Explore statistical learning theory for modern machine learning in this comprehensive seminar by John Shawe-Taylor from University College London. Delve into the foundations of learning generalization, high-confidence error distribution, and mathematical formalization. Examine risk measures, PAC-Bayes framework, and its comparison to Bayesian learning. Investigate the General PAC Bayesian Theorem and its proof, followed by an analysis of linear classifiers and SVM bounds. Gain insights from deep network training experiments and their results. Conclude with a discussion on the flexible framework of statistical learning theory and its implications for contemporary machine learning approaches.