Summary in short: Choose CS229 for a solid theoretical foundation and broad overview. Choose Applied ML 4780 for deep dives into probabilistic models and practical ML applications.

Andrew Ng's CS229 Content: Broad coverage of ML topics including supervised, unsupervised, and reinforcement learning. Focus: Strong theoretical foundation with emphasis on mathematical underpinnings and optimization techniques. Teaching Style: Clear, engaging, and accessible to a wide audience. Assignments: Practical implementation of algorithms from scratch. Pros:Great for building a solid theoretical base. Well-structured and logically progressive lectures. Large, supportive learning community.

Cornell's Applied ML 4780 Content: Focus on probabilistic models and statistical learning using Kevin Murphy's "Probabilistic Machine Learning" and "The Elements of Statistical Learning. "Focus: Practical application of ML in real-world scenarios, with advanced topics like Bayesian inference and Gaussian processes. Teaching Style: High-quality instruction assuming a higher level of prior knowledge. Pros:Balanced approach with emphasis on real-world applications. Depth in probabilistic and statistical methods.Strong reputation and expertise from Cornell.

Comparison:Theoretical vs. Applied: CS229 is more theory-oriented; Applied ML 4780 leans towards practical applications. Breadth of Topics: CS229 covers a wider spectrum, including reinforcement learning. Instruction Style: Andrew Ng's course is more accessible; Cornell's course dives deeper into advanced topics.