Education & interview prep
for the ML & AI era

Learn the fundamentals, then prove you know them. Structured courses and hands-on interview problems — purpose-built for ML engineers, AI researchers, data scientists, quants, and more.

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Beyond static content

Real understanding takes more
than reading and memorizing.

ML and AI span theory, code, math, and design — and interviews test all of it. Lycaeum pairs structured learning with hands-on practice across every format, so you build real understanding and can prove it when it counts.

Coding

Algorithms, debugging, and optimization — all with auto-graded test cases.

Math & Derivations

LaTeX-rendered derivations, proofs, and stochastic calculus. The pen-and-paper problems that actually show up.

Probability & Statistics

Distributions, inference, hypothesis testing, and Bayesian reasoning.

System Design

Architect ML systems on an interactive canvas — recommendation engines, feature stores, serving infrastructure.

Paper Reading

Annotated walkthroughs of foundational papers with guided questions. Understand what you cite.

Case Study & More

Applied scenarios, ML pipelines, SQL, estimation, and conceptual knowledge checks.

Courses

A structured learning path from fundamentals to advanced topics

Introduction to Machine Learning and AI — Part 1

Apprentice
Foundations

The paradigms and approaches of ML and AI: supervised, unsupervised, and reinforcement learning, plus classical ML and deep learning.

Introduction to Machine Learning and AI — Part 2

Apprentice
Foundations

Frontier systems (LLMs, generative AI, agents), application domains (vision, NLP, robotics), and the path to AGI.

The ML Lifecycle

Apprentice
Foundations

Train/test splits, overfitting, model evaluation metrics, and the bias-variance tradeoff.

K-Means

Scholar
Unsupervised Learning

K-Means clustering and K-Means++ initialization. Learn to group data without labels.

More courses coming soon.