Michael Erlihson on LinkedIn: Mathematics of Machine Learning
🚀 Unlock the Mathematics Behind Machine Learning! 📊 📘 Title: Mathematics of Machine Learning ✍️ Author: Philipp Christian Petersen 🤖 Are you ready to dive into the math that powers modern machine learning? This comprehensive lecture series is a treasure trove for anyone passionate about the mathematical foundations of ML. 🌟 Highlights Include: 1️⃣ PAC Learning Framework: Discover the rigorous foundations of Probably Approximately Correct learning. 2️⃣ Rademacher Complexity: Measure the capacity of hypothesis classes and generalization ability. 3️⃣ VC Dimension: Explore Vapnik–Chervonenkis bounds for learning theory. Model Selection: Master empirical and structural risk minimization. 4️⃣ Support Vector Machines & Kernel Methods: Learn margin theory and how kernels extend linear models to nonlinear spaces. 5️⃣ Neural Networks & Clustering: From shallow architectures to K-means and spectral clustering. 6️⃣ Dimensionality Reduction: Techniques like PCA, diffusion maps, and the Johnson
