This course covers the fundamentals of neural networks and deep learning as well as how they are used to address many artificial intelligence problems in society. Students will learn to design and implement multi-layered neural network architectures, train them on large amounts of data, and evaluate their performance. Included will be examination of popular architectures such as fully connected networks, convolutional neural networks, recurrent neural networks, and transformers, alongside learning strategies such as backpropagation, initialization, and regularization. Students will also gain practical, hands-on experience by applying learned skills to analyze visual data (computer vision) and textual data (natural language processing).
instructor(s)
Gates, Ami
Primary Instructor
- Fall 2022 / Fall 2023 / Fall 2024
Gurari, Danna
Primary Instructor
- Spring 2022 / Fall 2022 / Spring 2024