Foundations of Statistical Learning & Algorithms

This course covers linear algebra, probability, and optimization. It begins with systems of equations, matrix operations, vector spaces, and eigenvalues. Advanced topics include Cholesky and singular value decomposition. Probability modules address Bayes' theorem, Gaussian distribution, and inference techniques. The course concludes with model selection methods and an introduction to optimization

• Understand basics of linear algebra and its applications
• Apply concepts of probability and statistical inference
• Utilize optimization and mathematical modeling techniques

• Basic computer and internet skills
• Ability to read English instructions and complete short activities