Week 1 Flashcards
What is a perceptron? What is its definition?
What is the learning rule of the perceptron?
What is the definition of a decision tree?
What is the difference between a decision tree and a random forest?
What is the definition of the Nearest Neightbor classification and regression?
What is the definition of a SVM (Support Vector Machine)?
What are the main three ML branches?
What is the definition of a gradient?
What is the definition of the Jacobian?
What is the definition of a Hessian?
What is the definition of a local minimum, a global minimum, and a extremum?
Proof that a minimum should have a gradient of zero.
What is the definition of a saddle point? Why is it important for ML?
Proof that a local minimum has a positive to be a semi-definite matrix.
What is the definition of convexity?
What is the definition of a convext function?
Proof that a local minimum is a global minimum if f is a convex function.
Proof that a strictly convex function has one local minimum.
How can you show convexity from a function using a Hessian?
Name 5 shortcuts to show convexity.
What is the chain rule, Bayes rule, and the law of total probability?
Name three rules for expectations.
Name three rules for variances.
What is the Jenssen inequality? What is the Markov inequality?
