Machine Learning
Covers the fundamental concepts of machine learning and deep learning, from decision theory to neural networks. Mix of theoretical and practical activities, with a final project where students design, implement, and evaluate a complete ML pipeline.
Resources
A gamified, hands-on lab with a simulated terminal and live visualization of Git’s internal areas. Type real commands, earn points, and progress through 8 levels covering init, staging, commits, branching, merging, conflict resolution, remote operations, and .gitignore - all in the browser.
Watch SGD, SGD + Momentum, RMSProp, and Adam navigate the same loss landscape simultaneously. Live velocity vectors, per-optimizer stats, and adjustable learning rate & momentum sliders reveal why adaptive optimizers converge faster on ill-conditioned surfaces.
Watch a filter slide over a pixel-art image in real time. Adjust filter size, stride, and padding (zero or ones) and see how the feature map changes step by step. Kernel cells are colour-coded warm/cool by sign, and the output is normalised to full greyscale so patterns are always visible.
Four interactive sections covering the full RNN story: an animated unrolled forward pass with a 4-dimensional hidden state, a BPTT gradient-flow chart that makes vanishing and exploding gradients tangible, a live LSTM gate inspector with preset scenarios, and a side-by-side LSTM vs GRU formula comparison.
A sample end-to-end project showing how a final course project should be structured. Uses sequential data architectures (RNN, LSTM, GRU) for Human Activity Recognition, covering data loading, model definition, training, and evaluation.
Final Project
Starter template for the final ML course project. Provides the recommended repository structure, code organization, and documentation guidelines for students to build their machine learning pipeline.