Show the differences between machine learning and deep learning models in terms of the training time needed, the data and compute requirements, the accuracy demands, the necessity to modify the hyperparameters, and the dependence on hardware.

In this course, you learned key concepts and tools used in machine learning. Obviously, this field cannot possibly be covered in one course, regardless of length or learning modality. How would you describe your ability to relate the course concepts to real-world problems? If presented with a problem or challenge, how confident are you that this course has equipped you with the skills to build an appropriate machine learning model? If a particular machine learning topic of interest has not been covered, to what extent has this course has equipped you with the ability to expand your knowledge by learning on your own?

Why is it important to have a robust set of metrics for machine learning?

Models for machine learning are often taught using large, well-characterized data sets (or attributes). However, we are cognizant of the fact that characteristics may be meaningful or irrelevant depending on the context. Give some examples of how the "utility" of a feature may be explained.