Units:

12

Description:

Neural networks have increasingly taken over various AI/ML tasks, and currently produce the state of the art in many tasks ranging from computer vision and planning for self-driving cars to playing computer games. Basic knowledge of NNs, known currently in the popular literature as "deep learning", familiarity with various formalisms, and knowledge of tools, is now an essential requirement for any researcher or developer in most AI and NLP fields. This course is a broad introduction to the field of neural networks and their "deep" learning formalisms. The course traces some of the development of neural network theory and design through time, leading quickly to a discussion of various network formalisms, including simple feedforward, convolutional, recurrent, and probabilistic formalisms, the rationale behind their development, and challenges behind learning such networks and various proposed solutions. We subsequently cover various extensions and models that enable their application to various tasks such as computer vision, speech recognition, machine translation and playing games.

Anti-requisites: 11685, 11785, 18-780, 11485

Last Modified: 2024-11-22 1:24PM

Semesters offered:

• Spring 2025
• Spring 2024
• Spring 2023
• Fall 2021
• Spring 2021
• Fall 2020