In this section, we will cover the topics related to Probability for AI & ML.
Maths for AI & ML
This sheet contains all the topics that will be covered for Maths 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)
We will understand each topic from pure AI & ML perspective.
We will NOT go into much of theoretical proof of everything.
However, we will dive deep as and when required.
However, we will dive deep as and when required.