5

There are all sorts of things you could cover — machine learning is an extremely interesting and growing field, with many different approaches and tools you could explore. Some of the more popular tasks for machine learning algorithms include classification, regression and clustering. Not every application fits into this grouping (e.g. AlphaGo, the Go ...


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

The first two paragraphs of the Wikipedia article on Machine Learning will give you a good outline of the things you need to know. Algorithms and Data Structures are key from CS, but you also need quite a lot of things like Statistics, Mathematics, Pattern Recognition and such. It is a pretty long road if you start out only knowing how to program. It is ...


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

It's hard to know what you really know from just your assertion that you've "learned to code and know the basics of Machine learning as well." Do you mean that you can write a script/notebook in Python/R using libraries like sklearn or tensorflow? Can you do a full ML pipeline - preprocess data, train a model, then test and evaluate it's performance (oh, and ...


2

What are some topics (Machine Learning) that could be covered in a year long timespan that could be done by students with a basic understanding of Python and basic statistics (correlation/regression, measures of central tendency, hypothesis testing)? With regards to Neural Networks the for an introduction, even before specking oneself or doing suggested ...


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 ...


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 ...


1

I think one of the easiest ML algorithms to understand is K-Nearest Neighbors (KNN). We do a KNN project in my data structures class in the first week of school so that they can refresh their skills from AP CS and demystify ML a little bit. What I like about KNN is that it's conceptually easy to understand for high school students (I showcased it at Open ...


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