Hands-on, math-first class in machine and deep learning. We'll start at supervised learning, loss functions, gradient descent, and the chain rule, then work forward and backprop by hand for a tiny model. We'll compare and contrast layer types (linear, conv; quick tour of transfoerms/RNNs) and training heuristics (like initialization, normalization, regularization, early stopping, and SGD vs. Adam). Hands-on time: create your toy net using NumPy, recreate it in PyTorch and get it to learn on a very small real dataset. Short whiteboarding blocks, live coding, and frequent check-ins where you get to do it yourself will occur. Colab links will be given out. Python familiarity is preferred, but not required; the math is graspable and is performed so it will stick, though basic knowledge of calculus is required.

Introduction to web development using HTML, CSS, and JS

Overview of computer science curriculum IB DP HL/SL

Beginner-friendly introduction to Unity and C#, where we learn to code by creating small video games. We'll learn Unity fundamentals—Scenes, GameObjects, Components, Prefabs, 2D physics, collisions, UI, and input—by writing in C#, and learning about the Unity Engine package. Every session includes a brief demo, code-along session, and challenge session, along with playtesting and feedback. Projects are a 2D arcade and an endless runner with an optional mini 3D scene. Students work hands-on with version control basics, debugging, and basic game polish (audio, particles, menus). No experience necessary—though Unity should already be installed on your device. I'll offer starter templates and stretch goals to keep everyone engaged. By completion of all sessions, every student completes a small, playable game and knows the core Unity + C# workflow to keep on building.