Learn the most modern techniques for supervised learning, used in common applications such as facial recognition, speech recognition, and self driving cars. This course will also provide students with a linux server with GPU acceleration to run their algorithms. Topics include regression, test classification, convolutional image recognition, and more.
Completion of [CORE 5b] or AP CS, or permission of instructor. Also requires Algebra II math experience. [AI 1] highly recommended but not required.
These are examples of projects that students create as they grow their skills in [AI 2]
Introduction to Neural Networks
In this class we'll learn about the Perceptron as the building block for neural networks and deep learning
Introduction to TensorFlow
In this class we'll start exploring how to use the TensorFlow library.
More TensorFlow, Intro to Keras
In this class we'll keep working with TensorFlow and start learning to use Keras.
Working with Images
In this lesson we'll start learning how to process images with our ML architectures, including running a model for image ID or generation.
Convolutional Neural Networks (CNN)
Today we'll start exploring a new type of neural network and learn about regularizations
Transfer learning allows us to copy effective parts of existing models. Today we will also introduce the midterm project.
Midterm Project: Image Classification
Finish Midterm Project, presentations
Students will present their work from the midterm project. Class discussion on topics to cover in Unit 2 in order to meet student goals.
Recurrent Neural Networks (RNNs)
RNNs can be used to make predictions about time series data, like words in a sentence. Topic may change depending on student goals for Unit 2.