Course Catalog
Section G · College-Preparatory ElectiveUC Honors · +1.0 GPA
Artificial Intelligence & Machine Learning Honors
UC A-G approved Artificial Intelligence & Machine Learning Honors course delivered fully online through immersive VR and AI-enhanced instruction.
1/2 Year Online · Remote / Hybrid / In-Person Mathematics - Computer Science Grades 9, 10, 11, 12 5 credits
Section GUC A‑G Requirement
College-Preparatory Elective — 1 year required
Must be chosen from additional courses in categories A–F (beyond the minimum requirements) OR from other UC-approved college-preparatory electives, including computer science, business, statistics, and interdisciplinary STEM courses.
UC Honors Designation
AP and IB electives earn 1 extra weighted GPA point. Some G electives (Statistics, Computer Science) satisfy UC quantitative reasoning requirements.
Why This Matters for College Admission
G electives are where students develop depth and signal academic passion. AI, cybersecurity, data science, and entrepreneurship courses are highly competitive differentiators for tech-oriented admissions profiles.
What You'll Learn
Develop computational thinking skills: decomposition, abstraction, and algorithms
Write, debug, and document code in industry-relevant programming languages
Understand data structures, sorting algorithms, and complexity analysis
Apply mathematics (discrete math, statistics, or calculus) to computing problems
Build working software projects with version control and collaborative workflows
Explore career pathways in software engineering, data science, and AI/ML
Course Structure
Unit 1Weeks 1–4
Foundations & Context
- Core vocabulary and concepts
- Skill baseline assessment
Unit 2Weeks 5–9
Core Content
- Primary content delivery
- Applied practice activities
Unit 3Weeks 10–14
Application & Synthesis
- Real-world application
- Final project or exam prep
Curated syllabus · 6 units · 60 videos
Artificial Intelligence & Machine Learning Honors: study order
Six fast-paced units covering classical ML through neural networks, tracking the canonical Andrew Ng + 3Blue1Brown + StatQuest sequence.
Overview
Khan Academy
Khan Academy
Introduction to AI and Machine Learning | YUVA AI for All | MEITYKhan Academy India - English
Also see(2 more picks)
Specialist
Also see(3 more picks)
Specialist
Lecture 15 - PCA and ICA | Stanford CS229: Machine Learning Andrew Ng - Autumn 2018Stanford Online
Free-Response / Exam
Also see(1 more pick)
Overview
Khan Academy
Also see(2 more picks)
Specialist
Specialist
Stanford CS229: Machine Learning - Linear Regression and Gradient Descent | Lecture 2 (Autumn 2018)Stanford Online
Also see(3 more picks)
Free-Response / Exam
Free-Response / Exam
Linear Regression Using Least Squares Method - Line of Best Fit EquationThe Organic Chemistry Tutor
Also see(1 more pick)
Curated for Artificial Intelligence & Machine Learning Honors. Videos open in YouTube's privacy-enhanced (nocookie) embed; cookies are blocked until you click.
Prerequisites & Requirements
Open to qualifying students in grades 9, 10, 11, 12; no prerequisites required
All students complete a placement conversation with our admissions team before enrollment is confirmed. Questions? Contact admissions.