Math for Data Science

Notebook Files

The notebook files may be downloaded as one zip file.

• Introduction
• The MNIST Dataset
• Averages and Vector Spaces
• Mean and Variance
• High Dimensions
• Vectors and Matrices
• Products
• Matrix Inverse
• Span and Linear Independence
• Zero Variance Directions
• Projections
• Basis
• Eigenvalue Decomposition
• Singular Value Decomposition
• Principal Component Analysis
• Cluster Analysis
• Single-Variable Calculus
• Entropy and Information
• Back Propagation
• Convexity
• Probability
• Binomial Probability
• Random Variables
• Normal Distribution
• Chi-squared Distribution
• Multinomial Probability
• Estimation
• Z-test
• T-test
• Chi-Squared Tests
• Neural Networks
• Gradient Descent
• Network Training
• Regression Examples
• Permutations and Combinations
• The Binomial Theorem
• The Exponential Function
• Two Dimensions
• Complex Numbers
• Integration
• Asymptotics and Convergence
• SQL