Kaichen Xu

Kaichen Xu

Generative AI for Serendipity

Resources

When I guided the machine learning beginners and lower-year undergraduates in starting their research, I noticed that while many students have a strong interest in machine learning technology, their insufficient programming or mathematical skills often hinder their research progress. Therefore, based on my research experience, I have compiled a list of recommended books from different fields that are worth studying. Additionally, there are many other well-known and insightful books that are not included below, either because I have not read them or do not have access to public resources.

I hope the following content will be helpful to you!

Machine Learning 💡

The machine learning books recommended are mainly to help beginners get started quickly. Academic advancements in technologies are updated at a rapid pace, so while many of the concepts are classic, they may already be outdated. Therefore, the best strategy for improvement is always to follow cutting-edge researches and read the latest papers extensively.

Mathematics 📐

For students looking to delve deeper into machine learning, a strong foundation in mathematics is extremely important, yet often overlooked. Adequate theoretical analysis can make research more solid and provide innovative insights. Additionally, many of the books listed below are designed for students majoring in mathematics, so mastering all the content is usually unnecessary. I’m continually learning, too.