Mathematics for AI/ML/DL, OR/MS/IE, and Data Science

- Overview

Mathematics is a crucial aspect of AI because it provides the foundational framework for developing AI algorithms and models, allowing machines to efficiently process and analyze large amounts of data, making informed decisions through techniques like linear algebra, calculus, probability, and statistics, which are essential for tasks like image recognition, natural language processing, and optimization within AI systems.

Key roles about the importance of mathematics in AI: 

• Data representation: Linear algebra, with its matrices and vectors, enables the representation and manipulation of data, which is fundamental for AI applications like image recognition.
• Optimization: Calculus is used to optimize AI models by finding the best parameters to minimize errors and improve performance.
• Decision making: Probability and statistics help AI systems deal with uncertainty in data, allowing them to make informed decisions even with incomplete information.
• Algorithm design: Mathematical concepts are directly incorporated into AI algorithms, forming the basis for how machines learn and make predictions.

- Mathematics is the Foundation of AI

Mathematics is the foundation of AI. It enables machines to analyze, process, and interpret large amounts of data. 

Mathematics serves as the backbone of AI algorithms and models, empowering machines to process, analyze, and interpret vast amounts of data. Concepts from linear algebra, calculus, probability theory, and statistics are essential for developing ML algorithms.

Driven by data, ML models are the mathematical engines of AI, algorithmic expressions that can discover patterns and make predictions faster than humans. For the most transformative technological AI journey of our time, the engine you need is a ML model. 

For example, an ML model for computer vision might be able to identify cars and pedestrians in live video. A translatable word and sentence for natural language processing. 

However, math can be daunting, especially for someone from a non-technical background. Apply this complexity to ML and you're in a very bad place. We can easily build models and perform various ML tasks using widely available libraries in Python and R. So it's easy to avoid the math part of the field.

[ML Algorithms and Models]

- Mathematics is the Mother of All Sciences

Mathematics is considered the mother of all sciences as it is the tool for solving problems in all other sciences. Mathematics applied in all fields of science, including physics, engineering, biology and economics. Mathematics also helps develop critical thinking, problem-solving ability and logical reasoning ability.

Emmanuel Kant said: "A science is only accurate if it uses mathematics." Carl Friedrich Gauss called mathematics the "queen of science" because of its great success in revealing the nature of physical reality.

However, some people believe that mathematics is not a science. They say that science is devoted to understanding the physical world, and that our senses are the only way we receive information about the physical world. Mathematics, on the other hand, is meaning-agnostic. 

Mathematics (from Greek μάθημα máthēma “knowledge, study, learning”) is the study of quantity, structure, space, and change. Mathematicians seek out patterns and formulate new conjectures. Mathematicians resolve the truth or falsity of conjectures by mathematical proof. The research required to solve mathematical problems can take years or even centuries of sustained inquiry. 

Since the pioneering work of Giuseppe Peano (1858-1932), David Hilbert (1862-1943), and others on axiomatic systems in the late 19th century, it has become customary to view mathematical research as establishing truth by rigorous deduction from appropriately chosen axioms and definitions. 

When those mathematical structures are good models of real phenomena, then mathematical reasoning often provides insight or predictions. 

- The Information Theory for AI

Information theory is a mathematical method for analyzing and representing information. It's also known as the mathematical theory of communication. This is an important field that has made significant contributions to AI and Deep Learning.

Information theory is the study of how much information is present in the signals or data we receive from our environment. Machine learning (ML) is about extracting interesting representations/information from data which are then used for building the models.

Information theory is a combination of calculus, statistics, and probability. It's based on probability theory and statistics, and quantified information is usually described in terms of bits. 

Information theory is used in artificial intelligence (AI) and machine learning (ML) to create intelligent systems. It's also used to study the basic characteristics of data, such as structure and distribution. Information field theory (IFT) is a mathematical framework for signal reconstruction and non-parametric inverse problems.

Algorithmic information is an essential component in the theoretical foundations of AI. Some examples of concepts in AI that come from information theory or related fields include: 

• Entropy — also called Shannon Entropy. Used to measure the uncertainty in an experiment.

• Cross-Entropy — compares two probability distributions and tells us how similar they are.

• Kullback Leibler Divergence — another measure of how similar two probability distributions are.

• Viterbi Algorithm — widely used in Natural Language Processing (NLP) and Speech.

• Encoder-Decoder — used in Machine Translation RNNs and other models.