Math Academy

After briefly looking at some essential set theory, logic, and vector geometry, students explore matrices in-depth. They will study Gaussian elimination, solve systems of equations, learn about determinants and their properties, and compute inverse matrices.

As part of this course, students perform a deep dive into vector spaces, exploring linear independence, subspaces, bases, dimension, rank, and nullity. Students will generalize key concepts to abstract vector spaces and inner product spaces. Various aspects of orthogonality in vector spaces are considered, including orthogonal sets, complements, orthogonal matrices, orthogonal projections, and the Gram-Schmidt process.

Students will learn how to find the eigenvectors of a matrix, compute a matrix diagonalization, and extend this understanding to symmetric matrices.

In addition, this course discusses various linear algebra applications relevant to machine learning, such as singular value decomposition, linear least-squares, regression, and principal component analysis.

A solid grasp of some key multivariable calculus concepts is needed to understand fundamental machine learning algorithms successfully. In this course, students will become well-versed in partial derivatives, the multivariable chain rule (essential for backpropagation), vector-valued functions, and gradient vectors (for gradient descent). The remainder of the multivariable calculus discusses double integrals, a crucial tool for fully grasping continuous probability distributions and related concepts.

On the probability and statistics side, students will unravel discrete and continuous random variables. They will familiarize themselves with probability density functions, random variable transformations, expectation, moments, and variance. Some important discrete and continuous probability distributions will be discussed in detail.

Students then extend their knowledge of random variables to include joint, marginal, and conditional probability distributions, sums and products of random variables, conditional expectations, and variances. Special attention will be given to combinations of normally distributed random variables and the bivariate normal distribution.

The statistics part of the course concludes with an in-depth study of parametric inference, exploring point estimation, maximum likelihood estimation, and hypothesis testing. Students will also learn to construct confidence intervals for various parameters, including means, proportions, variances, and regression coefficients.

Preliminaries

24 topics

1.1. Sets

	1.1.1.	Special Sets
	1.1.2.	Set-Builder Notation
	1.1.3.	Equivalent Sets
	1.1.4.	Cardinality of Finite Sets
	1.1.5.	Subsets
	1.1.6.	The Complement of a Set
	1.1.7.	The Difference of Sets
	1.1.8.	The Cartesian Product
	1.1.9.	Sets and Functions
	1.1.10.	Interior and Boundary Points
	1.1.11.	Interiors and Boundaries of Sets
	1.1.12.	Open and Closed Sets

1.2. Logical Quantifiers

	1.2.1.	Mathematical Statements
	1.2.2.	Universal and Existential Quantifiers
	1.2.3.	Formal and Informal Language

1.3. Vector Geometry

	1.3.1.	The Vector Equation of a Line
	1.3.2.	The Parametric Equations of a Line
	1.3.3.	The Cartesian Equation of a Line
	1.3.4.	The Vector Equation of a Plane
	1.3.5.	The Cartesian Equation of a Plane
	1.3.6.	The Parametric Equations of a Plane
	1.3.7.	The Intersection of Two Planes

1.4. The Hyperbolic Functions

	1.4.1.	The Hyperbolic Functions
	1.4.2.	Graphs of the Hyperbolic Functions

Matrices

21 topics

2.5. Determinants

	2.5.1.	The Determinant of an NxN Matrix
	2.5.2.	Finding Determinants Using Laplace Expansions
	2.5.3.	Basic Properties of Determinants
	2.5.4.	Further Properties of Determinants

2.6. Gaussian Elimination

	2.6.1.	Systems of Equations as Augmented Matrices
	2.6.2.	Row Echelon Form
	2.6.3.	Solving Systems of Equations Using Back Substitution
	2.6.4.	Elementary Row Operations
	2.6.5.	Creating Rows or Columns Containing Zeros Using Gaussian Elimination
	2.6.6.	Solving 2x2 Systems of Equations Using Gaussian Elimination
	2.6.7.	Solving 2x2 Singular Systems of Equations Using Gaussian Elimination
	2.6.8.	Solving 3x3 Systems of Equations Using Gaussian Elimination
	2.6.9.	Identifying the Pivot Columns of a Matrix
	2.6.10.	Solving 3x3 Singular Systems of Equations Using Gaussian Elimination
	2.6.11.	Reduced Row Echelon Form
	2.6.12.	Gaussian Elimination For NxM Systems of Equations

2.7. The Inverse of a Matrix

	2.7.1.	Finding the Inverse of a 2x2 Matrix Using Row Operations
	2.7.2.	Finding the Inverse of a 3x3 Matrix Using Row Operations
	2.7.3.	Matrices With Easy-to-Find Inverses
	2.7.4.	The Invertible Matrix Theorem in Terms of 2x2 Systems of Equations
	2.7.5.	Triangular Matrices

Vector Spaces

20 topics

3.8. Vectors in N-Dimensional Space

	3.8.1.	Vectors in N-Dimensional Euclidean Space
	3.8.2.	Linear Combinations of Vectors in N-Dimensional Euclidean Space
	3.8.3.	Linear Span of Vectors in N-Dimensional Euclidean Space
	3.8.4.	Linear Dependence and Independence

3.9. Subspaces of N-Dimensional Space

	3.9.1.	Subspaces of N-Dimensional Space
	3.9.2.	Subspaces of N-Dimensional Space: Geometric Interpretation
	3.9.3.	The Column Space of a Matrix
	3.9.4.	The Null Space of a Matrix

3.10. Bases of N-Dimensional Space

	3.10.1.	Finding a Basis of a Span
	3.10.2.	Finding a Basis of the Column Space of a Matrix
	3.10.3.	Finding a Basis of the Null Space of a Matrix
	3.10.4.	Expressing the Coordinates of a Vector in a Given Basis
	3.10.5.	Writing Vectors in Different Bases
	3.10.6.	The Change-of-Coordinates Matrix
	3.10.7.	Changing a Basis Using the Change-of-Coordinates Matrix

3.11. Dimension and Rank in N-Dimensional Space

	3.11.1.	The Dimension of a Span
	3.11.2.	The Rank of a Matrix
	3.11.3.	The Dimension of the Null Space of a Matrix
	3.11.4.	The Invertible Matrix Theorem in Terms of Dimension, Rank and Nullity
	3.11.5.	The Rank-Nullity Theorem

Diagonalization of Matrices

12 topics

4.12. Eigenvectors and Eigenvalues

	4.12.1.	The Eigenvalues and Eigenvectors of a 2x2 Matrix
	4.12.2.	Calculating the Eigenvalues of a 2x2 Matrix
	4.12.3.	Calculating the Eigenvectors of a 2x2 Matrix
	4.12.4.	The Characteristic Equation of a Matrix
	4.12.5.	Calculating the Eigenvectors of a 3x3 Matrix With Distinct Eigenvalues
	4.12.6.	Calculating the Eigenvectors of a 3x3 Matrix in the General Case

4.13. Diagonalization

	4.13.1.	Diagonalizing a 2x2 Matrix
	4.13.2.	Diagonalizing a 3x3 Matrix With Distinct Eigenvalues
	4.13.3.	Diagonalizing a 3x3 Matrix in the General Case
	4.13.4.	Symmetric Matrices
	4.13.5.	Diagonalization of 2x2 Symmetric Matrices
	4.13.6.	Diagonalization of 3x3 Symmetric Matrices

Orthogonality & Projections

17 topics

5.14. Inner Products

	5.14.1.	The Dot Product in N-Dimensional Euclidean Space
	5.14.2.	The Norm of a Vector in N-Dimensional Euclidean Space
	5.14.3.	Introduction to Abstract Vector Spaces
	5.14.4.	Defining Abstract Vector Spaces
	5.14.5.	Inner Product Spaces

5.15. Orthogonality

	5.15.1.	Orthogonal Vectors in Euclidean Spaces
	5.15.2.	The Cauchy-Schwarz Inequality and the Angle Between Two Vectors
	5.15.3.	Orthogonal Complements
	5.15.4.	Orthogonal Sets in Euclidean Spaces
	5.15.5.	Orthogonal Matrices
	5.15.6.	Orthogonal Linear Transformations

5.16. Orthogonal Projections

	5.16.1.	Projecting Vectors Onto One-Dimensional Subspaces
	5.16.2.	The Components of a Vector with Respect to an Orthogonal or Orthonormal Basis
	5.16.3.	Projecting Vectors Onto Subspaces in Euclidean Spaces (Orthogonal Bases)
	5.16.4.	Projecting Vectors Onto Subspaces in Euclidean Spaces (Arbitrary Bases)
	5.16.5.	Projecting Vectors Onto Subspaces in Euclidean Spaces (Arbitrary Bases): Applications
	5.16.6.	The Gram-Schmidt Process for Two Vectors

Singular Value Decomposition

12 topics

6.17. Quadratic Forms

	6.17.1.	Bilinear Forms
	6.17.2.	Quadratic Forms
	6.17.3.	Change of Variables in Quadratic Forms
	6.17.4.	Positive-Definite and Negative-Definite Quadratic Forms
	6.17.5.	Constrained Optimization of Quadratic Forms
	6.17.6.	Constrained Optimization of Quadratic Forms: Determining Where Extrema are Attained

6.18. Singular Value Decomposition

	6.18.1.	The Singular Values of a Matrix
	6.18.2.	Computing the Singular Values of a Matrix
	6.18.3.	Singular Value Decomposition of 2x2 Matrices
	6.18.4.	Singular Value Decomposition of 2x2 Matrices With Zero or Repeated Eigenvalues
	6.18.5.	Singular Value Decomposition of Larger Matrices
	6.18.6.	Singular Value Decomposition and the Pseudoinverse Matrix

Applications of Linear Algebra

8 topics

7.19. Principal Component Analysis

	7.19.1.	Introduction to Principal Component Analysis
	7.19.2.	Computing Principal Components
	7.19.3.	The Connection Between PCA and SVD

7.20. Linear Least-Squares Problems

	7.20.1.	The Least-Squares Solution of a Linear System (Without Collinearity)
	7.20.2.	The Least-Squares Solution of a Linear System (With Collinearity)

7.21. Linear Regression

	7.21.1.	Linear Regression
	7.21.2.	Polynomial Regression
	7.21.3.	Multiple Linear Regression

Multivariable Calculus

27 topics

8.22. Partial Derivatives

	8.22.1.	The Domain of a Multivariable Function
	8.22.2.	Level Curves
	8.22.3.	Limits and Continuity of Multivariable Functions
	8.22.4.	Introduction to Partial Derivatives
	8.22.5.	Computing Partial Derivatives Using the Rules of Differentiation
	8.22.6.	Geometric Interpretations of Partial Derivatives
	8.22.7.	Partial Differentiability of Multivariable Functions
	8.22.8.	Higher-Order Partial Derivatives
	8.22.9.	Equality of Mixed Partial Derivatives
	8.22.10.	The Multivariable Chain Rule

8.23. Vector-Valued Functions

	8.23.1.	The Domain of a Vector-Valued Function
	8.23.2.	The Gradient Vector
	8.23.3.	Directional Derivatives
	8.23.4.	The Multivariable Chain Rule in Vector Form

8.24. Approximating Volumes With Riemann Sums

	8.24.1.	Partitions of Intervals
	8.24.2.	Calculating Double Summations Over Partitions
	8.24.3.	Approximating Volumes Using Lower Riemann Sums
	8.24.4.	Approximating Volumes Using Upper Riemann Sums
	8.24.5.	Lower Riemann Sums Over General Rectangular Partitions
	8.24.6.	Upper Riemann Sums Over General Rectangular Partitions
	8.24.7.	Defining Double Integrals Using Lower and Upper Riemann Sums

8.25. Double Integrals

	8.25.1.	Double Integrals Over Rectangular Domains
	8.25.2.	Double Integrals Over Non-Rectangular Domains
	8.25.3.	Properties of Double Integrals
	8.25.4.	Type I and II Regions in Two-Dimensional Space
	8.25.5.	Double Integrals Over Type I Regions
	8.25.6.	Double Integrals Over Type II Regions

Probability & Random Variables

37 topics

9.26. Probability

	9.26.1.	The Law of Total Probability (Extended)
	9.26.2.	Bayes' Theorem
	9.26.3.	Extending Bayes' Theorem

9.27. Random Variables

	9.27.1.	Probability Density Functions of Continuous Random Variables
	9.27.2.	Calculating Probabilities With Continuous Random Variables
	9.27.3.	Continuous Random Variables Over Infinite Domains
	9.27.4.	Cumulative Distribution Functions for Continuous Random Variables
	9.27.5.	Approximating Discrete Random Variables as Continuous
	9.27.6.	Simulating Random Observations

9.28. Transformations of Random Variables

	9.28.1.	One-to-One Transformations of Discrete Random Variables
	9.28.2.	Many-to-One Transformations of Discrete Random Variables
	9.28.3.	The Distribution Function Method
	9.28.4.	The Change-of-Variables Method for Continuous Random Variables
	9.28.5.	The Distribution Function Method With Many-to-One Transformations

9.29. Expectation

	9.29.1.	Expected Values of Discrete Random Variables
	9.29.2.	Properties of Expectation for Discrete Random Variables
	9.29.3.	Moments of Discrete Random Variables
	9.29.4.	Variance of Discrete Random Variables
	9.29.5.	Properties of Variance for Discrete Random Variables
	9.29.6.	Expected Values of Continuous Random Variables
	9.29.7.	Moments of Continuous Random Variables
	9.29.8.	Variance of Continuous Random Variables
	9.29.9.	The Rule of the Lazy Statistician

9.30. Discrete Probability Distributions

	9.30.1.	The Bernoulli Distribution
	9.30.2.	Mean and Variance of the Binomial Distribution
	9.30.3.	The Discrete Uniform Distribution
	9.30.4.	Modeling With Discrete Uniform Distributions
	9.30.5.	Mean and Variance of Discrete Uniform Distributions
	9.30.6.	The Poisson Distribution
	9.30.7.	Modeling With the Poisson Distribution

9.31. Continuous Probability Distributions

	9.31.1.	The Continuous Uniform Distribution
	9.31.2.	Mean and Variance of Continuous Uniform Distributions
	9.31.3.	Modeling With Continuous Uniform Distributions
	9.31.4.	The Gamma Function
	9.31.5.	The Chi-Square Distribution
	9.31.6.	The Student's T-Distribution
	9.31.7.	The Exponential Distribution

10.

Combining Random Variables

29 topics

10.32. Distributions of Two Discrete Random Variables

	10.32.1.	Double Summations
	10.32.2.	Joint Distributions for Discrete Random Variables
	10.32.3.	Marginal Distributions for Discrete Random Variables
	10.32.4.	Independence of Discrete Random Variables
	10.32.5.	Conditional Distributions for Discrete Random Variables
	10.32.6.	The Joint CDF of Two Discrete Random Variables

10.33. Distributions of Two Continuous Random Variables

	10.33.1.	Joint Distributions for Continuous Random Variables
	10.33.2.	Marginal Distributions for Continuous Random Variables
	10.33.3.	Independence of Continuous Random Variables
	10.33.4.	Conditional Distributions for Continuous Random Variables
	10.33.5.	The Joint CDF of Two Continuous Random Variables
	10.33.6.	Properties of the Joint CDF of Two Continuous Random Variables

10.34. Expectation for Joint Distributions

	10.34.1.	Expected Values of Sums and Products of Random Variables
	10.34.2.	Variance of Sums of Independent Random Variables
	10.34.3.	Computing Expected Values From Joint Distributions
	10.34.4.	Conditional Expectation for Discrete Random Variables
	10.34.5.	Conditional Variance for Discrete Random Variables
	10.34.6.	Conditional Expectation for Continuous Random Variables
	10.34.7.	Conditional Variance for Continuous Random Variables
	10.34.8.	The Rule of the Lazy Statistician for Two Random Variables

10.35. Covariance of Random Variables

	10.35.1.	The Covariance of Two Random Variables
	10.35.2.	Variance of Sums of Random Variables
	10.35.3.	The Correlation Coefficient for Two Random Variables
	10.35.4.	The Covariance Matrix

10.36. Normally Distributed Random Variables

	10.36.1.	Normal Approximations of Binomial Distributions
	10.36.2.	Combining Two Normally Distributed Random Variables
	10.36.3.	Combining Multiple Normally Distributed Random Variables
	10.36.4.	I.I.D Normal Random Variables
	10.36.5.	The Bivariate Normal Distribution

11.

Parametric Inference

29 topics

11.37. Point Estimation

	11.37.1.	The Sample Mean
	11.37.2.	Statistics and Sampling Distributions
	11.37.3.	Variance of Sample Means
	11.37.4.	The Sample Variance
	11.37.5.	Sample Means From Normal Populations
	11.37.6.	The Central Limit Theorem
	11.37.7.	Sampling Proportions From Finite Populations
	11.37.8.	Point Estimates of Population Proportions
	11.37.9.	The Sample Covariance Matrix

11.38. Maximum Likelihood

	11.38.1.	Product Notation
	11.38.2.	Logarithmic Differentiation
	11.38.3.	Likelihood Functions for Discrete Probability Distributions
	11.38.4.	Log-Likelihood Functions for Discrete Probability Distributions
	11.38.5.	Likelihood Functions for Continuous Probability Distributions
	11.38.6.	Log-Likelihood Functions for Continuous Probability Distributions
	11.38.7.	Maximum Likelihood Estimation

11.39. Hypothesis Testing

	11.39.1.	One-Tailed Hypothesis Tests
	11.39.2.	Two-Tailed Hypothesis Tests
	11.39.3.	Type I and Type II Errors in Hypothesis Testing
	11.39.4.	Hypothesis Tests for One Mean: Known Population Variance
	11.39.5.	Hypothesis Tests for One Mean: Unknown Population Variance
	11.39.6.	Hypothesis Tests for Two Means: Known Population Variances

11.40. Confidence Intervals

	11.40.1.	Confidence Intervals for One Mean: Known Population Variance
	11.40.2.	Confidence Intervals for One Mean: Unknown Population Variance
	11.40.3.	Confidence Intervals for Proportions
	11.40.4.	Confidence Intervals for Two Means: Known Population Variances
	11.40.5.	Confidence Intervals for Variances
	11.40.6.	Confidence Intervals for Slope Parameters in Linear Regression
	11.40.7.	Confidence Intervals for Intercept Parameters in Linear Regression

Mathematics for Machine Learning

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