About This Course
This course introduces statistical and mathematical methods needed in the practice of data science. It covers basic principles in probability, statistics, linear algebra, and optimization.
- Probability: Probability basics (axioms of probability, conditional probability, random variables, expectation, independence, etc.), multivariate distributions, introduction to concentration bounds, laws of large numbers, central limit theorem.
- Statistics: Maximum a posteriori and maximum likelihood estimation, minimum mean-squared error estimation, confidence intervals.
- Linear algebra: Vector spaces, linear transformations, singular value decomposition, eigendecomposition, principal component analysis, least squares, regression.
- Optimization: Matrix calculus, gradient descent, coordinate descent, introduction to convex optimization.
3 credit hours; This 15 week course requires students to spend approximately 8 hours a week on course work.