Introduction to Statistical Methods

17 lectures

Probability, estimation, and inference that underpin every quantitative ML technique.

01Lecture 01 · Session 1: Course Overview, Data Types, and Measures of Central Tendency

02Lecture 02 · Measures of Variability, Dispersion, and Introduction to Probability

03Lecture 03 · Probability Axioms, Conditional Probability, and Total Probability

04Lecture 04 · Bayes Theorem and Naive Bayes Classifier

05Lecture 05 · Naive Bayes, Laplace Smoothing, and Random Variables

06Lecture 06 · Random Variables — Discrete and Continuous Distributions, Expectation, Variance, Joint and Marginal Distributions

07Lecture 07 · Joint, Marginal, and Conditional Distributions — Discrete and Named Distributions

08Lecture 08 · Bernoulli, Binomial, Poisson, and Normal Distributions

09Lecture 09 · Descriptive Statistics, Probability, and Random Variables

10Lecture 10 · Sampling, Sampling Distributions, and Estimation

11Lecture 11 · Hypothesis Testing

12Lecture 12 · Hypothesis Testing — ANOVA, Chi-Square, Correlation and Regression

13Lecture 13 · Covariance, Correlation, and Linear Regression

14Lecture 14 · Time Series Analysis — Foundations and Basic Forecasting Models

15Lecture 15 · Time Series Analysis — Smoothing and Forecasting

16Lecture 16 · Gaussian Mixture Models, Maximum Likelihood Estimation, and Hypothesis Testing

17Lecture 17 · Hypothesis Testing Review, ANOVA, Chi-Square, Time Series, and GMM

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