Advanced Introduction to Machine Learning | CMU

Comprehensive and in-depth exploration of fundamental machine learning concepts and techniques, including deep learning, clustering, kernel machines, and graphical models.

Introduction

This advanced machine learning course covers a wide range of topics, including deep networks, clustering, kernel machines, Gaussian processes, graphical models, and computational learning theory. The course provides a comprehensive and in-depth exploration of these fundamental machine learning concepts and techniques.

Highlights

• Extensive coverage of deep learning, including backpropagation, layers, and representations
• Thorough treatment of unsupervised learning methods like K-Means, Expectation Maximization, and Principal Component Analysis
• Detailed study of kernel machines, Gaussian processes, and latent space models
• Exploration of graphical models, including Hidden Markov Models, directed and undirected models, and Markov Chain Monte Carlo methods
• Introduction to computational learning theory topics like risk minimization and VC dimension

Recommendation

This course is highly recommended for students and professionals with a strong background in machine learning who are looking to deepen their understanding of advanced techniques and theoretical foundations. The course covers a wide range of cutting-edge topics and provides a solid foundation for further research or practical applications in machine learning.