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, 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 Tensor Flow
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.
Optional reading: A college-level Linear Algebra book (either of the first two below are suggested).
Introduction to Linear Algebra, Fifth Edition (2016)
ISBN-13: 978-0980232776
Linear Algebra and Its Applications, 4th Edition
ISBN-13: 978-0030105678
Numerical Algorithms: Methods for Computer Vision, Machine Learning, and Graphics
ISBN-13: 978-1482251883
From Neuron to Brain
ISBN-13: 978-0878936090
Prerequisites: Basic knowledge of Linear Algebra - concept of vectors and matrices. Calculus I recommended.
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
+ Expand All
-
1/15/2019 - 3/16/2019
$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.
-
TEXTBOOKS:
No information available at this time.
-
POLICIES:
No refunds after: 1/22/2019.
-
1/15/2019 - 3/16/2019
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.