[2025] 300+ Math Online Courses from World’s Top Universities

In this article, we’ve compiled over 300 online courses offered by the 60 best universities in the world for studying math in 2025.

We did so by combining popular university rankings to identify the best institutions, and then using the Class Central database to find all their math online courses.

If you’d like to jump to the course list, click here. If you’d like to know how we built the list, keep reading.

Methodology

Manoel built the list following the same data-driven approach used to build the list of computer science courses from the top CS universities.

First, he identified the leading world university rankings. Since we were specifically interested in math, he looked at their latest rankings of the best universities for studying math (or closest superset). For the 2025 update, Suparn used a similar methodology to find the top 60 universities offering online mathematics courses.

Here are the rankings he ended up using:

Then, we crawled and scraped each ranking.

Now that we had some data, we used Jupyter with Python to process it. We combined the two rankings into one by averaging the position of each university in each ranking. Then, we filtered out the universities that didn’t offer online courses, and limited the list to the top 60 institutions.

Top 10 universities:1 Harvard 2 MIT 3 Stanford 4 Oxford 5 Cambridge 6 UC Berkeley 7 Princeton 8 ETH Zurich 9 Caltech 10 NUS (Singapore) — Combined ranking: top 10 universities for studying mathematics in 2025

As you can see in the image above, I found that the top three math institutions are:

Finally, we used the Class Central database, with its 250K online courses, to find all the math courses offered by the universities in the ranking.

The end result is a list of more than 300 online courses offered by 60 best universities in the world for studying math in 2025.

While processing the data, I noticed something interesting: 59 of the top 60 universities offer online courses, a lot more than I would have guessed. The world’s top institutions are very prolific creators of online courses.

Stats

Enrollments range from 21 to over 13 million. There are 8 courses with over 1 million enrollments
Altogether, the courses in this list have over 52 million enrollments, with an average of over 296 thousand enrollments
222 courses are free and 98 are paid
291 courses are in English, 19 Chinese, 7 French, and 1 each in Spanish, German, Korean.
Together, they account for 2,118 reviews at Class Central, with an average of 28 reviews
Average Rating 4.51 out of 5.0
80 courses are beginner level, 57 are intermediate level, and 11 are advanced level.

More Courses

With over 300 courses to pick from, we hope you find something you like. But if these aren’t enough, check out Class Central’s catalog of 250K online courses or our thematic collections:

Subjects

The full list is split into subjects. Click on a subject below to go to the relevant section.

General Mathematics (45)

Fibonacci Numbers and the Golden Ratio from The Hong Kong University of Science and Technology ★★★★★(271)
How to Learn Math: For Students from Stanford University ★★★★☆(17)
Convex Optimization from Stanford University ★★★★★(8)
Mathematics for Engineers: The Capstone Course from The Hong Kong University of Science and Technology ★★★★★(5)
A-level Mathematics for Year 12 – Course 1: Algebraic Methods, Graphs and Applied Mathematics Methods from Imperial College London ★★★★★(2)
Mathematical Thinking in Computer Science from University of California, San Diego ★★★★★(2)
Engineering Mathematics from University of Washington ★★★★★(1)
Mathematics for Engineers from The Hong Kong University of Science and Technology ★★★★★(1)
Political Geometry – The Mathematics of Redistricting from Harvard University ★★★★★(1)
Essential Math for AI from Columbia University
A-level Further Mathematics for Year 12 – Course 1: Complex Numbers, Matrices, Roots of Polynomial Equations and Vectors from Imperial College London
Delivery Problem from University of California, San Diego
A-level Further Mathematics for Year 13 – Course 1: Differential Equations, Further Integration, Curve Sketching, Complex Numbers, the Vector Product and Further Matrices from Imperial College London
Cómo Aprender Matemáticas – Para Estudiantes from Stanford University
Further Mathematics Year 13 course 2: Applications of Differential Equations, Momentum, Work, Energy & Power, The Poisson Distribution, The Central Limit Theorem, Chi Squared Tests, Type I and II Errors from Imperial College London
Foundational Mathematics for AI from Johns Hopkins University
Introduction to Complexity Science from Nanyang Technological University
Introduction to optimization on smooth manifolds: first order methods from École Polytechnique Fédérale de Lausanne
Nonlinear Dynamics and Chaos from Cornell University
Mathematics of Big Data and Machine Learning, IAP 2020 from Massachusetts Institute of Technology
How Learning Ten Equations Can Improve Your Life – David Sumpter from University of Oxford
离散数学 from Shanghai Jiao Tong University
The Mathematics of Visual Illusions – Ian Stewart from University of Oxford
Math Boot Camp for Engineers from Massachusetts Institute of Technology
Wrinkling – Oxford Mathematics Research Seminar from University of Oxford
Analyse I (partie 4) : Limite d’une fonction, fonctions continues from École Polytechnique Fédérale de Lausanne
Analyse I (partie 5) : Fonctions continues et fonctions dérivables, la fonction dérivée from École Polytechnique Fédérale de Lausanne
The Potential for AI in Science and Mathematics from University of Oxford
Stanford Lecture – Pi and The Art of Computer Programming – 2019 from Stanford University
Analyse I (partie 6) : Etudes des fonctions, développements limités from École Polytechnique Fédérale de Lausanne
Prime Time – James Maynard from University of Oxford
Bach and the Cosmos from University of Oxford
Analyse I (partie 1) : Prélude, notions de base, les nombres réels from École Polytechnique Fédérale de Lausanne
Analyse I (partie 7) : Intégrales indéfinies et définies, intégration (chapitres choisis) from École Polytechnique Fédérale de Lausanne
The Number Mysteries – Marcus du Sautoy from University of Oxford
Analyse I (partie 2) : Introduction aux nombres complexes from École Polytechnique Fédérale de Lausanne
Analyse I (partie 3) : Suites de nombres réels I et II from École Polytechnique Fédérale de Lausanne
Introduction to Metric Spaces from Massachusetts Institute of Technology
Cascading Principles – Conrad Shawcross, Martin Bridson and James Sparks with Fatos Ustek from University of Oxford
Why Does Rudolph Have a Shiny Nose? – Chris Budd from University of Oxford
Blueprints – How Mathematics Shapes Creativity from University of Oxford
Théorie des Groupes (partie 1) – Une introduction à la théorie des catégories from École Polytechnique Fédérale de Lausanne
Productive Generalization – Timothy Gowers from University of Oxford
Euler’s Pioneering Equation from University of Oxford
The Travelling Santa Problem and Other Seasonal Challenges – Marcus du Sautoy from University of Oxford

Linear Algebra (32)

Matrix Algebra for Engineers from The Hong Kong University of Science and Technology ★★★★★(843)
Linear Algebra – Foundations to Frontiers from The University of Texas at Austin ★★★★☆(15)
Introduction to Linear Models and Matrix Algebra from Harvard University ★★★★☆(12)
Mathematics for Machine Learning: Linear Algebra from Imperial College London ★★★☆☆(9)
Linear Algebra from Massachusetts Institute of Technology ★★★★★(5)
Matrix Methods in Data Analysis, Signal Processing, and Machine Learning from Massachusetts Institute of Technology ★★★★★(3)
Algèbre Linéaire (Partie 1) from École Polytechnique Fédérale de Lausanne ★★★★★(2)
MIT – A 2020 Vision of Linear Algebra, Spring 2020 from Massachusetts Institute of Technology ★★★★★(2)
Algèbre Linéaire (Partie 2) from École Polytechnique Fédérale de Lausanne ★★★★★(1)
Algèbre Linéaire (Partie 3) from École Polytechnique Fédérale de Lausanne ★★★★★(1)
Linear Algebra III: Determinants and Eigenvalues from Georgia Institute of Technology ★★★★★(1)
Linear Algebra IV: Orthogonality & Symmetric Matrices and the SVD from Georgia Institute of Technology ★★★★★(1)
Applications of Linear Algebra from Georgia Institute of Technology ★★★★★(1)
Linear Algebra: Linear Systems and Matrix Equations from Johns Hopkins University
Linear Algebra II: Matrix Algebra from Georgia Institute of Technology
Linear Algebra: Matrix Algebra, Determinants, & Eigenvectors from Johns Hopkins University
Linear Algebra I: Linear Equations from Georgia Institute of Technology
Math for AI beginner part 1 Linear Algebra from Korea Advanced Institute of Science and Technology
Introduction to Linear Algebra from The University of Sydney
Linear Algebra: Orthogonality and Diagonalization from Johns Hopkins University
Advanced Linear Algebra: Foundations to Frontiers from The University of Texas at Austin
Engineering Mathematics: Linear Algebra and ODEs, Fall 2014 from Massachusetts Institute of Technology
Linear Algebra from Elementary to Advanced from Johns Hopkins University
Introductory Linear Algebra from Georgia Institute of Technology
Linear Algebra from Massachusetts Institute of Technology
Matrix Calculus for Machine Learning and Beyond from Massachusetts Institute of Technology
A Vision of Linear Algebra from Massachusetts Institute of Technology
Computational Science and Engineering I from Massachusetts Institute of Technology
Linear Algebra and NxN Systems from Massachusetts Institute of Technology
线性代数(1) from Tsinghua University
线性代数(2) from Tsinghua University
简明线性代数 from Tsinghua University

Differential Equations (12)

Differential Equations for Engineers from The Hong Kong University of Science and Technology ★★★★★(374)
Differential Equations Part I Basic Theory from Korea Advanced Institute of Science and Technology ★★★★★(2)
Differential Equations from Massachusetts Institute of Technology ★★★★★(2)
Differential Equations Part II Series Solutions from Korea Advanced Institute of Science and Technology
Differential Equations Part III Systems of Equations from Korea Advanced Institute of Science and Technology
Engineering Math: Differential Equations and Linear Algebra from Massachusetts Institute of Technology
Numerical Solution of Differential Equations – Oxford Mathematics 3rd Year Student Lecture from University of Oxford
Differential Equations from Massachusetts Institute of Technology
Differential Equations: Fourier Series and Partial Differential Equations from Massachusetts Institute of Technology
Introduction to Differential Equations from Massachusetts Institute of Technology
Differential Equations: 2×2 Systems from Massachusetts Institute of Technology
A-level Further Mathematics for Year 13 – Course 1: Differential Equations, Further Integration, Curve Sketching, Complex Numbers, the Vector Product and Further Matrices from Imperial College London

Statistics & Probability (54)

Intro to Statistics from Stanford University ★★★★☆(39)
Probability – The Science of Uncertainty and Data from Massachusetts Institute of Technology ★★★★★(34)
Statistical Learning with R from Stanford University ★★★★☆(28)
Fundamentals of Statistics from Massachusetts Institute of Technology ★★★★☆(10)
Data Analysis: Basic Probability and Statistics from Harvard University ★★★★☆(6)
Introduction to Probability and Data with R from Duke University ★★★★☆(6)
Statistical Inference and Modeling for High-throughput Experiments from Harvard University ★★★★★(4)
Computational Probability and Inference from Massachusetts Institute of Technology ★★★★★(3)
Introduction to Statistics from Stanford University ★★★★★(2)
Statistics: Unlocking the World of Data from University of Edinburgh ★★★★☆(2)
Introduction to Probability from Harvard University ★★★★★(1)
Introduction to Statistics & Data Analysis in Public Health from Imperial College London ★★★★★(1)
Summary Statistics in Public Health from Johns Hopkins University ★★★★★(1)
Hypothesis Testing in Public Health from Johns Hopkins University ★★★★★(1)
A Crash Course in Causality: Inferring Causal Effects from Observational Data from University of Pennsylvania ★★★★☆(1)
Introductory Statistics : Basic Ideas and Instruments for Statistical Inference from Seoul National University ★☆☆☆☆(1)
Statistics for Applications from Massachusetts Institute of Technology ★★★★★(1)
Causal Inference from Columbia University
Causal Inference 2 from Columbia University
Introductory Statistics : Analyzing Data Using Graphs and Statistics from Seoul National University
Survival Analysis in R for Public Health from Imperial College London
Statistics for Data Science Essentials from University of Pennsylvania
Logistic Regression in R for Public Health from Imperial College London
Introduction to Probability Management from Stanford University
Statistics Using Python from University of Wisconsin–Madison
Probability and Statistics I: A Gentle Introduction to Probability from Georgia Institute of Technology
Introductory Statistics : Sample Survey and Instruments for Statistical Inference from Seoul National University
Probability and Statistics III: A Gentle Introduction to Statistics from Georgia Institute of Technology
Inferential Statistics from Duke University
Statistics 110 – Probability from Harvard University
Mathematical understanding of uncertainty from Seoul National University
Probability and Statistics II: Random Variables – Great Expectations to Bell Curves from Georgia Institute of Technology
Probability and Statistics IV: Confidence Intervals and Hypothesis Tests from Georgia Institute of Technology
What are the Chances? Probability and Uncertainty in Statistics from Johns Hopkins University
Advanced Statistics for Data Science from Johns Hopkins University
Advanced Probability and Statistical Methods from Johns Hopkins University
Probabilistic Systems Analysis and Applied Probability from Massachusetts Institute of Technology
Statistics with Python from University of Michigan
Statistics with Python Using NumPy, Pandas, and SciPy from University of Michigan
Probabilistic Graphical Models from Stanford University
Probabilistic Systems Analysis and Applied Probability from Massachusetts Institute of Technology
Selected Topics on Discrete Choice from École Polytechnique Fédérale de Lausanne
Bayesian Networks 3 – Maximum Likelihood – Stanford CS221: AI (Autumn 2019) from Stanford University
Bayesian Networks 1 – Inference – Stanford CS221: AI from Stanford University
Bayesian Networks 2 – Forward-Backward – Stanford CS221: AI from Stanford University
Statistics, Confidence Intervals and Hypothesis Tests from Georgia Institute of Technology
MIT RES.6-012 Introduction to Probability, Spring 2018 from Massachusetts Institute of Technology
Data Overload – Making Sense of Statistics in the News, Kristin Sainani from Stanford University
Probability/Random Variables from Georgia Institute of Technology
Dependent Stopping Times and Application to Credit Risk Theory – BQE Lecture Series from New York University (NYU)
Inferenzstatistik from Duke University
The Counting Project – How Data Built the Modern World from University of Oxford
R을 사용한 확률 및 데이터 소개 from Duke University
Statistical Methods for Computer Science from Johns Hopkins University

Calculus (30)

Calculus: Single Variable Part 1 – Functions from University of Pennsylvania ★★★★★(8)
Introduction to Calculus from The University of Sydney ★★★★★(6)
Calculus: Single Variable Part 2 – Differentiation from University of Pennsylvania ★★★★★(5)
Calculus for Engineers from The Hong Kong University of Science and Technology ★★★★★(5)
Calculus: Single Variable Part 3 – Integration from University of Pennsylvania ★★★★☆(4)
Calculus: Single Variable Part 4 – Applications from University of Pennsylvania ★★★★★(3)
Single Variable Calculus from Massachusetts Institute of Technology ★★★★★(3)
Calculus Applied! from Harvard University ★★★★★(2)
Combinatorial Mathematics | 组合数学 from Tsinghua University ★★★★☆(2)
Introductory Calculus – Oxford Mathematics 1st Year Student Lecture from University of Oxford ★★★★★(2)
Calculus through Data & Modeling: Differentiation Rules from Johns Hopkins University ★★★★☆(1)
Single Variable Calculus from University of Pennsylvania
Applied Calculus with Python from Johns Hopkins University
Calculus through Data & Modeling: Precalculus Review from Johns Hopkins University
Applied Math for Materials Science and Engineering from Korea Advanced Institute of Science and Technology
Calculus through Data & Modeling: Limits & Derivatives from Johns Hopkins University
A-Level Further Mathematics for Year 12 – Course 2: 3 x 3 Matrices, Mathematical Induction, Calculus Methods and Applications, Maclaurin Series, Complex Numbers and Polar Coordinates from Imperial College London
Calculus through Data & Modelling: Series and Integration from Johns Hopkins University
Calculus through Data & Modelling: Techniques of Integration from Johns Hopkins University
Calculus through Data & Modelling: Integration Applications from Johns Hopkins University
Calculus through Data & Modeling: Applying Differentiation from Johns Hopkins University
Introduction to Advanced Calculus from The University of Sydney
Calculus 1C: Coordinate Systems & Infinite Series from Massachusetts Institute of Technology
Calculus 1A: Differentiation from Massachusetts Institute of Technology
Calculus 1B: Integration from Massachusetts Institute of Technology
微积分（先修课） from Tsinghua University
A-Level Further Mathematics for Year 12 – Course 2: 3 x 3 Matrices, Mathematical Induction, Calculus Methods and Applications, Maclaurin Series, Complex Numbers and Polar Coordinates from Imperial College London
微积分——极限理论与一元函数 from Tsinghua University
微积分-1 from Tsinghua University
微积分-2 from Tsinghua University

Mathematical Modeling (9)

Modelling Ice Sheets – Oxford Mathematics Research Seminar from University of Oxford ★★★★★(4)
Infectious Disease Modeling in Practice from Johns Hopkins University
Scaling the Maths of Life from University of Oxford
How Do Mathematicians Model Infectious Disease Outbreaks? from University of Oxford
Mathematical Modelling in Biology – Neuronal Signalling from University of Oxford
Mathematical Modelling in Biology – Enzyme Kinetics and Perturbation Theory – Lecture 2 from University of Oxford
Responsible Modelling and the Ethics of Mathematics for Decision Support from University of Oxford
3 Minute Thesis Competition 2025 – Oxford Mathematics Postgraduate Research Presentations from University of Oxford
Going for Gold – The Mathematics of Sporting Glory from University of Oxford

Convex Optimization (17)

Convex Optimization I – Lecture 1 from Stanford University
Convex Optimization I – Lecture 3 from Stanford University
Convex Optimization I – Lecture 2 from Stanford University
Convex Optimization I – Lecture 8 from Stanford University
Convex Optimization I – Lecture 5 from Stanford University
Convex Optimization I – Lecture 4 from Stanford University
Convex Optimization I – Lecture 6 from Stanford University
Convex Optimization I – Lecture 7 from Stanford University
Convex Optimization I – Lecture 13 from Stanford University
Convex Optimization I – Lecture 16 from Stanford University
Convex Optimization I – Lecture 15 from Stanford University
Convex Optimization I – Lecture 10 from Stanford University
Convex Optimization I – Lecture 9 from Stanford University
Convex Optimization I – Lecture 12 from Stanford University
Convex Optimization I – Lecture 11 from Stanford University
Convex Optimization I – Lecture 17 from Stanford University
最优化理论与方法 from Nanjing University

Probability Theory (8)

概率论与数理统计 from Tsinghua University
Introduction to Probability from Massachusetts Institute of Technology
How to Put Uncertainty into Numbers – Understanding Chance, Luck and Ignorance from University of Oxford
Can We Truly Understand by Counting? Understanding Complex Physical Systems Through Mathematics from University of Oxford
Probability, Measure and Martingales – Let There Be Time: Filtrations and Stopping Times from University of Oxford
Martingales: Definition and First Properties – Probability, Measure and Martingales from University of Oxford
Probability, Measure and Martingales – Stopped Martingales and Optional Sampling Theorems from University of Oxford
Vitali’s Convergence Theorem and Martingale Inequalities – Lecture 5 from University of Oxford

Others (113)

Vector Calculus for Engineers from The Hong Kong University of Science and Technology ★★★★★(261)
Introduction to Mathematical Thinking from Stanford University ★★★★☆(51)
Bayesian Statistics from Duke University ★★★☆☆(12)
Discovery Precalculus: A Creative and Connected Approach from The University of Texas at Austin ★★★★★(3)
Combinatorics and Probability from University of California, San Diego ★★★★☆(3)
Analytic Combinatorics from Princeton University ★★★★☆(3)
Data Science: Linear Regression from Harvard University ★★☆☆☆(3)
Engineering Calculus and Differential Equations from The University of Hong Kong ★★★★★(2)
Multivariable Calculus from Massachusetts Institute of Technology ★★★★★(2)
Introduction to Graph Theory from University of California, San Diego ★★★★★(2)
Linear Regression and Modeling from Duke University ★★★★☆(2)
Linear Regression in R for Public Health from Imperial College London ★★★★★(1)
Simple Regression Analysis in Public Health from Johns Hopkins University ★★★★★(1)
Precalculus from Modern States ★★★★★(1)
Algebra: Elementary to Advanced – Functions & Applications from Johns Hopkins University ★★★★☆(1)
Algebra: Elementary to Advanced from Johns Hopkins University ★★★★★(1)
Cours préparatoire: Fonctions Trigonométriques, Logarithmiques et Exponentielles from École Polytechnique Fédérale de Lausanne ★★★★☆(1)
The Banach Contraction Mapping Theorem – Oxford Mathematics 1st Year Lecture from University of Oxford ★★★★☆(1)
The Seduction of Curves – The Lines of Beauty That Connect Mathematics, Art and The Nude from University of Oxford ★★★★★(1)
Hamiltonian Paths in Antiquity – Stanford Lecture 2016 from Stanford University ★★★★★(1)
Precalculus: Relations and Functions from Johns Hopkins University
Precalculus: Mathematical Modeling from Johns Hopkins University
Precalculus: Periodic Functions from Johns Hopkins University
Precalculus through Data and Modelling from Johns Hopkins University
Combinatorics and Algorithms Design from Tsinghua University
组合数学 from Tsinghua University
Linear Regression Modeling for Health Data from University of Michigan
Differential Calculus through Data and Modeling from Johns Hopkins University
Differential Calculus from Massachusetts Institute of Technology
Graph Theory and Additive Combinatorics from Massachusetts Institute of Technology
Graph Theory and Additive Combinatorics from Massachusetts Institute of Technology
Petri网：模型、理论与应用 from Tsinghua University
Multivariable Calculus from Massachusetts Institute of Technology
Multivariable Calculus 1: Vectors and Derivatives from Massachusetts Institute of Technology
Multidimensional Analysis and Geometry – Introduction to the Derivative in Higher Dimensions – Lecture 1 from University of Oxford
Multivariable Calculus 3: Theorems and Applications from Massachusetts Institute of Technology
Multivariable Calculus 2: Integrals from Massachusetts Institute of Technology
微积分——多元函数与重积分 from Tsinghua University
多元微积分（先修课） from University of Science and Technology of China
Using Language Models to Understand Wage Premia from Stanford University
Algebra: Elementary to Advanced – Equations & Inequalities from Johns Hopkins University
Algebra: Elementary to Advanced – Polynomials and Roots from Johns Hopkins University
Honors Algebra 2: Linear and Quadratic Functions from Johns Hopkins University
History of Mathematics – Classical Algebra – 19th-Century Beginnings of Modern Algebra – 3rd Year Lecture from University of Oxford
History of Mathematics – Classical Algebra: Equation Solving 1800 BC – AD 1800 – 3rd Year Lecture from University of Oxford
Honors Algebra 2: Algebraic, Exponential & Log Functions from Johns Hopkins University
Honors Algebra 2 from Johns Hopkins University
Honors Algebra 2 from Johns Hopkins University
Honors Algebra 2: Series, Trigonometry, and Probability from Johns Hopkins University
Geometry – Scalar Triple Product: Oxford Mathematics First Year Student Lecture from University of Oxford
Introduction to Conic Sections and Their Mathematical Properties – Lecture 1 from University of Oxford
Natural Tilings: From Hard Rock to Soft Cells from University of Oxford
Calculus through Data & Modelling: Vector Calculus from Johns Hopkins University
Math for AI Beginner Part 2 : Vector Calulus from Korea Advanced Institute of Science and Technology
Multidimensional Analysis and Geometry: Introduction to the Derivative in Higher Dimensions – Lecture 2 from University of Oxford
Real Analysis from Massachusetts Institute of Technology
Real Analysis from Massachusetts Institute of Technology
Discrete Mathematics from Shanghai Jiao Tong University
离散数学概论 Discrete Mathematics Generality from Peking University
Mathematics for Computer Science from Massachusetts Institute of Technology
Data Science Decisions in Time: Using Causal Information from Johns Hopkins University
教育定量研究方法（高级） from Tsinghua University
Optimization: principles and algorithms – Linear optimization from École Polytechnique Fédérale de Lausanne
运筹学 from Tsinghua University
Meaningful Predictive Modeling from University of California, San Diego
Foundations of Probability and Random Variables from Johns Hopkins University
Stanford CS109 – Future of Probability – Lecture 28 from Stanford University
Probability, Measure and Martingales: An Introduction from University of Oxford
From Coin Flips to Clinical Trials – Introduction to Probability – Medical Software Course from Yale University
Introduction to Uncertainty Quantification from Johns Hopkins University
Random Processes from Johns Hopkins University
Algebraic Curves – Curves in Affine Plane and the Projective Plane – Lecture 3 from University of Oxford
Algebraic Curves – More on Projective Geometry – Lecture 2 from University of Oxford
Algebraic Curves – Properties of Algebraic Curves – Lecture 4 from University of Oxford
Numerical Methods Applied to Chemical Engineering from Massachusetts Institute of Technology
The Butterfly Effect – What Does It Really Signify? from University of Oxford
Integral Calculus through Data and Modeling from Johns Hopkins University
Foundations of Logic from Tsinghua University
Paradox and Infinity from Massachusetts Institute of Technology
Single Variable Calculus from Massachusetts Institute of Technology
Introduction to Functional Analysis from Massachusetts Institute of Technology
Groups and Group Actions – Representations of Groups by Permutations from University of Oxford
Théorie des Groupes (partie 2) – Quotients de groupe from École Polytechnique Fédérale de Lausanne
Théorie des Groupes (partie 3) – Actions de groupe from École Polytechnique Fédérale de Lausanne
Théorie des Groupes (partie 4) – Groupes abéliens et sous-groupes de Sylow from École Polytechnique Fédérale de Lausanne
Groups and Group Actions – Group Homomorphisms from University of Oxford
How to Be a Statistical Detective from Stanford University
Principles of Digital Communication II from Massachusetts Institute of Technology
Knotty Problems – Marc Lackenby from University of Oxford
Can Yule Solve My Problems – Alex Bellos from University of Oxford
Analysis III: Basic Properties of Riemann Integration – Lecture 1 from University of Oxford
Mathematical Methods for Engineers II from Massachusetts Institute of Technology
Why There Are No Three-Headed Monsters from University of Oxford
From Ronald Ross to ChatGPT: The Birth and Strange Life of the Random Walk from University of Oxford
Introduction to Complex Numbers – Lecture 1 from University of Oxford
A-level Further Mathematics for Year 12 – Course 1: Complex Numbers, Matrices, Roots of Polynomial Equations and Vectors from Imperial College London
Geometry: Conics – Lecture 2 from University of Oxford
Numbers Are Serious but They Are Also Fun – Michael Atiyah from University of Oxford
Linear Diophantine Equations and the Extended Euclidean Algorithm – Lecture 1 from University of Oxford
1 + 1 = 2 – The Foundation of Mathematical Counting and Number Systems from University of Oxford
Complex Analysis: Multivalued Functions and Integration – Lecture 4 from University of Oxford
Algebraic Topology – Algebraic Invariants of Spaces from University of Oxford
Algebraic Topology – Chains, Cycles, and Homology Classes from University of Oxford
Honors Algebra 2: Polynomials and Complex Numbers from Johns Hopkins University
Transfer Functions and the Laplace Transform from Massachusetts Institute of Technology
The Taylor Series, Complex Numbers, and Simple Harmonic Motion – Lecture 16 from Yale University
Social Sequence Analysis: An Introduction and Overview from The University of Chicago
数值分析与算法 from Tsinghua University
Lie Groups – Introduction to Lie Groups from University of Oxford
Lie Groups – The Exceptional Lie Group G2 from University of Oxford
Algebraic Curves – Introduction to Projective Geometry – Lecture 1 from University of Oxford
Commutative Algebra – Primary Decomposition from University of Oxford
Commutative Algebra – Primary Decomposition 2 from University of Oxford

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