This repository is a curated, subjective, and incomplete selection of resources for learning Artificial Intelligence, Machine Learning, Statistical Inference, Deep Learning, and Reinforcement Learning. It is primarily aimed at beginners without a Computer Science background, guiding them from foundational mathematics to advanced concepts and the ability to read research papers.

How It Works

The resource list is organized into categories including video lectures, MOOCs (Massive Open Online Courses), tutorials, and books. It emphasizes a structured learning path, starting with foundational mathematics (linear algebra, calculus, probability, statistics) before progressing to machine learning and deep learning concepts. The author stresses the importance of understanding the underlying mathematics for a deep grasp of the field.

Quick Start & Requirements

• Access: The resources are primarily online links to videos, course platforms (Coursera, Udacity), and free online books.
• Prerequisites: Foundational knowledge in linear algebra, probability, statistics, and calculus is recommended, with specific recommendations for Khan Academy and MIT OCW for these subjects.
• Setup: No direct installation is required; users access external learning materials.

Highlighted Details

• Comprehensive coverage from basic math to advanced DL topics.
• Includes recommendations from prominent figures like Yann LeCun, Geoffrey Hinton, and Andrew Ng.
• Offers a mix of introductory overviews and in-depth theoretical courses.
• Features resources tailored for specific communities like artists and musicians.

Maintenance & Community

The author notes that the list is difficult to keep up-to-date due to the rapid growth of AI resources. Suggestions for additions can be made via GitHub issues. An "Other Recommendations" section includes community-vetted resources.

Licensing & Compatibility

The repository itself is a list of links and does not have a specific license. The linked resources are subject to their own respective licenses and terms of use. Compatibility for commercial use or closed-source linking depends entirely on the licenses of the individual external resources.

Limitations & Caveats

The list is explicitly subjective and incomplete, reflecting the author's personal learning journey. While aiming for beginners, some advanced courses may still present a steep learning curve. The author also mentions that some video lectures may have audio issues.