𝐶𝑎𝑟𝑎𝑚𝑖𝑧𝑎𝑟𝑢'𝑠 𝐵𝑙𝑜𝑔

Robot Learning 2025: Foundational Models for Robotics and Scaling DeepRL - created

admin@undisclosed.example.com (Horea Caramizaru) — Sat, 18 Apr 2026 17:41:19 +0000

Robot Learning 2025: Foundational Models for Robotics and Scaling DeepRL

Course playlist

Labi Siffre - Bless the Telephone

admin@undisclosed.example.com (Horea Caramizaru) — Fri, 06 Mar 2026 18:14:52 +0000

Labi Siffre - Bless the Telephone

Labi-Siffre, song

DeepMind x UCL | Introduction to Reinforcement Learning 2015

admin@undisclosed.example.com (Horea Caramizaru) — Mon, 02 Feb 2026 22:37:37 +0000

DeepMind x UCL | Introduction to Reinforcement Learning 2015

Full course

Access slides, assignments, exams, and more info about the course at: Course Web Page < redirection to >. ( backup link for course slides: Link )

To learn more about DeepMind's ongoing research with reinforcement learning, visit our blog: DeepMind blog.

For a powerful illustration of reinforcement learning in action, watch the AlphaGo documentary.

Solutions:

Learning, RL, Robotics, Control, David-Silver, UCL, 2015

GIAN course on Advances in Mixed-Integer Nonlinear Optimization

admin@undisclosed.example.com (Horea Caramizaru) — Tue, 30 Dec 2025 12:47:34 +0000

GIAN course on Advances in Mixed-Integer Nonlinear Optimization

Gil Strang's Final 18.06 Linear Algebra Lecture

admin@undisclosed.example.com (Horea Caramizaru) — Mon, 29 Dec 2025 23:52:18 +0000

Gil Strang's Final 18.06 Linear Algebra Lecture

After being with MIT for almost 66 years, at the age of 88, the legendary Prof. Strang had today his last Linear Algebra Lecture.

Thank you for the great service you have done to the math community and thank you, for making me and many other math enthusiasts, enjoy so much this topic! To this day, I remember vividly the time when I found your lectures. It was ~2006 and MIT OpenCourseWare was still in its infancy… I remember being stuck in front of my computer for hours enjoying your lectures! My life has never been the same since then

journal, Strang, MIT, MIT-OpenCourseWare, 2023

Introduction to Flow Matching and Diffusion Models

admin@undisclosed.example.com (Horea Caramizaru) — Mon, 17 Nov 2025 23:44:42 +0000

Introduction to Flow Matching and Diffusion Models

MIT Computer Science Class 6.S184: Generative AI with Stochastic Differential Equations

Diffusion and flow-based models have become the state of the art for generative AI across a wide range of data modalities, including images, videos, shapes, molecules, music, and more! This course aims to build up the mathematical framework underlying these models from first principles. At the end of the class, students will have built a toy image diffusion model from scratch, and along the way, will have gained hands-on experience with the mathematical toolbox of stochastic differential equations that is useful in many other fields. This course is ideal for students who want to develop a principled understanding of the theory and practice of generative AI.

Course Notes

The course notes serve as the backbone of the course and provide a self-contained explanation of all material in the class. In contrast, lecture slides will generally not be self-contained and are intended to provide accompanying visualizations during the lecture. You may view the notes by clicking on the colored link below.

Course Notes

The full video course and slides/course materials can be found here:

Course web page

Robotics, ML, RL, Diffusion-Models, stochastic-differential-equations, SDE, MIT, 2025

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