Differentiation and integration of functions; basic matrix operations; linearization; linear and nonlinear optimization techniques; clustering and similarity measures, introduction to probability and statistics, basic computational algorithms. Includes frequent illustration of concepts using mathematical computation tools.
- Solve practical discrete mathematics and calculus problems common in statistical learning theory.
- Express linear models and related concepts in matrix algebra.
- Explore functions that model non-linearities in the data.
- Understand Bayesian theory used in common statistical learning applications.
- Explore optimization methods and understand how common iterative algorithms work.
YOUR OPPORTUNITY: You'll be prepared to comprehend and develop tools and techniques for understanding an organization's data.
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