Matrix Methods in Data Analysis, Signal Processing, and Machine Learning

Four boxes with text and four arrows to illustrate the math relationship among linear algebra, probability and statistics, optimization, and deep learning.

Relationship among linear algebra, probability and statistics, optimization, and deep learning. Courtesy of Jonathan Harmon. Used with permission.

Instructor(s)

MIT Course Number

18.065 / 18.0651

As Taught In

Spring 2018

Level

Undergraduate / Graduate

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Course Description

Course Features

Course Description

Linear algebra concepts are key for understanding and creating machine learning algorithms, especially as applied to deep learning and neural networks. This course reviews linear algebra with applications to probability and statistics and optimization–and above all a full explanation of deep learning.

Related Content

Gilbert Strang. 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning. Spring 2018. Massachusetts Institute of Technology: MIT OpenCourseWare, https://ocw.mit.edu. License: Creative Commons BY-NC-SA.


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