Numerical Methods

Catalog description

Algorithms in machine learning and neural networks are built upon a strong foundation of linear algebra. For example, modern recommendation systems may have sparse matrices with millions of users and millions of items; matrix factorization methods make the underlying calculations tractable say this course builds a foundation of linear algebra concepts such as matrices, determinants, vectors and eigen values. Then it deepens it into data science applications around network analysis and logistic algorithms. In addition, some multi-variate calculus and graph theory topics are covered.