<aside> <img src="https://s3-us-west-2.amazonaws.com/secure.notion-static.com/ab618346-09a2-4812-b0a4-a911d62994da/icons8-project-80.png" alt="https://s3-us-west-2.amazonaws.com/secure.notion-static.com/ab618346-09a2-4812-b0a4-a911d62994da/icons8-project-80.png" width="40px" /> An important objective of the course is for the student to be able to communicate mathematics on a high level succinctly and efficiently. Every student has to choose at least two topics of the list below and present it to an audience of his/her classmates and some faculty members.

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Evaluation (peer evaluation)

The final score (10% bounce) of this part of the course is divided into two subcategories

(5%) Attending and Participating in Evaluating other presentations

(5%) Presenting

• Every presentation will be evaluated based on three main criteria
  1. Content
  2. Organization and Visual aids
  3. Delivery
  • For more details ...
• The evaluation is peer evaluation. Every student is required to attend his classmates' presentations and participate in evaluating them.

How it works?

• Step One: A student chooses topics from the list below.

By the end of 1st week

• Step Two: Scheduling the presentations with coordination with all students. (The schedule will be available through the table below)

By the end of 2nd week

• Step Three: Preparing, presenting and evaluating.

From week 2 through week 15

Presentations List

Positive-definite, positive-semidefinite matrix Projection Perron–Frobenius theorem Perron–Frobenius theorem for nonnegative multilinear forms and extensions Vandermonde matrix Stochastic matrix Toeplitz matrix Circulant matrix Hankel matrix Gram–Schmidt process SVD representation in space Eigenvalues, Eigenvectors, Multiplicities and Graphs Projective geometry Combinatorial matrices Scheduling of QR Factorization Algorithms on SMP and Multi-Core Architectures Riemannian geometry and matrix geometric means Linear Algebra for Data Science Linear Algebra for Machine Learning

MATLAB Linear Algebra toolkit works

Julia JuMP modeling language