Open House: Mathematics and Modeling for Modern AI
The math behind AI gets skipped.
This program covers it.
Most professionals working in AI can use the tools. Fewer can explain what is actually happening underneath them. That is not a knock on anyone. It is just that most training moves fast and treats the math as optional background.
Over five half-days online, you will work through the linear algebra, calculus, probability, and optimization that drive modern AI systems, taught in a way that connects to the work you are already doing.
What you will work through:
Linear algebra and calculus for ML Vectors, matrices, gradients. Covered in the context of how models actually use them, not as abstract theory you will figure out later.
Probability and statistics Distributions, Bayes, inference. The building blocks that show up in every modern AI system whether you notice them or not.
Optimization and training Gradient descent, loss functions, and why your model converges or does not. Finally explained clearly.
Neural network architecture From perceptrons to transformers. The math that makes attention mechanisms work and why the architecture is designed the way it is.
The open house is a chance to meet the faculty, watch a sample lesson, see how the material connects to tools you already use, and ask whatever is on your mind before you decide anything.