STA

Survival Analysis ( Hazard Function H(t), Survival Function S(t), Probability Density Function f(t).

Analysing the Vapnik-Chervonenkis (VC) dimension of linear regression and explain how it relates to the generalization ability of the model.

Formulating the decision tree learning problem as a constrained optimization problem with the objective of minimising the expected risk and constraints based on tree depth and complexity. Discuss the trade-off between these two factors.

Formalizing the Universal Approximation Theorem for Neural Networks and explain its implications for the expressive power of these models. Discuss the limitations of the theorem and its relationship to the number of neurons and network architecture.

This essay describes how to perform the back propagation algorithm step-by-step for a single two-layer neural networks (ANNs) with sigmoid activation function. Describe how to derive the update equations for the weights and biases.

This essay question is all about proving the equivalence between Bayes Rule and Minimum Expected Loss Rule for classification under assumption of known class-conditional densities.

This documents contains assignment questions from forth year Statistic Courses (Specifically Artificial Intelligent): 1. The Newton-Raphson Method. 2. Iteratively Reweighted Least Squares (IRLS). 3. Survival Analysis (hazard function H(t), Survival function S(t), probability density function f(t)). 4. Proving the equivalence between Bayes Rule and Minimum Expected Loss Rule for classification under the assumption of known class-conditional densities. 5. Performing the back propagation alg...

STA FINAL

Statistics PreTest

STA test 4.