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 CS01b or AP CS, or permission of instructor. Also requires Algebra II math experience. CS82 highly recommended but not required.
Post-Undergraduate / Research Grade Tools, Modeling with Keras and Tensorflow
Cutting edge techniques from resesarch in the last 2-5 years, e.g. Deep Convolutional Networks and Object Detection / Localization
GPU Compute Resources for Class include Titan Xp, GTX 1080ti, 32 Virtual Core Machine with 128GB RAM
Word Vectorization, Natural Language Classification, and broad coverage of different types of data sets
Linux tools, compute servers, provided in class. Students learn how to ask the right questions and perform research independently
Independent Student Projects can be submitted to science fairs or continued on in CS85