I'm looking to dive deep into the world of Artificial Intelligence and I have a solid foundation in applied math and computer science, having double majored in both during my undergraduate studies. However, I'm struggling to find the right resources. Most content online seems geared toward quick bootcamps or practical tutorials on specific projects, like building an LLM, but I'm really after something that covers the theory and fundamentals comprehensively. What would be the best way to rigorously study AI on my own, focusing on the core concepts and deeper understanding rather than surface-level skills?
6 Answers
Not sure if this is on-track, but Anthropic has some great resources for AI learning. Check out their site for some engaging courses and topics—they might pique your interest! Specific links like their engineering section and course offerings could provide you some valuable insights.
Since you are already experienced in academia, treat your self-study like another thesis. Use Google Scholar to find relevant papers—mix introductory and niche papers along with toolkits and supportive books. This way you'll continuously enrich your understanding.
When searching for courses, try focusing on machine learning rather than broadly looking for AI courses. The term "AI" often leads to contemporary topics like LLMs, while "ML" will connect you to the foundational math and theory.
You're in a great position with your background! Start by grabbing "Artificial Intelligence: A Modern Approach" by Russell and Norvig. It's the go-to textbook for a lot of AI courses and dives into everything from algorithms to neural networks with solid theory. For machine learning, check out "Pattern Recognition and Machine Learning" by Bishop or "Elements of Statistical Learning"—both are heavy on the math, which is what you want.
You can also take Stanford's CS229 course online for free. Andrew Ng covers essential topics, including the math foundations. MIT’s OpenCourseWare is another gold mine for AI materials. Just tackle the syllabi from these universities in the same order they teach them. Goodfellow’s "Deep Learning" is also a fantastic deep dive once you have the basics down.
When you're ready to explore specific areas, dive into research papers—ArXiv is perfect for this. Implementing the theories from scratch will solidify what you learn. And don't forget to do the exercises in your textbooks; just reading isn't enough—you need hands-on problem-solving!
Absolutely! Those were key texts for my studies too, especially AIMA.
Honestly, you might need to consider going back to school for a structured program if you really want to dig deep into AI. But, if that's not an option, there's also the route of building projects. Set a specific goal, like creating a generative AI for something cool, then investigate ways to improve it. I learn a ton through hands-on projects. But, it's just one method.
If you want rigor, definitely head to MIT's OpenCourseWare. They offer graduate-level content, including lectures and practical lab work. It's one of the best free options you'll get for a thorough learning experience.
Thanks for the thorough recommendations. This is exactly the direction I was hoping to find!