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. Then you will learn advanced skills for finding the highest and lowest points of systems, quantifying the degree of learning, and optimizing the speed of learning in vector spaces and linear transformations. The hands-on lessons and assignments will equip you with the mathematical background required to build and train simple neural networks.
Key topics:
- A review of matrix algebra fundamentals: vectors, matrices, linear systems
- Matrix Operations
- Linear System, Solution Sets
- Vector Spaces
- Eigenvalues, Eigenvectors
- Matrix inversion of non-square Matrices
- Quadratic forms, gradient descent
- Principal Component Analysis
- Basics of TensorFlow
Practical experience:
- Hands-on lab assignments and projects using various open-source software programs
Course typically offered: Quarterly, online.
Software: Students will use Octave, Caffe, and TensorFlow to complete hands-on assignments and projects. These tools are free and open-source.
Prerequisites: High school and/or college-level algebra. More specifically, knowledge and understanding of vectors, matrices, and three-dimensional coordinate systems.
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 unex-techdata@ucsd.edu.
Course Number: CSE-41287
Credit: 3.00 unit(s)
Related Certificate Programs: Machine Learning Methods
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9/20/2021 - 11/19/2021
$725
Online
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CLASS TYPE:
Online Asynchronous.
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.
Aleksic, Bilyana
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.
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TEXTBOOKS:
No information available at this time.
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POLICIES:
No refunds after: 9/26/2021.
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9/20/2021 - 11/19/2021
extensioncanvas.ucsd.edu
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.
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1/10/2022 - 3/11/2022
$750
Online
-
-
-
CLASS TYPE:
Online Asynchronous.
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.
Aleksic, Bilyana
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.
-
TEXTBOOKS:
No information available at this time.
-
POLICIES:
No refunds after: 1/16/2022.
-
1/10/2022 - 3/11/2022
extensioncanvas.ucsd.edu
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 unex-sciencetech@ucsd.edu for information about when this course will be offered again.