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. You will begin by learning overview 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:
- Fundamentals of matrix algebra: vectors, matrices, tensors
- Multiplying matrices and vectors
- Identity and inverse matrices
- Linear dependence and span
- Norms
- Dimensions and hyperplanes
- Conjugate gradients
- Eigenvalues, Eigenvectors, Eigendecomposition
- Principal Component Analysis
- Basics of TensorFlow
Practical experience:
- Hands-on lab assignments and projects using various open-source software programs
Course typically offered: Online in Fall, Winter, Spring, and Summer
Software: Students will use Octave, Caffe, and TensorFlow to complete hands-on assignments and projects. These tools are free and open-source.
Prerequisites: Strong understanding of college-level algebra and calculus required.
Next steps: Upon completion, consider taking courses in our Data Mining for Advanced Analytics program 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)
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4/3/2018 - 6/5/2018
$650
Online
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CLASS TYPE:
Online, Web-based instruction
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: 4/10/2018.
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4/3/2018 - 6/5/2018
ucsdextension.blackboard.com
You will have access to your course on the published start date.
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6/25/2018 - 9/8/2018
$650
Online
-
-
-
CLASS TYPE:
Online, Web-based instruction
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.
-
POLICIES:
No refunds after: 7/2/2018.
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6/25/2018 - 9/8/2018
ucsdextension.blackboard.com
You will have access to your course on 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.