ENGS 108: Applied Machine Learning

Offered

17F: 12
Cybenko

Textbooks  
Available Online: (1) Candidate Textbook: Goodfellow; Ian; Yoshua Bengio; and Aaron Courville. Deep learning. MIT Press; 2016. (2) Software: MATLAB; TensorFlow; Keras; R; Python

18F: 12
Cybenko

Prerequisites

Because of the large overlap in material covered, no student will receive credit for both ENGS 108 and COSC 74/174.

This course will introduce students to modern machine learning techniques as they apply to engineering and applied scientific and technical problems.  Techniques such as recurrent neural networks, deep learning, reinforcement learning and online learning will be specifically covered.  Theoretical underpinnings such as VC-Dimension, PAC Learning and universal approximation will be covered together with applications to audio classification, image and video analysis, control, signal processing, computer security and complex systems modeling.  Students will gain experience with state-of-the-art software systems for machine learning through both assignments and projects