Video Playlist Notebook

Video Playlist

• 1.1 Set Theory (16 min)
• 1.2 Probability Axioms (21 min)
• 1.3 Conditional Probability Concepts (15 min)
• 1.4 Conditional Probability Examples (11 min)
• 1.5 Independence (16 min)
• 1.6 Counting (13 min)
• 2.1 Discrete Random Variables (22 min)
• 2.2 Discrete Random Variables: Cumulative Distribution Function (11 min)
• 2.3 Expectation for Discrete Random Variables (16 min)
• 2.4 Variance for Discrete Random Variables (11 min)
• 2.5 Important Families of Discrete Random Variables (21 min)
• 2.6 Conditioning for Discrete Random Variables (18 min)
• 3.1 Continuous Random Variables (21 min)
• 3.2 Expectation and Variance for Continuous Random Variables (13 min)
• 3.3 Important Families of Continuous Random Variables (14 min)
• 3.4 Conditioning for Continuous Random Variables (13 min)
• 4.1 Pairs of Discrete Random Variables (16 min)
• 4.2 Conditional PMF (14 min)
• 4.3 Pairs of Continuous Random Variables (14 min)
• 4.4 Conditional PDF (12 min)
• 4.5 Independence of Random Variables (15 min)
• 4.6 Expected Value of a Function of Random Variables (14 min)
• 4.7 Conditional Expectation (17 min)
• 5.1 Covariance (16 min)
• 5.2 Correlation Coefficient (8 min)
• 5.3 Jointly Gaussian Random Variables - Concepts (12 min)
• 5.4 Jointly Gaussian Random Variables - Examples (8 min)
• 5.5 Random Vectors (16 min)
• 5.6 Gaussian Vectors (7 min)
• 6.1 Binary Hypothesis Testing - Concepts (18 min)
• 6.2 Binary Hypothesis Testing - Examples (9 min)
• 6.3 Likelihood Ratio (17 min)
• 6.4 Detection with Vector Observations (11 min)
• 7.1 MMSE Estimation (14 min)
• 7.2 Linear Estimation (14 min)
• 7.3 Vector Estimation (8 min)
• 8.1 Sums of Random Variables (11 min)
• 8.2 Limits of Random Variables (10 min)
• 9.1 Confidence Intervals - Concepts (14 min)
• 9.2 Confidence Intervals - Examples (7 min)
• 9.3 Significance Testing - Concepts (13 min)
• 9.4 Significance Testing - Examples (8 min)
• 10.1 Machine Learning Overview (15 min)
• 10.2 Binary Classification (17 min)
• 10.3 Dimensionality Reduction (11 min)
• 11.1 Markov Chains (13 min)
• 11.2 State Vector and Transition Matrix (13 min)
• 11.3 State Classification (18 min)
• 11.4 Unique Limiting State Vector (11 min)