2023 brought massive breakthroughs in artificial intelligence. Over the course of the year we were honored to hear from some of the most influential thinkers in the field.
Videos in this playlist 22
Ask About AI: Professor Chris Manning Answers AI-Generated Questions
Ask About AI: Professor Chris Manning Answers Your AI Career Questions
Ask About AI: Professor Chris Manning on the Future of AI
Ask About AI: Professor Chris Manning on Foundation Models
Ask About AI: Professor Chris Manning on Explainability in AI Models
Stanford Professor Chris Manning: Ask About AI - Full Version
Andrew Ng and Fei-Fei Li Discuss Human-Centered Artificial Intelligence - Stanford Online
What advice do you have for getting started in AI & Machine Learning? - Fei-Fei Li & Andrew Ng
Andrew Ng and Chris Manning Discuss Natural Language Processing
What does the future hold for AI and Robotics? - Chelsea Finn & Andrew Ng
Is a Career in AI and Machine Learning Right for Me? - Fei-Fei Li & Andrew Ng
What inspired you to choose AI and Robotics as a profession? - Chelsea Finn & Andrew Ng
What is our ethical responsibility with AI & Machine Learning? - Fei-Fei Li & Andrew Ng
What inspired you to study Natural Language Processing? - Chris Manning & Andrew Ng
AI and Robotics - Chelsea Finn & Andrew Ng
What advice do you have for someone breaking in to AI? - Chris Manning & Andrew Ng
What does the future hold for Natural Language Processing? - Andrew Ng & Chris Manning
What is it like working in an AI lab? - Chelsea Finn & Andrew Ng
Andrew Ng: Opportunities in AI - 2023
Stanford Webinar - GPT-3 & Beyond
Stanford Webinar - The Frontier of Deep Learning for Robotics, Chelsea Finn
Stanford Webinar - Building Safe and Reliable Autonomous Systems
Advantages and disadvantages
Advantages
Free and complete: every lecture is in the playlist, in order.
Watch at 1.5x, skip what you know, rewatch what you don't.
The actual Stanford lectures (CS229, CS224N, CS231N…): the deepest free material on AI anywhere.
Taught by Stanford University, one of the strongest names in its field.
2,022,223 views, so help and notes are easy to find.
Completely free.
Self-paced: start any time.
A clear syllabus (22 videos) you can see before you start.
Disadvantages
No certificate, deadlines or graded work.
Quality and depth vary: check that the playlist is finished before you start.
Graduate level: needs linear algebra, probability and programming first.
No certificate.
No graded assignments or feedback.
Some points apply to every course of this kind; see how we rank.
Free
Free: Watch on YouTube, no account needed.
Certificate: None. Code or take notes along to make it stick.
MIT 18.650 Statistics for Applications, Fall 2016 View the complete course: http://ocw.mit.edu/18-650F16 Instructor: Philippe Rigollet This course offers an in-depth the theoretical foundations for statistical methods that are useful in many applications. The goal is to understand the role of mathe…
-Learn more and enroll in the program: https://online.stanford.edu/programs/generative-ai-technology-business-and-society-program Recent advancements in generative AI are reshaping industries, pushing technological boundaries, and revolutionizing creative processes. In this fast-changing landscape…
For more information about Stanford's Artificial Intelligence programs visit: https://stanford.io/ai Delve into the exciting world of Artificial Intelligence with insights and research from the world's top experts. This playlist offers a comprehensive journey, from foundational machine learning con…
Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (cluster…
View the complete course: http://ocw.mit.edu/6-034F10 Instructor: Patrick Winston In these lectures, Prof. Patrick Winston introduces the 6.034 material from a conceptual, big-picture perspective. Topics include reasoning, search, constraints, learning, representations, architectures, and probabili…