Linear Algebra and Geometry 3
What you'll learn
- How to solve problems in linear algebra and geometry (illustrated with 144 solved problems) and why these methods work.
- Solve more advanced problems on eigendecomposition and orthogonality than in the second course.
- Use diagonalization of matrices for solving various problems from different branches of mathematics (ODE, dynamical systems).
- Inner product spaces different from R^n: space of continuous functions, spaces of polynomials, spaces of matrices.
- Work with geometric concepts as length (norm), distance, angles, and orthogonality in non-geometric setups.
- Pythagorean Theorem, Cauchy-Schwarz inequality, and triangle inequality in various inner product spaces.
- Orthogonal and orthonormal bases, and Gram-Schmidt process in various inner product spaces.
- Min-max problems using Cauchy-Schwarz inequality, Best Approximation Theorem, least squares solutions.
- Symmetric matrices and their properties; orthogonal diagonalization: how it is done and how to understand it geometrically.
- Positive/negative definite matrices, indefinite matrices; various methods of determining definiteness of matrices.
- Quadratic forms and their connection to symmetric matrices: uniqueness of this correspondence and its consequences.
- Geometry of quadratic forms in two and three variables: conic sections and quadratic surfaces.
- Some concepts from abstract algebra: group, ring, field, and isomorphism; understand the concept of isomorphic vector spaces.
- Crowning of the course and a natural consequence of all the other topics: Singular Value Decomposition and pseudoinverses.
- Note: all the vector spaces discussed in this course are spaces over R (not over the field of complex numbers), and all our matrices have only real entries.
- High-school and college mathematics (mainly arithmetic, some trigonometry, polynomials)
- Linear Algebra and Geometry 1 (systems of equations, matrices and determinants, vectors and their products, analytic geometry of lines and planes)
- Linear Algebra and Geometry 2 (vector spaces, linear transformations, orthogonality, eigenvalues and eigenvectors, diagonalization)
- Some basic calculus
- Basic knowledge of complex numbers (this course contains a short introduction to complex numbers)
Linear Algebra and Geometry 3
Inner product spaces, quadratic forms, and more advanced problem solving
Chapter 1: Eigendecomposition, spectral decomposition
S1. Introduction to the course
S2. Geometrical operators in the plane and in the 3-space
You will learn: using eigenvalues and eigenvectors of geometrical operators such as symmetries, projections, and rotations in order to get their standard matrices; you will also strengthen your understanding of geometrical transformations.
S3. More problem solving; spaces different from R^n
You will learn: work with eigendecomposition of matrices for linear operators on various vector spaces.
S4. Intermezzo: isomorphic vector spaces
You will learn: about certain similarities between different spaces and how to measure them.
S5. Recurrence relations, dynamical systems, Markov matrices
You will learn: more exciting applications of eigenvalues and diagonalization.
S6. Solving systems of linear ODE, and solving higher order ODE
You will learn: solve systems of linear ODE and linear ODE of higher order with help of diagonalization.
Chapter 2: Inner product spaces
S7. Inner product as a generalization of dot product
You will learn: about other products with similar properties as dot product, and how they can look in different vector spaces.
S8. Norm, distance, angles, and orthogonality in inner product spaces
You will learn: how to define geometric concepts in non-geometric setups.
S9. Projections and Gram-Schmidt process in various inner product spaces
You will learn: apply Gram-Schmidt process in inner product spaces different from R^n (which were already covered in Part 2); work with projections on subspaces.
S10. Min-max problems, best approximations, and least squares
You will learn: solve some simple min-max problems with help of Cauchy-Schwarz inequality, find the shortest distance to subspaces in IP spaces, handle inconsistent systems of linear equations.
Chapter 3: Symmetric matrices and quadratic forms
S11. Diagonalization of symmetric matrices
You will learn: about various nice properties of symmetric matrices, and about orthogonal diagonalization.
S12. Quadratic forms and their classification
You will learn: how to describe (geometrically) and recognise (from their equation) quadratic curves and surfaces.
S13. Constrained optimization
You will learn: how to determine the range of quadratic forms on (generalized) unit spheres in R^n.
Chapter 4: The Grand Finale
S14. Singular value decomposition
You will learn: about singular value decomposition: how it works and why it works; about pseudo-inverses.
S15. Wrap-up Linear Algebra and Geometry
Make sure that you check with your professor what parts of the course you will need for your final exam. Such things vary from country to country, from university to university, and they can even vary from year to year at the same university.
A detailed description of the content of the course, with all the 200 videos and their titles, and with the texts of all the 144 problems solved during this course, is presented in the resource file
under video 1 ("Introduction to the course"). This content is also presented in video 1.
Who this course is for:
- University and college engineering
I am a multilingual mathematician with a passion for mathematics education. I always try to find the simplest possible explanations for mathematical concepts and theories, with illustrations whenever possible, and with geometrical motivations.
I worked as a senior lecturer in mathematics at Uppsala University (from August 2017 to August 2019) and at Mälardalen University (from August 2019 to May 2021) in Sweden, but I terminated my permanent employment to be able to create courses for Udemy full-time.
I am originally from Poland where I studied theoretical mathematics and got pedagogical qualifications at the Copernicus University in Torun (1992-1997). Before that, I enjoyed a very rigorous mathematical education in a mathematical class in high school "Liceum IV" in Torun, which gave me a very solid foundation for everything else I have learned and taught later.
My PhD thesis (2009) was at Uppsala University in Sweden, with the title: "Digital Lines, Sturmian Words, and Continued Fractions".
In 2018 I received four pedagogical prizes from students at the Faculty of Science and Technology of Uppsala University: on May 13th from the students at the Master Program in Engineering Physics; on May 25th from the students at the Master Program in Electrical Engineering; on December 20th from the students at the Master Program in Chemical Engineering; on January 10th 2019 from UTN (Uppsala Union of Engineering and Science Students at Uppsala University).
I speak Polish, Swedish, English, Dutch, and some Russian; learning Ukrainian.
I have a background in medicine and software development. I've done enough mathematics to at least follow along in Hania's courses and I'm learning a lot as I edit the material. I have also written a book about medical software design as it pertains to the medical record ("Rethinking the electronic healthcare record"). For Hania's math courses, it's my job to set up the environment and produce the final output that goes into these courses.