CMPS144: Applied Machine Learning

This course provides a practical and project-oriented introduction to machine learning, with an emphasis on neural networks and deep learning. The course will start with a discussion of foundational pieces of statistical inference. Then, we introduce the basic elements of machine learning: loss functions and gradient descent. With these, we first present logistic regression, or one-layer networks, and then move on to more complex models: deep neural networks, convolutional networks for image recognition, and recurrent networks and LSTM for temporal and sequence data. The course also covers the basics of dataset preparation and visualization, and the performance characterization of the models created. The course has weekly homework, and a final project that can be done in groups. Prerequisite(s): CMPS 101. Enrollment restricted to juniors and seniors.

5 credits

Year	Fall	Winter	Spring	Summer

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