Probability for Data Science

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Chapter 1Mathematical Background

  1. 1.1Infinite Series
  2. 1.2Approximation
  3. 1.3Integration
  4. 1.4Linear Algebra
  5. 1.5Basic Combinatorics
  6. Summary

Chapter 2Probability

  1. 2.1Set Theory
  2. 2.2Probability Space
  3. 2.3Axioms of Probability
  4. 2.4Conditional Probability
  5. Summary

Chapter 3Discrete Random Variables

  1. 3.1Random Variables
  2. 3.2Probability Mass Function
  3. 3.3Cumulative Distribution Functions (Discrete)
  4. 3.4Expectation
  5. 3.5Common Discrete Random Variables
  6. Summary

Chapter 4Continuous Random Variables

  1. 4.1Probability Density Function
  2. 4.2Expectation, Moment, and Variance
  3. 4.3Cumulative Distribution Function
  4. 4.4Median, Mode, and Mean
  5. 4.5Uniform and Exponential Random Variables
  6. 4.6Gaussian Random Variables
  7. 4.7Functions of Random Variables
  8. 4.8Generating Random Numbers
  9. Summary

Chapter 5Joint Distributions

  1. 5.1Joint PMF and Joint PDF
  2. 5.2Joint Expectation
  3. 5.3Conditional PMF and PDF
  4. 5.4Conditional Expectation
  5. 5.5Sum of Two Random Variables
  6. 5.6Random Vectors and Covariance Matrices
  7. 5.7Transformation of Multidimensional Gaussians
  8. 5.8Principal-Component Analysis
  9. Summary

Chapter 6Sample Statistics

  1. 6.1Moment-Generating and Characteristic Functions
  2. 6.2Probability Inequalities
  3. 6.3Law of Large Numbers
  4. 6.4Central Limit Theorem
  5. Summary

Chapter 7Regression

  1. 7.1Principles of Regression
  2. 7.2Overfitting
  3. 7.3Bias and Variance Trade-Off
  4. 7.4Regularization
  5. Summary

Chapter 8Estimation

  1. 8.1Maximum-Likelihood Estimation
  2. 8.2Properties of ML Estimates
  3. 8.3Maximum A Posteriori Estimation
  4. 8.4Minimum Mean-Square Estimation
  5. Summary

Chapter 9Confidence and Hypothesis

  1. 9.1Confidence Interval
  2. 9.2Bootstrapping
  3. 9.3Hypothesis Testing
  4. 9.4Neyman-Pearson Test
  5. 9.5ROC and Precision-Recall Curve
  6. Summary

Chapter 10Random Processes

  1. 10.1Basic Concepts
  2. 10.2Mean and Correlation Functions
  3. 10.3Wide-Sense Stationary Processes
  4. 10.4Power Spectral Density
  5. 10.5WSS Process through LTI Systems
  6. 10.6Optimal Linear Filter
  7. Summary
  8. Appendix