Introduction to Linear Algebra

by Dr. Alexander Coward

 

Overview

Linear Algebra – the study of vector spaces and linear maps – is foundational to many branches of both pure and applied mathematics. In particular, with the advent of neural networks, it has become a crucial ingredient in machine learning and artificial intelligence.

While there are many courses that approach machine learning from a practical point of view, this course, along with the second course in this series, is intended for students who want to develop a deeper understanding of the details of Linear Algebra.

Venue

All classes will be taught in person in the Principia Mathematica room at the Academia.edu offices, at California and Kearny in downtown San Francisco.

Schedule

The course will last eight weeks, with two class meetings and one lab per week. If you have to miss a class, we'll make sure you're able to catch up, either by scheduling a one-one meeting or sharing a video of the class.

On Wednesday evenings there will be a class from 6:30pm to 8:30pm in the form of a lecture on the week's topics.

On Saturday mornings from 10am to 12:30pm there will be an optional lab to work together on the week's homework assignment, and we will go for lunch afterwards. If you wish to have your homework reviewed, it will be due at 5pm on Saturday via email.

On Sunday evenings from 6pm to 8pm there will be a discussion class to review the homework. Alternatively, you may opt to have the homework reviewed in individual tutorials; if so, please select the appropriate option while booking.

Curriculum

We will be following the first year curriculum for undergraduates in the University of Oxford Mathematics degree program studying Linear Algebra. The course details are available here.

Prerequisites

Participants should have a solid understanding of high-school mathematics, but beyond that there are no prerequisites.

Learning Outcomes

By the end of the course, participants will:

  • Have an understanding of matrices and of their applications to the algorithmic solution of systems of linear equations and to their representation of linear maps between vector spaces;
  • Understand the notions of a vector space, a subspace, linear dependence and independence, spanning sets and bases;
  • Understand and be able to use the abstract notions of a general vector space, a subspace, linear dependence and independence, spanning sets, bases, and be able to prove results related to these concepts;
  • Be prepared for the second course in this series, Advanced Linear Algebra, which will be running for eight weeks following the same format starting November 7th.

Venue

Academia.edu
580 California Street
Fourth floor
San Francisco, CA 94104

Dates

Wed, Sep 5, 2018, 6:30pm –
Sun, Oct 28, 2018, 8:00pm.

 

Attendance is free for students who subscribe to an EDeeU Education Director of Studies.