Math

Mathematics plays a crucial role in the development and advancement of artificial intelligence (AI). AI is fundamentally based on mathematical principles and concepts, and without a strong mathematical foundation, the potential of AI would be severely limited. It provides the language, tools, and frameworks to describe, analyze, and optimize complex problems. Math is used to model and solve AI tasks, develop algorithms and models, understand theoretical foundations, design and evaluate systems, and optimize performance. A strong mathematical foundation is crucial for advancing AI and building effective algorithms and systems.

Visit the following resources to learn more:

  1. Mathematics for Machine Learning Specialization

  2. Algebra and Differential Calculus for Data Science

Statistics

Statistics in AI refers to the use of statistical methods and techniques to analyze data, make inferences, and draw conclusions in the context of artificial intelligence. It involves applying statistical principles to design experiments, model data, estimate parameters, and evaluate the performance of AI models.

Visit the following resources to learn more:

  1. Introduction to Statistics-Stanford
  2. Hypothesis Testing
  3. Probability and Sampling
  4. Applying the Delta Method in Metric Analytics

Econometrics

Econometrics in AI refers to the application of AI techniques, such as machine learning, to analyze economic data, test theories, and make predictions. It is important because AI can handle large and complex datasets, improve economic forecasting accuracy, enable counterfactual analysis, automate data preprocessing, and enhance decision-making in economics. By combining AI with econometrics, economists can gain deeper insights, make more accurate predictions, and understand the complex relationships within the economy.

Visit the following resources to learn more:

  1. Fundamentals of Econometrics
  2. Intro to Econometrics
  3. Learn Time Series
  4. Forecasting Task with Solution

Classical ML

Classical machine learning (ML) refers to the traditional approach to machine learning that was prevalent before the advent of deep learning. It encompasses a set of algorithms and techniques that are used to build models and make predictions or decisions based on data.

Classical ML provides a foundation for understanding and applying machine learning techniques, and it continues to be relevant and widely used today, particularly in cases where simplicity, interpretability, and limited data availability are important considerations.

Visit the following resources to learn more:

  1. Bishop-Pattern-Recognition-and-Machine-Learning
  2. Machine Learning in Action