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SCI 52 — Artificial Intelligence: An Introduction to Neural Networks and Deep Learning

Quarter: Winter
Day(s): Tuesdays
Course Format: On-campus
Duration: 7 weeks
Date(s): Jan 15—Feb 26
Time: 7:00—8:50 pm
Drop Deadline: Jan 28
Unit: 1
Tuition: $395
Instructor(s): Ronjon Nag
Status: Open
7:00—8:50 pm
Jan 15—Feb 26
7 weeks
Drop By
Jan 28
1 Unit
Ronjon Nag
Artificial intelligence (AI) is inspired by our understanding of how the human brain learns and processes information and has given rise to powerful techniques known as neural networks and deep learning. This course will provide an introductory overview of artificial intelligence techniques and emphasize neural networks and deep learning. In this course, through pre-built hands-on exercises, we will discuss how current AI platforms compare with how the brain works, how systems actually “learn,” and how to build and apply neural networks. We will also discuss the real-world applications of neural networks. By the end of the course, students will understand how AI techniques work so they can: (1) converse with neural network practitioners and companies; (2) be able to critically evaluate AI news stories and technologies; and (3) consider what the future of AI can hold and what barriers need to be overcome with current neural network models.

This introductory course is open to students of all levels. No computer science or programming experience is needed, but an understanding of simple algebra is expected.

Ronjon Nag, Fellow, Stanford Distinguished Careers Institute

Ronjon Nag has deployed artificial intelligence systems for mobile devices over three decades, working on neural networks at Cambridge, where he received a PhD in engineering; at Stanford, where he was a Harkness Fellow; and at MIT, where he received an MS. In 2014, he received the Mountbatten Medal from the Institution of Engineering and Technology for his contributions to the modern mobile phone industry. Companies he has co-founded or advised have been sold to Motorola, BlackBerry, and Apple.

Textbooks for this course:

(Required) Tariq Rashid, Make Your Own Neural Network (ISBN 978-1530826605)