Matrices and vectors
Addition and scalar Multiplication
Mean, Standard Deviation, Median
Matrix vector multiplication
Matrix Multiplication and Properties
Inverse and Transpose
Mathematical Representation of a line (2D), plane(3D) and hyperplane (n*D)
Hyper planes and Hyper spaces
Geometric Representation of a circle (2D), sphere (3D) and hypersphere (n*D)
Equation of an ellipse (2D), ellipsoid (3D) and hyper ellipsoid (n*D)
Correlation, Coefficient intuition
Length and Dot Products
Introduction to probability
Bayesian Interpretation & Bayes Rules and Bayes Theorem
Analyze Dataset for Distributions
Refining the Qualitative and Quantitative Data.
Variation in Datasets (Univariate, Bivariate and Multivariate Data)
Population & Sample.
Gaussian/Normal Distribution and its PDF (Probability Density Function).
CDF (Cumulative Density Function) of Gaussian/Normal Distribution
Symmetric distribution, Skewness, and Kurtosis
Standard normal variate (z) and standardization.
Kernel density estimation.
Law of large Number?
Joint and Disjoint Outcomes
Sample Space and Complements
Permutation and combination
Markov Decision Process
Discrete Sample Space (Finite and Infinite)
Joint Probability and Conditional Probability
General Multiplication Rule
Laws of Total Probability
Correlation and causation
Discrete and Continuous Uniform distributions.
Bernoulli and Binomial distribution