Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
18.065 · Mathematics · Undergraduate · Spring 2018
Prof. Gilbert Strang
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.
The syllabus, on MIT OpenCourseWare
The full course — syllabus, assigned readings, problem sets, exams, and lecture notes — lives on OCW. These open the real thing:
Syllabus ↗
Course overview, grading, schedule
Readings ↗
The assigned reading list, session by session
Assignments ↗
Problem sets and projects
Full course on OCW ↗
Everything, including lecture materials
Attribution
Prof. 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: CC BY-NC-SA 4.0.
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