Machine Learning Honors
Course Description
Honors-level, hands-on introduction to machine learning. Students learn the end-to-end ML workflow: problem framing, data preparation, supervised and unsupervised modeling, and model evaluation. Core topics include linear and logistic regression, decision trees, clustering, and basic neural networks, with emphasis on ethics, bias, and limitations. The course culminates in a capstone project applying ML to a real dataset.
| Credits | 5 (one semester) |
| UC A–G Area | G — College-Preparatory Elective (confirm on UCOP A–G list) |
| Level & GPA Weight | Honors (weighted; confirm UCOP/district policy) |
| Recommended Grades | G10–12 |
| Prerequisites | 1 semester of Python (or equivalent); Data Science Honors |
| Textbook | Instructor-provided readings and datasets (no fixed text) |
Major Units & Topics
- Intro to ML & problem framing
- Data prep: cleaning, splitting, feature engineering
- Supervised learning: regression & classification
- Unsupervised learning: clustering & dimensionality reduction
- Model evaluation & validation
- Intro to neural networks
- Ethics, fairness, and responsible AI
- Capstone project
Practical Work
- Notebook labs for EDA, modeling, and evaluation
- Mini-projects using real datasets
- Capstone project (proposal → build → presentation)
Technology & Platforms
- Python (Jupyter/Colab), pandas, scikit-learn, visualization libraries
- Versioned notebooks & reproducible workflows
- Curated public datasets & instructor-supplied data
Delivery Options
| Model | What’s Included | Live Hours and Tuition |
|---|---|---|
| Mini-One-on-One | Targeted model-building help, code reviews, and capstone pacing | Typically 12 hours at $119/hour |
| One-on-One | Fully personalized instruction & capstone mentorship | Typically 24 hours at $119/hour |
For current tuition, see the pricing section or contact our team.
Time & Workload Expectations
- Total live session hours: ~22 hours (One-on-one); ~12 hours (Mini-one-on-one).
- Pre-study for Mini-one-on-one: plan an additional ~12 hours of structured pre-study (before live sessions).
- Independent work: ~25–30 hours for assignments, reviews, projects, and studying for assessments.
- Communication: Q&A and periodic progress updates via email (all models).
- Weekly load depends on course window (start/end dates). Example:
(22 live hours + 38 independent hours) ÷ 12 weeks ≈ 5 hours/week(Adjust independent hours to match your model and pacing.)
Grading & Course Policies
| Category | Weight |
|---|---|
| Classwork | 10% |
| Tests/Quizzes | 20% |
| Homework | 30% |
| Project | 20% |
| Final Test | 20% |
- Assignments: due next session unless noted; one-session late accepted with reduced credit.
- Class conduct (for synchronous): active participation, focused device use, photos only with permission.
Additional Notes
- Pacing Guide: Personalized plan based on start/end dates and seasonal schedules.
- Accessibility: Reasonable accommodations available; notify counselor/instructor during onboarding.
- Parent Updates: Progress check-ins available upon request (per FERPA/guardian permissions).
Track Eligibility (Academic Track & AI Certification)
- Full-Time (≥5 courses/year): counts toward certifications.
- Dual Enrollment (LCP school of record) (≥3 courses/year): counts toward certifications.
- Dual Enrollment (other school of record) (≥6 LCP courses across high school with counselor-approved plan): counts toward certifications.
- Part-Time à-la-carte: does not count toward certifications.