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Introduction to Statistics
1. Introduction to Statistics
2. Descriptive Statistics
Probability Theory
3. Introduction to probability theory
3.1. Definition of stochastic variable
3.2. Discrete stochastic variables
3.3. Continuous stochastic variables
3.4. Multi-dimensional stochastic variables
3.5. Transformations of probability functions
3.6. Characteristic functions
3.7. Convergence in statistics
3.8. Independent identically distributed random variables
3.9. Heavy-tailed distributions
4. Stochastic processes
4.1. Wiener process - Brownian motion
4.2. White noise
4.3. Stochastic calculus
5. “Correlation is Not Causation”
Statistical Inference
6. Introduction to Statistical Inference
Introduction to Machine Learning
7. Introduction to Machine Learning
7.1. Models in Machine Learning
7.2. Good Practices in Machine Learning
8. Supervised Learning
8.1. SL: theory
9. Unsupervised Learning
10. Reinforcement Learning
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Transformations of probability functions
3.5.
Transformations of probability functions
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