MIT 18.642 Topics in Mathematics with Applications in Finance, Fall 2024
Instructors: Vasily Strela, Jake Xia, and Peter Kempthorne
View the complete course: https://ocw.mit.edu/courses/18....-642-topics-in-mathe
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

This video provides an introductory overview of a course combining mathematical theory and real-world financial applications, featuring lectures by academics and industry experts. The instructors emphasize the practical use of mathematics in finance, covering topics like bond math, portfolio optimization, and machine learning, while utilizing tools such as RStudio Cloud for data analysis. Additionally, the course includes guest speakers from prominent financial institutions, offering students exposure to cutting-edge quantitative finance concepts and industry practices.

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Lecture 2: Science and Research
Instructor: John Gabrieli
View the complete course: http://ocw.mit.edu/9-00SCS11

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MIT 15.773 Hands-On Deep Learning Spring 2024
Instructor: Rama Ramakrishnan
View the complete course: https://ocw.mit.edu/courses/15....-773-hands-on-deep-l
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

Introduction to natural language processing, including vectorization, the bag-of-words model, and includes demonstration in CoLab.

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MIT 6.100L Introduction to CS and Programming using Python, Fall 2022
Instructor: Ana Bell

View the complete course: https://ocw.mit.edu/courses/6-....100l-introduction-to
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

This lecture discusses the core elements of programs: strings, input/output, f-strings, operators, branching, and indentation. Big idea: Debug early, debug often. Write a little and test a little. Don’t write a complete program at once. It introduces too many errors. Use the Python Tutor to step through code when you see something unexpected!

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MIT 22.01 Introduction to Nuclear Engineering and Ionizing Radiation, Fall 2016
Instructor: Michael Short
View the complete course: https://ocw.mit.edu/22-01F16
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

Prof. Short uses all the concepts introduced thus far to introduce the study of nuclear materials and radiation damage - his field of study. The concept of ionizing radiation creating nuclear displacements, not just electron ionization, is introduced as the first event in radiation damage. The structural defects produced from these displacements are shown to cluster, move, and evolve, resulting in drastic changes to material properties. Key structural material properties and their formal definitions are introduced and demystified by watching a pair of Finnish scientists smash various items with a 50 ton hydraulic press.

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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.

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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.

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MIT 18.642 Topics in Mathematics with Applications in Finance, Fall 2024
Instructor: Peter Kempthorne
View the complete course: https://ocw.mit.edu/courses/18....-642-topics-in-mathe
YouTube Playlist: https://www.youtube.com/playli....st?list=PLUl4u3cNGP6

The lecture introduces linear algebra with a focus on its applications in quantitative finance, covering vector and matrix fundamentals, portfolio valuation, and concepts like short selling, arbitrage, and contingent claims. It further explores stochastic matrices and Markov chains, eigenvalues and eigenvectors, and their roles in modeling financial markets, culminating in discussions on no-arbitrage conditions, market completeness, and pricing measures essential for option pricing theory.

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MIT 15.401 Finance Theory I, Fall 2008
View the complete course: http://ocw.mit.edu/15-401F08
Instructor: Andrew Lo

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Course details: https://stanford.io/4qjWTCk

Think back to the first time you had to tell a direct report their work wasn’t good enough, or persuade your supervisor to change course. For most new managers, those difficult conversations might not have gone as planned. But research shows these early moments can shape the trajectory of your entire career. The good news is you don’t have to wait for the next high-stakes conversation to get it right.

In this course, Stanford faculty combine decades of research with the emergent superpowers of generative AI to give you a “practice field” for leadership. You will rehearse critical conversations in a low-stakes environment and gain the skills and confidence to lead with authority and empathy.

You will learn to:
- Craft clear, respectful communication strategies that shift outcomes
- Give and receive constructive feedback that accelerates growth

Using generative AI, you will role-play high-pressure situations such as confronting difficult team members, motivating diverse talent, or disagreeing with your manager. Each practice round gives you structured, real-time feedback on tone, presence, and empathy so you can iterate and improve.

The course begins with three preset scenarios drawn from the experiences of Stanford alumni.

For each, you will:
- Observe: Hear how young leaders confronted real challenges
- Practice: Role-play with an AI partner as your conversational counterpart
- Get Feedback: Receive personalized feedback from our tough conversation AI coach
-Reflect and Repeat: Apply insights, refine your approach, and try again

Finally, you will design your own AI conversation partner for a current workplace challenge, building a personalized tool you can use beyond the course. Along the way, Stanford faculty share research, alumni share lived experiences, and you gain the playbook to shift your career trajectory.

Please note: This course requires a free ChatGPT account to participate in practice activities.

For more information about Stanford's online Artificial Intelligence programs visit: https://stanford.io/ai

To learn more about enrolling in this course visit: https://online.stanford.edu/co....urses/cs336-language

To follow along with the course schedule and syllabus visit: https://stanford-cs336.github.io/spring2025/

Percy Liang
Associate Professor of Computer Science
Director of Center for Research on Foundation Models (CRFM)

Tatsunori Hashimoto
Assistant Professor of Computer Science

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

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

January 9, 2026
This lecture covers:
• How the ongoing integration of LLMs into our social worlds is creating new risks and opportunities
• Best practices for designing rehabilitative bots

To follow along with the seminar schedule, visit: https://hci.stanford.edu/

Jeremy Foote is an Assistant Professor in the Brian Lamb School of Communication at Purdue University and a faculty member in the Community Data Science Collective.

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 online Artificial Intelligence programs visit: https://stanford.io/ai

This lecture covers:
1. The course (10 mins)
2. Human language and word meaning (15 mins)
3. Word2vec introduction (15 mins)
4. Word2vec objective function gradients (25 mins)
5. Optimization basics (5 mins)
6. Looking at word vectors (10 mins or less)

Key learning: The (astounding!) result that word meaning can be represented rather
well by a (high-dimensional) vector of real numbers

To learn more about enrolling in this course visit: https://online.stanford.edu/co....urses/cs224n-natural

To follow along with the course schedule and syllabus visit: hhttps://web.stanford.edu/class..../archive/cs/cs224n/c

Professor Christopher Manning
Thomas M. Siebel Professor in Machine Learning, Professor of Linguistics and of Computer Science
Director, Stanford Artificial Intelligence Laboratory (SAIL)

Statistical Learning, featuring Deep Learning, Survival Analysis and Multiple Testing

Trevor Hastie, Professor of Statistics and Biomedical Data Sciences at Stanford University - https://statistics.stanford.ed....u/people/trevor-j-ha

Robert Tibshirani, Professor of Statistics and Biomedical Data Sciences at Stanford University - https://statistics.stanford.ed....u/people/robert-tibs

Jonathan Taylor, Professor Statistics at Stanford University - https://statistics.stanford.ed....u/people/jonathan-ta

You are able to take Statistical Learning as an online course on EdX, and you are able to choose a verified path and get a certificate for its completion. You can choose to take the course in R (https://www.edx.org/course/statistica) or in Python (https://www.edx.org/learn/data....-analysis-statistics

For more information about courses on Statistics, you can browse our Stanford Online Catalog: https://stanford.io/3QHRi72