About this course
A guided first research project for students who have completed Introduction to Machine Learning. Over the term each student moves from a question to a defensible result, mentored through the actual research loop: reading the literature, forming a hypothesis, choosing a method, running experiments, evaluating honestly, and writing it up. The projects are deliberately small, real, and self-contained — reproducing and extending a simple published result, an applied model on a dataset the student cares about, a careful comparison of model families, or a small fairness or robustness probe. It is not a novel algorithm or a leaderboard chase; it is a genuine first piece of research, and a portfolio artifact suitable for college applications and research programs.
Is this the right fit?
- Who it’s for
- Students aged 15+ who have completed Introduction to Machine Learning and want a genuine first research project for their applications.
- Prerequisites
- Completion of Introduction to Machine Learning I.
- Tools students use
- Python, scikit-learn, PyTorch, public datasets, and academic sources in Google Colab
Class schedule
Two weekly time-slot options, so students in Asia, the Gulf, and the Americas can all join live. Pick the one that fits; a coach confirms it when you enroll.
Times convert to your zone automatically and account for daylight saving. A coach confirms your slot when you enroll.
What students walk away with
- A completed, self-contained ML research project on a real question
- The research loop in practice: literature, hypothesis, method, experiments, honest evaluation
- Proper train/validation/test discipline and real error analysis
- A short research-style paper — abstract, method, results, limitations — plus reproducible code
- A presentation of the work, and guidance on framing it for admissions
What a session looks like
- 0:00
Progress check
Each student reports results since last week and what is blocking them.
- 0:30
Method clinic
The week's research skill — reading a paper, designing an experiment, an evaluation pitfall — taught live.
- 1:15
Mentored work
Students run experiments and write, with the coach on call.
Curriculum
Weeks 1–2 — Find a question
Choosing a tractable project; reading and citing sources responsibly; locking in a topic and dataset.
Weeks 3–4 — Baselines & method
Establish a baseline and design the experiment with proper train/validation/test discipline.
Weeks 5–8 — Experiments
Run, measure, and iterate; error analysis; avoiding the ways ML fools itself.
Weeks 9–10 — Write it up
Draft the paper — abstract, method, results, limitations — with a peer-review round.
Weeks 11–12 — Defend & present
Present the research and take questions; discuss sharing the work responsibly and framing it for applications.
What’s included
- 12 live small-group research sessions, capped at 6 students
- One-on-one mentoring on each student's project
- A short research-style paper and reproducible code
- A final presentation with feedback
- Guidance on submitting to research programs and referencing it in applications
How to join
- Purchase your seat. Choose your time-zone slot at checkout and pay securely. Classes are capped at six, so seats go in the order they’re booked.
- We confirm the details. After checkout a coach confirms your slot and asks a few quick questions so the first class is pitched right for your child.
- Sit the first class risk-free. If it’s not the right fit, tell us after that class and we’ll refund you in full.
Not sure yet? Book a free consultation or email hello@redwoodacademy.ai and a coach will help you choose.
Frequently asked questions
What does a realistic first project look like?
Small, real, and self-contained — not a novel algorithm and not a leaderboard chase. For example: reproduce a simple published result and extend it with one clean variable, build an applied model on a dataset you care about with proper discipline, or carefully compare two or three model families and analyze why one wins.
Is the paper good enough for college applications?
That is the design goal: a short research-style paper with reproducible code and a presentation, done to a standard a student can credibly discuss in a personal statement, interview, or supplemental essay, or submit to a research program.
When does it run?
It runs as the term after Introduction to Machine Learning, with the same weekend time-zone options. Graduates of the first course are invited first.