Probability, Statistics, and Linear Algebra for AI

25 lessons in AI Fundamentals

• Extracting an Eigenvector from np.linalg.eigCode Quiz
• Data as Vectors and MatricesSlides / Video
• Gradients and Partial DerivativesSlides / Video
• Singular Value Decomposition BasicsSlides / Video
• Matrix Transpose and InverseSlides / Video
• Linear Independence, Span, and BasisSlides / Video
• Vector Norms and Distance MetricsSlides / Video
• Dot Product and Vector SimilaritySlides / Video
• Matrix Multiplication in AISlides / Video
• Matrices and Matrix OperationsSlides / Video
• Vectors and Vector OperationsSlides / Video
• Confidence IntervalsSlides / Video
• Hypothesis Testing and p-valuesSlides / Video
• Sampling and the Central Limit TheoremSlides / Video
• Descriptive Statistics: Center and SpreadSlides / Video
• Maximum Likelihood EstimationSlides / Video
• Joint, Marginal, Conditional DistributionsSlides / Video
• Covariance and CorrelationSlides / Video
• Expectation, Variance, Standard DeviationSlides / Video
• Bayes' Theorem in AISlides / Video
• Conditional Probability and IndependenceSlides / Video
• Gaussian, Bernoulli, Binomial DistributionsSlides / Video
• Discrete vs Continuous DistributionsSlides / Video
• Random Variables and DistributionsSlides / Video
• Eigenvalues and Eigenvectors BasicsSlides / Video