4

The first thing that must be discussed in the matter, is the mathematics involved. Deep learning is first and foremost a mathematical model. An overview of this big subject is given quite well in its Wikipedia article. The page showcases a summary of the subject, as well as links to more resources (2 birds in 1 go). To explain the mathematical model, I ...


3

From the perspective of MOOCs, a great place to start is MITx's 6.00.2x: Introduction to Computational Thinking and Data Science found on edX. It uses Python to introduce the study of data science and does not presume more than a beginner level of either Python or data science (although 6.00.1x, the first part of the course, is helpful for those who no ...


3

I suppose the design of the curriculum revolves around the goal. If the objective is to prepare students to enter the field as researchers, then the balance needs to favor the research that's active (and probably employable.) Where that fits into the AI picture as a whole is helpful. What limitations it has, and the missing pieces it doesn't solve are ...


2

This is only a partial answer as the scope of deep learning is vast and usually requires some fundamentals/prerequisites. Google Developers Youtube channel has a playlist Machine Learning Recipes with Josh Gordon which while not definitive of deep learning, provides a very accessible introduction to the field, especially to a younger audience without ...


2

A very interesting way to design a curriculum would be to in 3-lesson blocks. A block deals with a specific subject. A small list of various subjects in the field of AI can be found at the bottom of the answer (I put it there because it contains information that might be confusing if not explained beforehand). The 3 lessons for each block are formatted like ...


Only top voted, non community-wiki answers of a minimum length are eligible