MIT 6.868J The Society of Mind, Fall 2011
View the complete course: http://ocw.mit.edu/6-868JF11
Instructor: Marvin Minsky

In this lecture, students discuss Chapter 1 of The Emotion Machine, covering topics such as love, infatuation, and the Self.

License: Creative Commons BY-NC-SA
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More courses at http://ocw.mit.edu

MIT 8.04 Quantum Physics I, Spring 2013
View the complete course: http://ocw.mit.edu/8-04S13
Instructor: Allan Adams

In this lecture, Prof. Adams gives a panoramic view on various experimental evidence that indicates the inadequacy of pre-quantum physics. He concludes the lecture with a short discussion on Bell's inequality.

License: Creative Commons BY-NC-SA
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Episode Summary: MIT professor Andrew W. Lo tackles AI-assisted financial advising, healthcare, and the effect of human behavior on financial markets.

Episode Description: In this the first of two pilot episodes of Chalk Radio with VIDEO, Professor Andrew Lo, who teaches finance at MIT’s Sloan School of Management, knows that many people find financial matters perplexing and scary. Lots of us don’t have a good head for numbers, and besides, how can one get advice and make sound decisions when it’s taboo to discuss one’s finances at all? That’s where a financial advisor is useful–someone who understands the concepts, can crunch the numbers, and has a fiduciary responsibility to look out for your best interests. For many people, hiring a financial advisor might be a financial impossibility, but Prof. Lo and his colleagues are working to develop an AI financial advisor that not only gives ordinary people access to sound financial advice, but acts with real fiduciary responsibility. Large language models can’t do this yet, he says, but the technology is developing fast. Other topics he touches on in this episode include the outsized influence of finance on drug development and global decarbonization and the equally outsized influence of teachers on their students–he names many who changed his own life, from his third-grade teacher in Queens to his professors at college and graduate school.

Subscribe to Chalk Radio on podcast platforms ➜ https://chalk-radio.simplecast.com

Relevant Resources:
MIT OpenCourseWare (https://ocw.mit.edu)
The OCW Educator portal (https://ocw.mit.edu/educator)
Professor Lo’s faculty page (https://mitsloan.mit.edu/facul....ty/directory/andrew-
15.401 Finance Theory I on MIT OpenCourseWare (https://ocw.mit.edu/courses/15....-401-finance-theory-
15.481x Adaptive Markets: Financial Market Dynamics and Human Behavior on MIT Open Learning Library (https://openlearninglibrary.mi....t.edu/courses/course
15.482x Healthcare Finance on MIT Open Learning Library (https://openlearninglibrary.mi....t.edu/courses/course
Music in this episode by Blue Dot Sessions (https://www.sessions.blue/)

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0:01 Intro
0:51 Why Finance Matters
3:02 Inflation, and practical finance applications to mitigate rising costs
5:19 Can ChatGPT reliably plan someone's retirement?
7:54 How to deal with AI hallucinations
9:14 Financial planning - why you need to start early!
9:58 Finances - a taboo topic?
11:07 AI Finance tools and ethics
12:31 Will AI take people's jobs?
13:18 Finance for positive impact on people & healthcare - Andrew's origin story
16:47 How Finance could help Climate
18:48 'It all comes down to money'
19:22 How human behavior affects Finance
19:47 How humans react to a market crash
21:12 Andrew's Adaptive Markets Hypothesis
22:02 How can we counteract irrational human tendencies?
24:00 How Andrew makes finance accessible through his teaching
24:55 Andrew's education and identifying different types of intelligence
28:31 Andrew's learning disorder and how teachers helped him manage it
35:08 Andrew's meaningful memento
38:17 Conclusion


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Credits:
Sarah Hansen, host and producer
Brett Paci, producer
Jackson Maher, producer
Dave Lishansky, producer
Peter Chipman, show notes

MIT 15.S21 Nuts and Bolts of Business Plans, IAP 2014
View the complete course: http://ocw.mit.edu/15-S21IAP14
Instructor: Bob Jones

This session will discuss these issues and provide guidance on how to approach the marketing section of your business plan.

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

MIT 8.04 Quantum Physics I, Spring 2013
View the complete course: http://ocw.mit.edu/8-04S13
Instructor: Allan Adams

In this lecture, Prof. Adams introduces wave functions as the fundamental quantity in describing quantum systems. Basic properties of wavefunctions are covered. Uncertainty and superposition are reiterated in the language of wavefunctions.

License: Creative Commons BY-NC-SA
More information at http://ocw.mit.edu/terms
More courses at http://ocw.mit.edu

In an effort to spread knowledge and promote life-long learning, Stanford University has put extensive efforts into offering free online courses available to anyone, anywhere.

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

October 28, 2025
This lecture provides walkthroughs of examples of AI projects and making day-to-day decisions in building AI systems.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

To follow along with the course schedule and syllabus, visit: https://cs230.stanford.edu/syllabus/

More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

NOTE: There was no class on November 4, 2025 (Lecture 7). The next lecture is Lecture 8.

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

November 11, 2025
This lecture covers agents, prompts, and RAG.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

Please follow along with the course schedule and syllabus: https://cs230.stanford.edu/syllabus/

More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

NOTE: There was no class on November 4, 2025 (Lecture 7). The previous lecture is Lecture 6.

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai

October 21, 2025
This lecture covers deep reinforcement learning.

To learn more about enrolling in this course, visit: https://online.stanford.edu/co....urses/cs230-deep-lea

To follow along with the course schedule and syllabus, visit: https://cs230.stanford.edu/syllabus/

More lectures will be published regularly.
View the playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

Andrew Ng
Founder of DeepLearning.AI
Adjunct Professor, Stanford University’s Computer Science Department

Kian Katanforoosh
CEO and Founder of Workera
Adjunct Lecturer, Stanford University’s Computer Science Department

For more information about Stanford’s graduate programs, visit: https://online.stanford.edu/graduate-education

November 21, 2025
This lecture covers:
• LLM-as-a-judge overview
• Best practices and benefits
• Biases and pitfalls

To follow along with the course schedule and syllabus, visit: https://cme295.stanford.edu/syllabus/

Chapters:
00:00:00 Introduction
00:07:08 Inter-rater agreement metrics
00:18:24 Rule-based metrics
00:21:00 METEOR, BLEU ROUGE
00:28:00 LLM-as-a-judge
00:33:44 Structured outputs
00:36:48 Variants
00:38:47 Position, verbosity, self-enhancement bias
00:47:22 Best practices
00:54:06 Factuality
01:00:15 Agent evaluation
01:23:50 Benchmarks
01:25:12 Knowledge with MMLU
01:29:34 Reasoning AIME, PIQA
01:33:57 Coding with SWE-bench
01:36:15 Safety with HarmBench
01:40:51 Agents with Tau-Bench

Afshine Amidi is an Adjunct Lecturer at Stanford University.

Shervine Amidi is an Adjunct Lecturer at Stanford University.

View the course playlist: https://www.youtube.com/playli....st?list=PLoROMvodv4r

https://github.com/aamini/introtodeeplearning

Lab Materials for MIT 6.S191: Introduction to Deep Learning - aamini/introtodeeplearning

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An overview of Deep Learning, including representation learning, families of neural networks and their applications, a first look inside a deep neural network, and many code examples and concepts from TensorFlow. This talk is part of a ML speaker series we recorded at home. You can find all the links from this video below. I hope this was helpful, and I'm looking forward to seeing you when we can get back to doing events in person. Thanks everyone!

Chapters:
0:00 - Intro and outline
1:42 - TensorFlow.js demos + discussion
3:58 - AI vs ML vs DL
7:55 - What’s representation learning?
8:40 - A cartoon neural network (more on this later)
9:20 - What features does a network see?
10:47 - The “deep” in “deep learning”
12:48 - Why tree-based models are still important
13:38 - How your workflow changes with DL
14:02 - A couple illustrative code examples
17:59 - What’s a hyperparameter?
19:44 - The skills that are important in ML
20:48 - An example of applied work in healthcare
21:58 - Families of neural networks + applications
28:55 - Encoder-decoders + more on representation learning
32:45 - Families of neural networks continued
35:50 - Are neural networks opaque?
38:29 - Building up from a neuron to a neural network
49:11 - A demo of representation learning in TF Playground
53:24 - Importance of activation functions
54:36 - What’s a neural network library?
58:43 - Overfitting and underfitting
1:02:38 - Autoencoders (and anomaly detection) screencast and demo
1:12:13 - Book recommendations

Here are three helpful classes you can check out to learn more:

Intro to Deep Learning from MIT → http://goo.gle/3sPj8To
MIT Deep Learning and Artificial Intelligence Lectures → https://goo.gle/3qh7H54
Convolutional Neural Networks for Visual Recognition from Stanford → http://goo.gle/3bbC34I

And here are all the links to demos and code from the video, in the order they appeared:

Face and hand tracking demos → http://goo.gle/2WTCwSc
Teachable machine demo → https://goo.gle/3bSCzCi
What features does a network see? → http://goo.gle/3e2zpA5
DeepDream tutorials → http://goo.gle/3bYIBTp and http://goo.gle/384B6JC
Hyperparameter tuning with Keras Tuner → http://goo.gle/2InBK7J
Development and Validation of a Deep Learning Algorithm for Detection of Diabetic Retinopathy in Retinal Fundus Photographs → http://goo.gle/309pMY5
Linear (and deep) regression tutorial → http://goo.gle/3sKxkN7
Image classification with a CNN tutorial → http://goo.gle/3qdD2Wb
Audio recognition tutorial → http://goo.gle/3kFpl1j
Transfer learning tutorial → http://goo.gle/3bV7D60
RNN tutorial (sentiment analysis / text classification) → http://goo.gle/3bVM1X7
RNN tutorial (text generation with Shakespeare) → http://goo.gle/3qmnrnz
Timeseries forecasting tutorial (weather) → http://goo.gle/3ecdYg9
Sketch RNN demo (draw together with a neural network) → http://goo.gle/3bbHTTy
Machine translation tutorial (English to Spanish) → http://goo.gle/3e7IJme
Image captioning tutorial → http://goo.gle/3sKFNQz
Autoencoders and anomaly detection tutorial → http://goo.gle/30aD0UA
GANs tutorial (Pix2Pix) → http://goo.gle/3kI1ZrB
A Deep Learning Approach to Antibiotic Discovery → https://goo.gle/3e7ivQD
Integrated gradients tutorial → http://goo.gle/2PxfRtq and http://goo.gle/3sE0bmq
TensorFlow Playground demos → http://goo.gle/2Px6rhB
Introduction to gradients and automatic differentiation → http://goo.gle/3sFVybo
Basic image classification tutorial → http://goo.gle/3c2AF3o
Overfitting and underfitting tutorial → http://goo.gle/3cdA9Qv
Keras early stopping callback → http://goo.gle/308XQUj
Interactive autoencoders demo (anomaly detection) → http://goo.gle/3kPfW7q
Deep Learning with Python, Second Edition → http://goo.gle/3qcQ5Y5
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition → http://goo.gle/386DKP4
Deep Learning book → http://goo.gle/3c2VQmd

Find Josh on Twitter → https://goo.gle/308Ve8P

Subscribe to TensorFlow → https://goo.gle/TensorFlow

Deep learning has shown remarkable success in processing and understanding unstructured data like text and images. In this module, we will explore how deep neural networks can be leveraged to build intelligent systems in the domains of natural language processing and computer vision. Get ready to dive into the exciting world of deep learning on text and images!

We will start with the agenda and course overview. Then, we will review some key machine learning concepts, including regression, optimization, regularization, and text and data representation.

--------------------

This video is part of Machine Learning University’s open-source "Application of Deep Learning to Text and Image Data" series, part of our Fundamentals of Machine Learning content. Access the full set of lessons, hands-on labs, and Jupyter Notebooks here:

🔗 GitHub Repository: https://github.com/aws-mlu/aws....-mlu-eep-traditional
🔗 Watch More MLU Videos: https://www.youtube.com/@machinelearninguniversity

Machine Learning University provides free, open-source AI/ML educational content for educators. New content is released regularly — subscribe to stay updated.

A brief introduction into the concepts behind deep learning.

Github repo:
https://github.com/mlberkeley/intro-dl-workshop

Deep learning currently provides state-of-the-art performance in computer vision, natural language processing, and many other machine learning tasks. In this talk, we will learn when deep learning is useful (and when it isn't!), how to implement some simple neural networks in Python using Theano, and how to build more powerful systems using the OpenDeep package.

Our first model will be the 'hello world' of deep learning - the multilayer perceptron. This model generalizes logistic regression as your typical feed-forward neural net for classification.

Our second model will be an introduction to unsupervised learning with neural nets - the denoising auto-encoder. This model attempts to reconstruct corrupted inputs, learning a useful representation of your input data distribution that can deal with missing values.

Finally, we will explore the modularity of neural nets by implementing an image-captioning system using the the OpenDeep package.

Markus Beissinger
Recent graduate from the Jerome Fisher Program in Management and Technology dual degree program at the University of Pennsylvania (The Wharton School and the School of Engineering and Applied Science), and current Master's student in computer science. Focus on machine learning, startups, and management.

Slides: http://goo.gl/P9QGnV

Sebastian's books: https://sebastianraschka.com/books/
The lecture slides are available at: https://github.com/rasbt/stat4....53-deep-learning-ss2

Introduces the main ideas behind Convolutional Neural Networks (CNNs). The topics are:

Challenges of Image Classification
Convolutional Neural Network Basics
CNN Architectures
What a CNN Can See
CNNs in PyTorch