Courses

Modules: Introduction, Abstract Data Type, Stack, List, Queue· Singly and doubly linked lists· Advanced Constructs, Sets, Reference Types, Generics· Recursion· Trees· Balanced Binary and B-Trees

Modules: Coordinates and colour· Pieces, values, and power· Covering and non-attacking pieces· Lines, slopes, and points· Paths, tours, and route counting· Strategy games

Modules: Optimization with linear programming: the main ideas· The simplex algorithm: mathematical details

Modules: Talking to Python: print, input, and basic types· Types as measurement scales, and your first function· Lists as vectors: numeric work with loops· Matrices as nested lists· Functions, type hints, and feature helpers· Grouping and aggregating without pandas

Modules: But what is a neural network? | Deep learning chapter 1· Gradient descent, how neural networks learn | Deep learning chapter 2· Backpropagation, intuitively | Deep learning chapter 3· Backpropagation calculus | Deep learning chapter 4· Large Language Models explained briefly | Deep learning chapter 5· Transformers, the tech behind LLMs | Deep learning chapter 6

Modules: Vectors | Chapter 1· Linear combinations, span, and basis vectors | Chapter 2· Linear transformations and matrices | Chapter 3· Matrix multiplication as composition | Chapter 4· Three-dimensional linear transformations | Chapter 5· The determinant | Chapter 6

Modules: Intro: Quantum Computing· Quantum Encoding

Modules: OOP Principles: Inheritance· OOP Case Study· Processing Different Data Formats· ORM, Decorators & Pandas· Recursion & structural decomposition· Asymptotics & empirical timing