Courses

AI and machine learning rely on statistical principles that are essential for building, evaluating, and interpreting algorithms. This course provides a rigorous yet accessible grounding in the statistical methodologies underpinning contemporary AI, machine learning, and data science. Students will explore inference techniques, hypothesis testing, and prediction models, progressing from foundational methods like linear regression and k-means clustering to advanced approaches such as random forests, XGBoost, PCA, and transformer architectures.

Through hands-on exercises with real and synthetic data sets, participants will learn to extract meaningful insights, evaluate model performance, and understand algorithmic limitations. Practical applications from healthcare, marketing, finance, and natural language processing will illustrate how statistical reasoning drives reliable AI solutions. By the end of the course, students will be able to select appropriate methodologies for diverse analytical challenges, interpret results with confidence, and design robust evaluation frameworks. This course is ideal for data scientists, engineers, and researchers seeking to strengthen their mathematical foundations of AI and machine learning.