This book provides a practical introduction to exploratory data analysis using Python. It covers topics such as distributions, probability, and hypothesis testing. The book is very hands-on and includes many case studies.
A foundational text on convex optimization by one of the pioneers of the field.
A foundational book on kernel methods, providing a comprehensive overview of the theory and algorithms. While not a course, it is a key resource for in-depth learning.
A comprehensive book that starts with beginner topics like graph theory and traditional graph approaches and moves to more advanced topics such as novel GNN models and state-of-the-art research. It is a self-contained resource with most of the required theory for graph neural networks.
This book provides a practical guide to the key concepts in statistics for data scientists. It covers topics such as exploratory data analysis, sampling, and hypothesis testing. The book is very hands-on and includes many examples using R and Python.
A comprehensive book on numerical optimization, covering both theory and practical algorithms.
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