Students are introduced to the machine learning landscape, particularly neural nets. Students learn the use of Scikit-Learn to track an example machine-learning project end-to-end. Students explore several training models, including support vector machines, decision trees, random forests, and ensemble methods. Students are introduced to the TensorFlow library to build and train neural nets, neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning. Students learn techniques for training and scaling deep neural nets. Prerequisite: ADSC 3710 (min. grade of C)
Students are introduced to the machine learning landscape, particularly neural nets. Students learn the use of Scikit-Learn to track an example machine-learning project end-to-end. Students explore several training models, including support vector machines, decision trees, random forests, and ensemble methods. Students are introduced to the TensorFlow library to build and train neural nets, neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning. Students learn techniques for training and scaling deep neural nets. Prerequisite: ADSC 3710 (min. grade of C)
