ALGORITHMS FOR DATA SCIENCE

Prerequisites: basic knowledge in programming (e.g., at the level of
COMS W1007
), a basic grounding in calculus and linear algebra.
Methods for organizing data, e.g. hashing, trees, queues, lists,priority queues. Streaming algorithms for computing statistics on the data. Sorting and searching. Basic graph models and algorithms for searching, shortest paths, and matching. Dynamic programming. Linear and convex programming. Floating point arithmetic, stability of numerical algorithms, Eigenvalues, singular values, PCA, gradient descent, stochastic gradient descent, and block coordinate descent. Conjugate gradient, Newton and quasi-Newton methods. Large scale applications from signal processing, collaborative filtering, recommendations systems, etc.

• TR 1:10 p.m.-2:25 p.m.
• TR 2:40 p.m.-3:55 p.m.
• Instructor: Eleni Drinea
  CULPA: 8 Info
• 3 points