Linear Algebra for Machine Learning
Linear algebra provides a mathematical framework for organizing information and then using that information to solve problems, especially physics, math, engineering, or data analytics problems. Linear algebra is essential for understanding and creating machine learning algorithms, especially neural network and deep learning models.
In this course, you will learn the linear algebra skills necessary for machine learning and neural network modelling. The course starts off with a review of basic matrices and vector algebra as applied to linear systems. The hands-on lessons and assignments will equip you with the mathematical background required to build and train simple neural networks.
The course begins with an introduction in neural networks and overview of the basic machine learning algorithms with simple practical examples to develop more intuitive understanding for machine learning models and why we need linear algebra. Subsequent sessions will present in detail principles of linear algebra with a sharp focus on the topics that will be useful in Machine Learning.
- Tensor operations and their role in data representation and early neural network models such as MLP
- Expressing systems of linear equations in matrix format and obtain teh solution set, equivalent to calculating weights of neural network
- Calculate basis for a vector space, change of bases, used for text and image linear transformations: projection, mirroring
- Solving linear regression with matrix inversion of non-square matrices
- Continuous neural network optimization using gradient descent
- Singular value decomposition (with applications in image compression, reconstruction)
- Dimensionality reduction & principal component analysis
- Tensor flow 2 regression models
- Hands-on lab assignments and projects using various open-source software programs (Octave/Python)
Course typically offered: Quarterly, online.
Software: Students will use Octave/Python and TensorFlow to complete hands-on assignments and projects. These tools are free and open-source.
Prerequisites: Understanding of college-level algebra and calculus
Next steps: Upon completion, consider additional coursework in our specialized certificate in Machine Learning Methods to continue learning.
Contact: For more information about this course, please contact firstname.lastname@example.org.
Course Number: CSE-41287
Credit: 3.00 unit(s)
Related Certificate Programs: Machine Learning Methods
+ Expand All
3/28/2023 - 5/27/2023
This course is entirely web-based and to be completed asynchronously between the published course start and end dates. Synchronous attendance is NOT required.
You will have access to your online course on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
Bilyana Aleksic is senior staff hardware engineer with more than 20 years of experience in complex chip SOC development. She received her master's in control systems from Faculty of Electrical Engineering University of Belgrade, former Yugoslavia, and was an honored student of Mathematical Gymnasium, winning many world Mathematical Olympiad Competitions. Aleksic spent 15 years at Canadian tech company ATI Technologies where she worked on implementation of graphics CPU chips. After she moved to California she spent 7 years in mobile chips implementation and verification methodology development. During the past year she shifted her focus to machine learning for medical applications.
No information available at this time.
No refunds after: 4/3/2023.
3/28/2023 - 5/27/2023
You will have access to your course materials on the published start date OR 1 business day after your enrollment is confirmed if you enroll on or after the published start date.
There are no sections of this course currently scheduled. Please contact the Science & Technology department at 858-534-3229 or email@example.com for information about when this course will be offered again.