/1806

18.06 course at MIT

Primary LanguageJupyter Notebook

MIT 18.06, Spring 2023
Linear Algebra

Welcome to MIT 18.06: Linear Algebra! The Spring 2023 course information, materials, and links are recorded below. Course materials for previous semesters are archived in the other branches of this repository. You can dive in right away by reading this introduction to the course by Professor Strang.

Catalog Description: Basic subject on matrix theory and linear algebra, emphasizing topics useful in other disciplines, including systems of equations, vector spaces, determinants, eigenvalues, singular value decomposition, and positive definite matrices. Applications to least-squares approximations, stability of differential equations, networks, Fourier transforms, and Markov processes. Uses linear algebra software. Compared with 18.700, more emphasis on matrix algorithms and many applications.

Syllabus

Lectures: Monday, Wednesday, and Friday at 11am in 26-100.

Instructors: Prof. Gilbert Strang and Dr. Andrew Horning.

Textbook: Introduction to Linear Algebra: 6th Edition. Professor Strang will explain more about this new 6th edition in class (it is not yet on Amazon). It now ends with two chapters on deep learning. Professor Strang plans to make the textbook available for students to purchase at a discount. Here again is a link to the preface and contents.

Recitations: Tuesday at the following times and locations.

  • Tuesday 9am: R1 Mo Chen (2-132)
  • Tuesday 10am: R2 Mo Chen (2-132), R3 V. Krylov (2-136)
  • Tuesday 11am: R4 V. Krylov (2-136), R5 M. Harris (4-159)
  • Tuesday 12pm: R6 M. Harris (4-159), R7 D. Kluiev (2-136), R9 I. Ganguly (2-105)
  • Tuesday 1pm: R8 D. Kluiev (2-136), R10 I. Ganguly (2-132)
  • Tuesday 2pm: R11 K. Vashaw (2-136)
  • Tuesday 3pm: R12 K. Vashaw (2-136)

Exams: TBD.

Resources: In addition to this repository, we will use the following online resources.

  • Canvas - course announcements will be posted on Canvas.
  • Gradescope - submit Psets and check grades through Gradescope.
  • Piazza - ask questions in the course discussion forum.

MIT also has excellent study resources: math learning center, TSR^2 study/resource room, pset partners.

Problem sets

Exams

Lecture Material and Summaries