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

Quarter: Summer
Day(s): Tuesdays
Course Format: On-campus
Duration: 5 weeks
Date(s): Jul 10—Aug 14
Time: 7:00—8:50 pm
Drop Deadline: Jul 23
Unit: 1
Tuition: $355
Instructor(s): Ronjon Nag, Sohila Zadran
Status: Open
Please Note: No class on July 31
Summer
On-campus
Tuesdays
7:00—8:50 pm
Date(s)
Jul 10—Aug 14
5 weeks
Drop By
Jul 23
1 Unit
Fees
$355
Instructor(s):
Ronjon Nag, Sohila Zadran
Open
Please Note: No class on July 31
Artificial intelligence (AI) is inspired by our understanding of how the human brain learns and processes information and has given rise to powerful AI techniques known as neural networks and deep learning. Much of deep learning in artificial intelligence uses the neuron, the cellular unit of the brain, as its biological inspiration. This course will provide an introductory overview of artificial intelligence techniques and emphasize neural networks and deep learning. We will discuss how current AI platforms compare and differ from how the brain works, how systems actually “learn,” and how to build a neural network. We will also discuss the real-world applications of neural networks. By the end of the course, students should have enough basic understanding of how AI techniques work so they can converse with neural network practitioners and company executives, critically evaluate AI news stories and technologies, and 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 middle-school math (e.g., simple algebra) is expected, with any further material covered in class. Students are not required to complete SCI 48: “An Introduction to Artificial Intelligence: How Neuroscience Is Creating Smarter Technologies” prior to this course. However, prospective students should review both syllabi to determine which course best suits their needs.

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.

Sohila Zadran, Neuroscientist

Sohila Zadran received a PhD in neuroscience from USC and was a postdoctoral fellow at Caltech. She has founded four biotech companies and serves as an advisor to several companies and to accelerators including SkyDeck and QB3. She has also worked for various Silicon Valley venture capital firms and recently joined Khosla Ventures.

Textbooks for this course:

(Required) Tariq Rashid, Make Your Own Neural Network (ISBN 978-1530826605)
DOWNLOAD THE PRELIMINARY SYLLABUS » (subject to change)