Summer is coming, and it is a great time to do some out-of-class learning! As part of Build Intro to Tech Skills, we are proud to announce the release of AI for Beginners Curriculum. It is a 24-lesson-long curriculum available on GitHub under MIT License, and it is intended for both students and teachers:
Studentscan use it to learn the basics of AI and Neural Networks. In addition to text-based lessons, you will have executable Jupyter Notebooks with samples, as well as labs that you can do to deepen your knowledge. You canrun notebookseither on your local computer or in the cloud.
Teacherswould be able to re-use this material in class, or incorporate some of the samples into their own courses. Also, the course isopen for contributions.
About the Course
Despite the nameAI for Beginners, this course is not a lightweight introductory course that will give you basic idea of what AI is, and teach how to use Cognitive Services or pre-trained models. It is a full-fledged course on AI, similar to the one that you will learn in the university. We have links to more lightweight courses fromAI for Beginnershome page, for example AI Fundamentals onComputer VisionandNatural Language. This course is also not about ways AI can be used in business: there is greatIntroduction to AI for Business userson MS Learn for that.
The main difference betweenAI for Beginnersand a university course is that we tried to make it a bit more applied, and less theoretical, accessible to students without strong mathematical background. This course is definitely not a replacement for a university course, and you should not expect to be able to invent new neural architectures after taking it - but for sure you will be able to use different existing architectures and train complex AI models. Good thing is that without strong mathematics this course should be comprehensible even for high school students (although we have not tried)!
Here is a small glimpse into what students will be able to do during the course:
What’s in the Course
AI for Beginnersis a text-based course that will introduce you to the topic of AI in general, with special emphasis on Neural Networks and Deep Learning. We provide you with lesson materials and a set of executable Jupyter Notebooks full of content together with some exercises / labs for you to play with.
Unlike many contemporary courses that cover only neural networks and deep learning, we also touch on topics such as knowledge representation and reasoning, multi-agent systems and genetic algorithms, albeit very briefly.
The main topic covered in the curriculum include:
Overview of AI landscape and different approaches to intelligence.
GOFAI, symbolic reasoning and knowledge representation.
Neural networks and Neural frameworks: Tensorflow and PyTorch.
Computer Vision: from simple convolution networks to object detection and semantic segmentation.
Natural Language Processing: from simple recurrent networks to modern transformer-based architectures.
Deep Reinforcement Learning
Other AI techniques, such as Genetic Algorithms and Multi-Agent Systems
Learning together as a group is definitely more fun! Later this summer or in the fall we are planning to start aStudy groupfor us to learn the material in order, over the period of a couple of months. This study group will be an experiment, to see how we can use this curriculum to implementflipped classroomapproach, where you learn the theory at home, and then we get together to discuss.
If you want to be a part of the study group - make sure tojoin Telegram channel, because that is the place we will post all the announcements.
We need your feedback!
This curriculum is not a book, it is rather a living organism. We are sure the first time we release it, it contains some bugs in the code, as well as some places that are not very clear. Please let us know how we can improve it! And if you find out some of the problem that you can fix yourself, or some content you want to add - feel free to do the pull request, to become co-author of the curriculum!
Also, we would like to welcome any translations of the materials to different languages. Get in touch if you want to contribute!