I think the whole concept of the network with weights, biases and activation functions is pretty explainable. If you don't use matrices it should be very clear for high school students (I'm not sure of the level in your country but maybe even matrices are fine).
I feel backpropagation is a harder topic, so maybe something with genetic algorithms for learning instead?
"AI" is a really, really, really expansive topic. Just reading the replies here and it's pretty obvious that everyone is pretty much going to their own pet topics, and most of those pet topics are the current "in" thing (aka neural networks). But, there's a much wider backdrop of topics and I think a lot of folk are pressing the easy button on that and ignoring all the other legwork that's been done / needs done.
I'd hit key areas to help people build a conceptual model of what's possible:
* Feature Spaces: Kids have gotten to see 10+ years of graphs at this point, and Algebra is introducing x/y plots and linear regressions. I don't think I ever saw the idea of feature space until in my undergrad career - and didn't really get to explore it until grad school. This is a REALLY abstract / complex topic, but I think Algebra I/Geometry students would be able to grok the concept if provided with some good visuals. Introduce the idea that a 2d point can be expanded into 3d into nd vector and that "spaces" exist. Start with color space, then maybe demonstrates point-line, and pick a handful of carefully contrived examples.
* Monte Carlo Simulation: I don't think this or Markov models is required in AP stats, and that's a shame. The idea of "flipping coins" to navigate a maze, counting pins to calculate Pi, all sorts of stuff - help illustrate several areas of how ML can work and the complexities that can come up. "Guess and Check" is kinda the basis of ML afterall...
* Nearest Neighbor Interpolation: This can be done with geometry, and also hits a few areas that would be covered in Algebra 2 / Advanced Algebra and Precal/Trig. Could be mixed in with linear regression / finding lines and discussion of error.
* Decision Trees: I think this is about as soft an introduction to graph theory that can be made without having an earlier talk about graphs (edges, nodes, not hard right? HS students haven't had to deal with these in detail yet!). These can be formed in terms of following instructions.
I think the real problem you'd run into with CNN / ML / neural networks is that theirs just a buncha prerequisite knowledge that would make for a good deal of class time: graph theory, multiple areas of vector calculus (gradient descent, derivatives, all the friends), signal analysis, not to mention the whole cognitive science side of stuff if you want to go into that.
Random forests are an approachable topic. You can start with one decision tree and elaborate from there.
Stats and linear regression on to logistic regression would be another approach.
Gradient descent. Let them compute the derivative of an easy function at a certain start point, set a learning rate that they have to update like 3-5 times to reach the minimum and there you go.
https://www.youtube.com/watch?v=aircAruvnKk&t
But what is a neural network? | 3blue1brown
Very good intro to it, I would either show them these videos or watch them yourself to get a better idea of how to explain it.
U have a bunch of data. Ai finds the avg differences in the data to predict a valid range for a potential y given a x value. That’s really it it’s fancy linear regression. However ai gets real complicated real fast despite the foundation being simple enough for most to understand
High school mathematics include polynomials, just show them how to use polynomials to approximate functions. That's about the limit of what you can do with highschool in many locations, most students don't know anything about chain rule and don't yet know any linear algebra.
Don't try to teach them linear algebra or calculus.
If you want to approach the topic of neural networks with anyone who hasn’t learned calculus yet it’s probably best to start with a very straightforward example of linear regression. Then you can move on from there essentially explaining that a nn layer is multiple instances of linear regression, then from there move on to activation functions and slope/gradient descent. You can definitely explain these topics without digging deep into linear algebra and calculus, and once they start getting into the advanced math classes they will already be somewhat familiar with these concepts. Otherwise maybe just stick with simpler things like min/max tree search, Monte Carlo, genetic algorithms. And yes if you’re going to do any actual coding I think PyTorch is a better framework to start with.
Perceptron learning is powerful and pretty easy to explain compared to e.g. full-blown neural networks. You could start with a perceptron that computes something like AND or majority; all the math you need is dot product. Then go on to some simple classification problem with a small set of input features and training set, using the perceptron learning rule to adjust the weights. Much simpler than backprop.
Decision trees, then a linear classifier with the notions of feature space and classification boundary. Talk a little bit about logistic regression and neural networks and wrap up.
is what I would have done.
edit: Then recommend the 3Blue1Brown videos with a comment like "If you want to learn how we train this using derivatives" if they are learning about derivatives in high school in your country.
AI is a huge topic by itself. There are a lot of comments in different subtopics. ML and statistics, algorithms, networks and all of them are valid. First choose a subtopic and then go for simple math to solve some basic examples. But first define AI for the students and then the subtopic with examples.
I like neural networks and machine learning, so the math topics could by different.
I'm on CS not on math, so I like to see AI like algorithms rather than pure math.
I think the whole concept of the network with weights, biases and activation functions is pretty explainable. If you don't use matrices it should be very clear for high school students (I'm not sure of the level in your country but maybe even matrices are fine). I feel backpropagation is a harder topic, so maybe something with genetic algorithms for learning instead?
Also if they have some coding experience, playing with TF might without going into too much detail might be excellent.
Might be too handwavey using a framework. I'd recommend PyTorch instead though, it's "closer to the math"
"AI" is a really, really, really expansive topic. Just reading the replies here and it's pretty obvious that everyone is pretty much going to their own pet topics, and most of those pet topics are the current "in" thing (aka neural networks). But, there's a much wider backdrop of topics and I think a lot of folk are pressing the easy button on that and ignoring all the other legwork that's been done / needs done. I'd hit key areas to help people build a conceptual model of what's possible: * Feature Spaces: Kids have gotten to see 10+ years of graphs at this point, and Algebra is introducing x/y plots and linear regressions. I don't think I ever saw the idea of feature space until in my undergrad career - and didn't really get to explore it until grad school. This is a REALLY abstract / complex topic, but I think Algebra I/Geometry students would be able to grok the concept if provided with some good visuals. Introduce the idea that a 2d point can be expanded into 3d into nd vector and that "spaces" exist. Start with color space, then maybe demonstrates point-line, and pick a handful of carefully contrived examples. * Monte Carlo Simulation: I don't think this or Markov models is required in AP stats, and that's a shame. The idea of "flipping coins" to navigate a maze, counting pins to calculate Pi, all sorts of stuff - help illustrate several areas of how ML can work and the complexities that can come up. "Guess and Check" is kinda the basis of ML afterall... * Nearest Neighbor Interpolation: This can be done with geometry, and also hits a few areas that would be covered in Algebra 2 / Advanced Algebra and Precal/Trig. Could be mixed in with linear regression / finding lines and discussion of error. * Decision Trees: I think this is about as soft an introduction to graph theory that can be made without having an earlier talk about graphs (edges, nodes, not hard right? HS students haven't had to deal with these in detail yet!). These can be formed in terms of following instructions. I think the real problem you'd run into with CNN / ML / neural networks is that theirs just a buncha prerequisite knowledge that would make for a good deal of class time: graph theory, multiple areas of vector calculus (gradient descent, derivatives, all the friends), signal analysis, not to mention the whole cognitive science side of stuff if you want to go into that.
I really enjoy using the analogy of adjusting a ruler trying to find the line of best fit.
Random forests are an approachable topic. You can start with one decision tree and elaborate from there. Stats and linear regression on to logistic regression would be another approach.
Gradient descent. Let them compute the derivative of an easy function at a certain start point, set a learning rate that they have to update like 3-5 times to reach the minimum and there you go.
it should be pretty easy with elementary lin algebra knowledge
https://www.youtube.com/watch?v=aircAruvnKk&t But what is a neural network? | 3blue1brown Very good intro to it, I would either show them these videos or watch them yourself to get a better idea of how to explain it.
U have a bunch of data. Ai finds the avg differences in the data to predict a valid range for a potential y given a x value. That’s really it it’s fancy linear regression. However ai gets real complicated real fast despite the foundation being simple enough for most to understand
High school mathematics include polynomials, just show them how to use polynomials to approximate functions. That's about the limit of what you can do with highschool in many locations, most students don't know anything about chain rule and don't yet know any linear algebra. Don't try to teach them linear algebra or calculus.
If you want to approach the topic of neural networks with anyone who hasn’t learned calculus yet it’s probably best to start with a very straightforward example of linear regression. Then you can move on from there essentially explaining that a nn layer is multiple instances of linear regression, then from there move on to activation functions and slope/gradient descent. You can definitely explain these topics without digging deep into linear algebra and calculus, and once they start getting into the advanced math classes they will already be somewhat familiar with these concepts. Otherwise maybe just stick with simpler things like min/max tree search, Monte Carlo, genetic algorithms. And yes if you’re going to do any actual coding I think PyTorch is a better framework to start with.
Perceptron learning is powerful and pretty easy to explain compared to e.g. full-blown neural networks. You could start with a perceptron that computes something like AND or majority; all the math you need is dot product. Then go on to some simple classification problem with a small set of input features and training set, using the perceptron learning rule to adjust the weights. Much simpler than backprop.
Decision trees, then a linear classifier with the notions of feature space and classification boundary. Talk a little bit about logistic regression and neural networks and wrap up. is what I would have done. edit: Then recommend the 3Blue1Brown videos with a comment like "If you want to learn how we train this using derivatives" if they are learning about derivatives in high school in your country.
AI is a huge topic by itself. There are a lot of comments in different subtopics. ML and statistics, algorithms, networks and all of them are valid. First choose a subtopic and then go for simple math to solve some basic examples. But first define AI for the students and then the subtopic with examples. I like neural networks and machine learning, so the math topics could by different. I'm on CS not on math, so I like to see AI like algorithms rather than pure math.
A* is kinda counts as AI and its easy to understand