confident output can still be wrong. Topics: Narrow AI, Algorithms vs models, Pattern recognition, Confident but wrong.
- Module 02, How AI Learns from Data. Datasets, features, labels, training and testing evidence, ground truth, data quality, and the effect of biased or incomplete examples. Topics: Features and labels, Train vs test, Ground truth, Data quality.
- Module 03, No-Code AI Lab. Train and test a real classifier in the browser, separate false positives from false negatives, inspect class imbalance, improve the evidence, and document limitations. Topics: Train a classifier, False positives, Mistake analysis, Iteration.
- Module 04, AI Ethics, Bias, and Safety. Fairness, unequal impact, hidden proxy variables, privacy, hallucinations, transparency, accountability, and meaningful human oversight. Topics: Fairness, Proxy variables, Privacy and consent, Human oversight.
Key terms: AI, narrow AI, automation, algorithm, model, machine learning, deep learning, generative AI, inference, input, output, dataset, training data, feature, label, validation set, test set, ground truth, classification, confusion matrix, false positive, false negative, class imbalance, model card, bias, fairness, disparate impact, proxy variable, privacy, consent, data minimization, hallucination, transparency, explainability, accountability, human oversight.
Phase 02: Work With the Foundations (Python, Data, and Machine Learning), modules Modules 5 - 9
How can we work with its code, data, and evidence?
Move from concepts to hands-on reasoning. Enough Python to work with data, enough statistics to question it, and enough machine learning to carry a beginner project from data preparation through evaluation.
- Module 05, Python Basics. Variables, collections, conditions, loops, functions, debugging, and the responsible use of AI coding assistance. Topics: Variables and lists, Loops and functions, Debugging, AI-assisted coding.
- Module 06, Working With Data. Table structure, data types, missing values, filtering, sorting, summaries, provenance, and reproducible cleaning. Topics: Rows and columns, Missing values, Filtering and sorting, Provenance.
- Module 07, Statistics for AI. Center, spread, distributions, outliers, base rates, sampling bias, and correlation versus causation. Topics: Mean and spread, Outliers, Sampling bias, Correlation vs causation.
- Module 08, Machine Learning. Supervised learning, features and targets, training loss, baselines, hyperparameters, overfitting, underfitting, and leakage. Topics: Supervised learning, Baselines, Overfitting, Data leakage.
- Module 09, Machine-Learning Project. Build a beginner model, compare accuracy, precision, recall and F1, adjust thresholds, document the workflow, and explain the limitations clearly. Topics: Build a model, Precision and recall, Thresholds, Explain the limits.
Key terms: variable, list, condition, loop, function, parameter, argument, return value, syntax error, runtime error, logic error, schema, categorical data, numerical data, missing value, data provenance, reproducibility, distribution, variability, base rate, sampling bias, correlation, causation, supervised learning, unsupervised learning, feature, target, prediction, regression, classification, training, train-test split, loss, hyperparameter, baseline, generalization, overfitting, underfitting, data leakage, metric, accuracy, precision, recall, F1 score, decision threshold.
Phase 03: Inspect Modern AI (Modern AI Applications and Workflows), modules Modules 10 - 14
How do modern AI systems work, fail, and stay controlled?
Make modern AI understandable without treating it as magic. Learn the essential mechanism behind each system, then inspect outputs, test weaknesses, verify evidence, and design safeguards.
- Module 10, Images and Vision. Pixels and features, neural networks, CNNs and learned weights, classification, detection, segmentation, and vision failure conditions. Topics: Neural networks, CNNs, Detection, Failure conditions.
- Module 11, AI for Language. Tokenization, embeddings, text tasks, semantic similarity, retrieval, semantic search, ambiguity, tone, and context. Topics: Tokenization, Embeddings, Semantic search, Ambiguity and tone.
- Module 12, Generative AI and LLMs. Transformers, context windows, multimodal models, hallucinations, grounding, retrieval-augmented generation, provenance, and verification. Topics: Transformers, Context windows, Hallucinations, RAG and grounding.
- Module 13, Prompting and Evaluation. Task framing, constraints, zero-shot through few-shot prompting, acceptance criteria, rubrics, revision, prompt injection, red-teaming, and regression tests. Topics: Task framing, Few-shot prompting, Rubrics, Red-teaming.
- Module 14, Agentic AI. Architecture, orchestration, tools and permissions, state and memory, execution traces, stopping conditions, escalation, and recovery. Topics: Tools and permissions, State and memory, Execution traces, Guardrails.
Key terms: pixel, feature, artificial neural network, convolutional neural network, weight, classification, detection, segmentation, natural language processing, token, tokenization, embedding, semantic similarity, retrieval, semantic search, transformer, next-token prediction, context window, multimodal model, hallucination, temperature, output variability, grounding, retrieval-augmented generation, provenance, prompt, role, context, constraint, format, zero-shot prompting, one-shot prompting, two-shot prompting, few-shot prompting, instruction hierarchy, acceptance criteria, rubric, prompt injection, red-teaming, regression test, chatbot, workflow, agent, guardrail, human-in-the-loop, architecture, orchestration, least privilege, state, memory, execution trace, stopping condition, escalation, recovery.
Phase 04: Build and Explain a System (Vibe Coding and Full-Stack AI Project Development), modules Modules 15 - 18
How do we build, test, document, and present one responsibly?
Turn knowledge into a coherent project. Specify an idea, understand the full-stack structure, build with guidance, test failure behavior, refine the experience, and present the result honestly.
- Module 15, Vibe Coding Studio. Specifications, requirements, acceptance criteria, prototypes, code review, version control, refactoring, technical debt, and bug evidence. Topics: Build prompts, Reading generated code, Bug logs, Version control.
- Module 16, Understand the System. System boundaries, frontend and backend, APIs and payloads, architecture, orchestration, data flow, control flow, and the user journey. Topics: Frontend and backend, APIs and payloads, Data flow, User journey.
- Module 17, Guided Build. AI integration, input validation, access control, end-to-end testing, logging, timeouts, retries, fallbacks, and graceful degradation. Topics: AI integration, Input validation, End-to-end tests, Fallbacks.
- Module 18, Refine and Showcase. Usability, accessibility, reliability, intended use, limitations, system documentation, monitoring, maintenance, residual risk, and presentation. Topics: Usability, System card, Residual risk, Final presentation.
Key terms: specification, requirement, acceptance criteria, prototype, bug log, version control, refactoring, technical debt, system boundary, user interface, frontend, backend, server-side logic, API, endpoint, payload, architecture, orchestration, data flow, control flow, input validation, end-to-end test, authentication, authorization, timeout, retry, fallback, logging, testing evidence, graceful degradation, usability, accessibility, reliability, system card, intended use, out-of-scope use, deployment, monitoring, maintenance, residual risk.
18 modules · 18 weeks · 4 phases · 1 capstone project
See AI Clearly
“What is AI, how does it learn, and who is affected?”
- 01
AI Hidden in Plain Sight
- 02
How AI Learns from Data
- 03
No-Code AI Lab
- 04
AI Ethics, Bias, and Safety
You finish with a system, not a summary.
The final phase pulls the whole course together. You specify an idea, learn how the frontend, backend and AI layers fit, build it with guidance, test how it fails, then present it honestly.
- Clear evidence it was tested
- A system explanation anyone can follow
- Honest limitations
- A responsible-use boundary
Every module runs on the same rhythm.
The structure is intentionally predictable. Once you learn it, you can focus on the ideas and skills instead of figuring out where to go next.
Watch
Begin with short readings, videos, examples, and visuals that introduce one key idea at a time.
Practice
Try the concept through guided activities, prompt tasks, audits, sorting exercises, or applied examples.
Check
Use quick checks to confirm what you understand and notice what may need another look.
Discuss
Connect the lesson to real situations through reflection and discussion prompts.
Save
Add your work to the portfolio so your growth is visible across the course.
Clear pacing
Each module moves from introduction to practice to reflection, so the work feels manageable.
Active learning
You do more than read about AI. You test ideas, inspect examples, and apply concepts.
Visible progress
Portfolio artifacts show how your AI literacy develops from the first module to the final project.
What you walk away with
A working AI application
Not a slide deck. A real full-stack, AI-enabled application you specified, built, tested and can demonstrate.
A portfolio of evidence
Eighteen modules of saved artifacts: notebooks, datasets, bug logs, evaluation rubrics and written reflections.
Fluency you can defend
175 technical terms you can use correctly, plus the habit of asking what evidence sits behind an AI system.
A Stevens certificate
Proof of completion from the College of Professional Education at Stevens Institute of Technology.
Finish the course. Then decide about college credit.
Intro to Applied AI is a complete, self-contained course on its own. Students who complete it and meet all course requirements may be eligible to pursue undergraduate credit by examination from Stevens Institute of Technology.
The course
Full access to all 18 modules and the certificate of completion.
- All 4 phases and 18 modules
- Videos, labs, quizzes and assignments
- Full-stack AI capstone project
- Certificate of completion
- Self-paced, start anytime
Credit by examination
For eligible students who want the work to count toward a Stevens undergraduate transcript.
- Sit the credit-by-examination assessment
- Undergraduate credit awarded by Stevens
- Requires full course completion
- Eligibility criteria apply
Credit by examination is optional and separate from course enrollment. Eligibility, assessment requirements and the awarding of credit are determined by Stevens Institute of Technology. Completing the course does not by itself guarantee credit.
Stevens Early Edge is the high school initiative of the College of Professional Education at Stevens Institute of Technology.
Common questions
Do I need prior coding experience?
No. The first four modules contain no code. You will write your first line of Python in Module 5. Learners who have coded before may move faster, but the course is designed so beginners can build confidence step by step.
Am I permitted to use AI tools to assist with assignments?
Yes. Use course-linked or instructor-approved AI tools to support learning, test ideas, improve work, and debug problems, not to bypass thinking. Follow any school or account-age requirements, identify AI assistance when an activity asks, and make sure you can explain everything you submit.
How long should I expect each module to take?
Plan for approximately 4 to 5 hours per module. Phase 1 modules may feel lighter. Phase 4 build modules may take longer depending on your project idea. The course is self-paced, so consistency matters more than speed.
What software do I need to install?
Nothing. The course uses browser-based tools, including Google Colab for Python, no-code AI tools for early modules, and AI-assisted development platforms for the final build. A reliable internet connection is the main requirement.
What will this course prepare me for?
This course gives you a practical foundation in AI: enough to understand key concepts, use AI tools effectively, evaluate outputs critically, and build a working AI-powered project.
What information should I never enter into an AI tool?
Do not enter passwords, account details, addresses, private messages, school records, identifying photos or recordings, confidential documents, or another person's personal information. Use fictional, public, or de-identified examples unless the course explicitly provides approved data.
Do I need to create accounts for every tool?
No. Follow the instructions and links provided in the course. Do not create a new account, begin a paid trial, or share personal information merely because a tool appears in an activity. Ask the instructor when an account requirement is unclear.