Build the Foundations. Understand the Algorithms. Apply the Science.
This page brings together the core knowledge needed to understand and apply machine learning in a scientific context. From foundational math to essential algorithms and chemical applications, each section is designed to help researchers and students build real fluency, not just familiarity, with the tools shaping modern discovery.
Introduction to Machine Learning
Let's start with the core and foundations of machine learning and artificial intelligence.
To understand how machine learning and AI can transform scientific discovery, it’s essential to start with the fundamentals — from a general introduction to core concepts like supervised, unsupervised, and reinforcement learning. Each course is designed to help researchers understand how ML models work, particularly in the context of chemical research and its broader applications.
Intro to Machine Learning
This course introduces the core ideas behind machine learning — what it is, how it works, and why it matters. It lays the foundation for deeper topics like supervised, unsupervised, and reinforcement learning by focusing on concepts, structure, and scientific relevance.
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Supervised Learning
This course focuses on supervised learning - analyzing labeled data. Master core algorithms like linear regression, decision trees, & k-NN. Understand the mathematical intuition, as well as techniques for model evaluation such as accuracy, precision, and recall.
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Unsupervised Learning
Key topics include clustering, dimensionality reduction, association rules, and an introduction to neural networks. Students will learn to discover patterns and insights from unlabeled data, using algorithms like k-means, PCA, t-SNE, and more clustering methods.
Study the CourseMathematics for Machine Learning
Lay the mathematical groundwork for machine learning.
Mathematics forms the language of machine learning. This section offers intuitive, structured content on the core mathematical tools that underpin ML — from linear algebra and probability to calculus and optimization. These courses are ideal for researchers and students who want to strengthen their theoretical foundation and gain deeper insight into how models truly work.
MathML I: Basic Math for Machine Learning
This course builds the essential mathematical groundwork for understanding machine learning. Topics include functions, vectors, matrices, single-variable and multivariable calculus, and basic optimization — all taught with a focus on developing mathematical intuition for ML algorithms.
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MathML II: Intermediate Math for Machine Learning
MathML II deepens the mathematical framework with advanced linear algebra, spectral theory, multivariable optimization, and functional spaces. Learners will gain the tools needed to understand and implement foundational ML models like PCA, regression, and kernel methods.
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MathML III: Advanced Math for Machine Learning
MathML III covers the mathematical core of modern ML theory — including measure theory, functional analysis, stochastic processes, variational methods, and information geometry. This course prepares learners for probabilistic modeling, Bayesian inference, and deep mathematical research in machine learning.
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