Introduction to Probability
Introduction to Probability
Probability
Probability for AI & ML
less than a minute
In this section, we will cover the topics related to Probability for AI & ML.
Probability for AI & ML
This sheet contains all the topics that will be covered for Probability for AI & ML.
Below is the list of topics that will be covered in this section.
- Probability
- Sample Space
- Random Variables
- Discrete & Continuous Random Variables
- Probability Distribution
- Cumulative Distribution Function (CDF)
- Probability Mass Function (PMF)
- Probability Density Function (PDF)
- Bayes’ Theorem
- Conditional Probability
- Joint Probability
- Marginal Probability
- Independence & Mutual Exclusion
- Expectation
- Markov Inequality
- Chebyshev’s Inequality
- Chernoff Bound
- Law of Large Numbers
- Independent & Identically Distributed Random Variables
- Cross Entropy
- Kullback-Leibler Divergence
- Maximum Likelihood Estimation (MLE)
- Maximum A Posteriori Estimation (MAP)
- Minimum Mean Squared Error (MMSE)
End of Section
Conditional Probability
Conditional Probability & Bayes Theorem
Independence of Events
Independence of Events
Cumulative Distribution Function
Cumulative Distribution Function of a Random Variable
Probability Mass Function
Probability Mass Function of a Discrete Random Variable
Probability Density Function
Probability Density Function of a Continuous Random Variable
Expectation
Expectation of a Random Variable
Moment Generating Function
Moment Generating Function
Joint & Marginal
Joint, Marginal & Conditional Probability
Independent & Identically Distributed
Independent & Identically Distributed (I.I.D) Random Variables
Convergence
Convergence of Random Variables
Law of Large Numbers
Law of Large Numbers
Markov's Inequality
Markov’s, Chebyshev’s Inequality & Chernoff Bound
Cross Entropy & KL Divergence
Cross Entropy & KL Divergence
Parametric Model Estimation
Parametric Model Estimation