Mathematics For Artificial Intelligence
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The document discusses the importance of mathematics for artificial intelligence and provides an overview of key mathematical concepts relevant to AI, including linear algebra, calculus, and probability. It notes that an AI practitioner who does not understand math is like a politician without persuasion skills, as both have essential areas they must master. The overview then lists topics in linear algebra like vectors, matrices, eigenvalues and eigenvectors. It also covers calculus concepts such as functions, derivatives, and gradient algorithms. Finally, it outlines important probability topics such as Bayes' theorem, random variables, and different probability distributions.
Mathematics For Artificial Intelligence
- 3. “A person working in the field of AI who doesn’t know math is like a politician who doesn’t know how to persuade. Both have an inescapable area to work upon!” https://opencubelabs.com
- 4. LINEAR ALGEBRA ● Vectors ● Matrices ● Eigenvalues & Eigenvectors ● Principle Component Analysis ● Singular Value Decomposition https://opencubelabs.com
- 5. CALCULUS ● Functions ● Scalar Derivative ● Gradient ● Vector and Matrix Calculus ● Gradient Algorithms https://opencubelabs.com
- 6. PROBABILITY ● Basic Rules and Axioms ● Random Variables ● Bayes’ Theorem ● Distributions: Binomial, Bernoulli, Poisson, Exponential, Gaussian ● Conjugate Priors https://opencubelabs.com