Complete linear algebra: theory and implementation

Course details

You need to learn linear algebra!

Linear algebra is perhaps the most important branch ofmathematics for computational sciences, includingmachine learning, AI, data science, statistics, simulations, computer graphics,multivariate analyses, matrix decompositions,and so on.

You need to knowapplied linear algebra, not just abstract linear algebra!

The way linear algebra is presented in 30-year-old textbooks is different from how professionals uselinear algebra in computers to solve real-world applications.For example, the "determinant" of a matrixis important forlinear algebra theory, but should you actually use the determinant in practical applications?The answer may surprise you, and it's in this course!

If you are interested in learning the mathematicalconcepts linear algebra and matrix analysis,butalso want toapplythose concepts to data analyses on computers, then this course is for you!

Unique aspects of this course

• Clear and comprehensible explanations of concepts and theories in linear algebra.
• Several distinct explanations of the same ideas, which is a proven technique for learning.
• Visualization using graphs, numbers, and spaces thatstrengthens the geometric intuition of linear algebra.
• Implementations in MATLAB and Python. Com'on, in the real world, you never solve math problems by hand! You need to know how to implement math in software!
• Beginning to intermediate topics, including vectors, matrix multiplications, least-squares projections, eigendecomposition, and singular-value decomposition.
• Strong focus on modern applications-oriented aspects of linear algebra and matrix analysis.
• Intuitive visual explanations of diagonalization, eigenvalues and eigenvectors, and singular value decomposition.

Benefits of learning linear algebra

• Understand statistics including least-squares, regression, and multivariate analyses.
• Improvesimulations in engineering, computational biology, finance, and physics.
• Understand data compression and dimension-reduction (PCA, SVD, eigendecomposition).
• Understand the math underlying machine learning and linear classification algorithms.
• Explore the link between linear algebra, matrices, and geometry.

Why Iam qualified to teach this course:

Ihave beenusinglinear algebra extensively in my research and teaching (primarily in MATLAB)for many years. Ihave written several textbooks about data analysis, programming, and statistics, that rely extensively on concepts in linear algebra.

So what are you waiting for??

Watch the course introductory video and free sample videos to learn more about the contents of this course and about my teaching style. If you are unsure if this course is right for you and want to learn more, feel free to contact with me questions before you sign up.

Ihope to see you soon in the course!

Mike