Regression

This is a technique used to predict numerical values based on the relationship between variables. The book discusses both linear and logistic regression, and how these models are used in AI.

Neural Networks

These are a class of AI models inspired by the structure of the human brain. The book explains the math behind neural networks, including training functions, loss functions, and optimization techniques.

Convolution

In the context of AI, convolution is a mathematical operation often used in image processing and convolutional neural networks to extract features from images.

Optimization

This involves finding the best solution to a problem, often by minimizing a loss function. It's a critical component in training AI models.

Probability

The book covers fundamental concepts of probability theory, including probability distributions, conditional probabilities, and Bayesian networks. These concepts are essential for dealing with uncertainty in AI systems.

Markov Processes

These are mathematical systems that evolve over time, where the future state depends only on the current state. They are used to model sequences of events in AI.

Differential Equations

These equations describe rates of change and are used to model various phenomena. The book explores how AI can be used to solve or approximate solutions to differential equations.

Singular Value Decomposition (SVD)

This is a matrix factorization technique with applications in image processing, natural language processing, and data analysis.

Natural Language Processing (NLP)

This field focuses on AI techniques for understanding and processing human language. The book discusses vector representations of text and applications of NLP.

Probabilistic Generative Models

These AI models can generate new data that resembles the training data. The book covers various types of generative models and their applications.

Graph Models

These models use graphs to represent relationships between data points. The book explores graph theory and graph neural networks.

Operations Research

This field uses mathematical and computational methods to make better decisions. The book discusses optimization techniques and their relevance to AI.

Mathematical Logic

This involves the principles of logical reasoning. The book touches on different logic frameworks and their connections to AI.

Ethics in AI

The book also addresses the ethical and social implications of AI technologies, emphasizing the importance of responsible AI development and deployment.