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

In this video Jake Xia provides an overview of key financial concepts, including market structure, types of financial products like stocks, bonds, derivatives, and the roles of various market participants. He also introduces investment strategies, emphasizing the importance of understanding personal financial goals, risk management, and the use of mathematical models in pricing and trading, culminating in a practical trading game to apply these concepts.

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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

If you want to master AI agents in 2026 and actually build systems that can reason, plan, and take action on their own- this video is your complete roadmap.

I walk you through exactly what you need to learn, in what order, and which free resources will actually get you there- without wasting months chasing hype.

The space feels overwhelming right now. Everyone's throwing around words like agents, autonomy, multi-agent systems, tool use, planning, memory- and it all sounds exciting, but also kind of chaotic.
You're not too late. But you do need a plan. That's exactly what this video gives you.

Chapters:
00:00 – Why You Need a Roadmap for AI Agents
01:08 – Prerequisites: Python, APIs, ML Fundamentals
03:25 – Month 1: Foundations & Architecture
05:17 – Month 2: Agent Frameworks & Memory
06:55 – Month 3: Tools, APIs & Multi-Agent Systems
08:06 – Month 4: Evaluation, Safety & Deployment
08:55 – Month 5-6: Specialization, Advanced Topics & Capstone
10:12 – Final Advice & Resources

Free Resources Mentioned:

Prerequisites-
Google's Python Class: https://developers.google.com/edu/python
Python for Everybody (Charles Severance): https://www.py4e.com/

Month 1: Foundations

Hugging Face Agents Course: https://huggingface.co/learn/agents-course

Month 2: Frameworks & Memory

LangGraph Documentation: https://www.langchain.com/langgraph
CrewAI Documentation: https://docs.crewai.com/

Month 3-4: Tools, Multi-Agent & Production

OpenAI Function Calling Guide: https://platform.openai.com/do....cs/guides/function-c
LangSmith (Evaluation): https://www.langchain.com/langsmith

Month 5-6: Advanced & Capstone

Berkeley LLM Agents Course: https://llmagents-learning.org/f24

Drop a comment: Where are you in your AI agents journey? Just starting, already building, or stuck somewhere in the middle?