Spring 2025 Semester Update
For the Spring 2025 semester at Courant, I’ll be taking Advanced Machine Learning with Professor Mehryar Mohri and Reinforcement Learning with Professor Lerrel Pinto. There are a few reasons behind my course selection, but I’ll keep them a secret for now. If it bears fruit, I’ll be sure to share it with you—promise!
For now, let’s talk about what we can expect from these courses and how I’ll map everything out here on the blog:
Machine Learning
This is where it all started for me, and it serves as the foundation for everything else we’ll explore. I’ve already posted about half of the ML course, and I’ll keep adding more content to build a solid understanding based on first principles. On the Blog page, you’ll find a mix of posts on other topics as well. If you’re following the ML series and want to stay focused, just click the “ML-NYU” tag, and you’ll find all the blogs in this series, organized for easy follow-along.
To make things easier for you, I’ll occasionally reintroduce foundational concepts in each post so you won’t have to revisit older blogs to keep up. However, if something doesn’t quite click, feel free to check the previous post for clarification—you should be good to go!
Advanced Machine Learning
This course takes a deep, theoretical approach, focusing on critical analysis and research to build innovative ML algorithms. It’s one of the toughest courses I’m taking, but my goal is to simplify the concepts and make them accessible. We’ll dive deep into the theory, derive algorithm bounds and guarantees, and explore why things are the way they are.
Given the advanced nature of the course, I encourage you to question the material and develop your own intuition at times, as things can get dense and hard to re-explain. But don’t worry—if I feel something needs revisiting, I’ll add references or clarifications. To stay updated with this series, check the “ADV-ML-NYU” tag.
Reinforcement Learning
Reinforcement Learning (RL) has a lot of deep learning prerequisites. If deep learning isn’t your strong suit yet, no worries—we’re in this together. However, having a solid foundation in ML is a must to follow along. If we encounter any complex or new deep learning topics, we’ll break them down in separate blogs to make them easier to understand and revisit.
RL is a practical topic—we’ll be building agents that make decisions. I’m still figuring out the best way to incorporate the hands-on aspects, but let’s see how it goes. For this series, you can track posts using the “RL-NYU” tag.
General Note
If you’re looking for a specific topic, just hit cmd + k to search for keywords across the blog.
That’s all for now! Let’s continue learning and growing together, one step at a time 🙂.