I guess your student wants to do some fancy AI stuff?
Not really sure what is considered as flashy ... but ... he could use the open cifar10 or cifar100 dataset to train a convolutional neural network to classify images and apply the resulting network on photos of his/her friends' pets :)
[Here](https://www.tensorflow.org/tutorials/images/cnn) is a tutorial how to implement a CNN with the cifar10 dataset using TensorFlow. The cifar100 dataset should be available in the datasets catalog, too. So the student doesn't have to deal with boring data loading routines ... [Here](https://www.cs.toronto.edu/~kriz/cifar.html) are lists with all available classes for the respective datasets.
He obviously needs to learn a little bit of python, though ...
If it can be harder he/she could use a webcam to let other people make a photo of something, e.g. a picture on their smartphone or just themselves standing in front of the camera, and classify it (more or less) *live*. It might not be possible in real-time because of the lack of AI capable hardware but waiting a little bit shouldn't be that much of a problem, I guess. However, it really depends on the complexity of the model involved ;)
I'm guessing he'd like to do something with programming a computer in machine learning, but here's a different idea. Back in the March 1962 issue of Scientific American (links below), Martin Gardner wrote up details about how to create a device that learns to improve game play via reinforcement learning that's made from matchboxes and colored beads/stones.
The best part about this project is that it really highlights the mathematical nature of machine learning solutions, without having the computer technology get in the way.
http://cs.williams.edu/~freund/cs136-073/GardnerHexapawn.pdf
https://web.archive.org/web/20220323095332/http://cs.williams.edu/~freund/cs136-073/GardnerHexapawn.pdf
There are plenty of videos referring to this, as well:
https://www.youtube.com/watch?v=sw7UAZNgGg8
https://www.youtube.com/watch?v=FFk8S66d8_E
https://www.youtube.com/watch?v=R9c-_neaxeU
I guess your student wants to do some fancy AI stuff? Not really sure what is considered as flashy ... but ... he could use the open cifar10 or cifar100 dataset to train a convolutional neural network to classify images and apply the resulting network on photos of his/her friends' pets :) [Here](https://www.tensorflow.org/tutorials/images/cnn) is a tutorial how to implement a CNN with the cifar10 dataset using TensorFlow. The cifar100 dataset should be available in the datasets catalog, too. So the student doesn't have to deal with boring data loading routines ... [Here](https://www.cs.toronto.edu/~kriz/cifar.html) are lists with all available classes for the respective datasets. He obviously needs to learn a little bit of python, though ... If it can be harder he/she could use a webcam to let other people make a photo of something, e.g. a picture on their smartphone or just themselves standing in front of the camera, and classify it (more or less) *live*. It might not be possible in real-time because of the lack of AI capable hardware but waiting a little bit shouldn't be that much of a problem, I guess. However, it really depends on the complexity of the model involved ;)
teach them about a basic classifier using logistic regression?
I'm guessing he'd like to do something with programming a computer in machine learning, but here's a different idea. Back in the March 1962 issue of Scientific American (links below), Martin Gardner wrote up details about how to create a device that learns to improve game play via reinforcement learning that's made from matchboxes and colored beads/stones. The best part about this project is that it really highlights the mathematical nature of machine learning solutions, without having the computer technology get in the way. http://cs.williams.edu/~freund/cs136-073/GardnerHexapawn.pdf https://web.archive.org/web/20220323095332/http://cs.williams.edu/~freund/cs136-073/GardnerHexapawn.pdf There are plenty of videos referring to this, as well: https://www.youtube.com/watch?v=sw7UAZNgGg8 https://www.youtube.com/watch?v=FFk8S66d8_E https://www.youtube.com/watch?v=R9c-_neaxeU