The Mississippi Artificial Intelligence Network (MAIN) is excited to announce a new addition to its course offerings: Data-Centric AI, based on Intel’s AI for Workforce curriculum!
Enroll today for FREE: https://mainms.org/resources/
Course Description:
This course, presented in a “Train the Trainer” format, provides an application-focused introduction to the mathematical foundations essential for Artificial Intelligence, emphasizing their role throughout the AI Project Cycle. Participants will explore key concepts from Statistics, Probability, and Linear Algebra, developing a deep appreciation for how mathematics underpins AI systems. Through hands-on exercises using Python, learners will apply these concepts to real-world datasets—practicing data transformation, sampling, feature extraction, and model evaluation. The course introduces descriptive and inferential statistics, matrix operations, and dimensionality reduction techniques such as PCA and SVD. Additionally, learners will engage with probability-driven approaches including Bayes Theorem, enhancing their understanding of classification and optimization tasks. Ethical considerations such as data bias, along with the use of industry dashboards, are addressed to promote responsible AI practices. Designed to build both theoretical understanding and practical application, the course equips participants to leverage core mathematical tools in optimizing AI models and making data-informed decisions.
Learning Objectives:
By the end of this course, participants will be able to:
- List several applications of Mathematics in AI and identify the role of Mathematics in the AI Project Cycle.
- Describe fundamental properties of Descriptive and inferential Statistics. Explain the need for transforming data into raw features and the influence of Descriptive Statistics on Synthetic Data Generation.
- Distinguish between Population & Sampling. Describe general characteristics & and common measures of dispersion for the given dataset.
- Apply Python to perform the above Mathematical operations for given datasets.
- Examine the impact biased data could have on the AI Project and new challenges brought by the advancement of Generative AI.
- Discuss and report various applications of dashboards across different industries.
- Describe basic concepts & and terms encountered in Linear Algebra – Scalars, Vectors & Matrices.
- Perform common Linear Algebra operations encountered in AI Projects using Python.
- Differentiate between SVD & PCA. Explain the importance of feature extraction and dimensionality reduction.
- Describe common terms encountered in Probability. Apply Bayes Theorem in common Machine Learning tasks such as Classification and Optimization.
- Evaluate traditional AI models using common metrics, explore metrics for Generative AI models, and differentiate overfitting from underfitting.
- Appreciate the role of calculus in optimizing AI models.
- And more!