The document discusses several challenges in machine learning, including: 1) Determining when generative vs. discriminative learning methods are better and how to make generative methods more computationally feasible. 2) Developing methods for learning from non-vectorial data like text, images, and graphs that can work across different data types and learning algorithms. 3) Extending discriminative methods like neural networks and support vector machines to more complex problems beyond classification and regression. 4) Developing distributed learning methods that can handle distributed data while preserving privacy.