What is the longest and thickest nerve in the body?

Pick 1 of the following options to address. Option 1: How to Kill a CityConsider the arguments about gentrification in How to Kill a City by Peter Moskowitz. Now that you have spent most of a semester learning about how to develop a city economically and how to run a city administratively. Do the activities that we’ve discussed this semester (be specific) ultimately set the stage for building a city or do they set the stage for “killing” it?Option 2: Walkable CityPart A: Application of Walkability PrinciplesSpeck outlines several principles and strategies for making cities more walkable. Choose two of these principles and discuss how they can be implemented in a small American city like Joplin. What challenges might urban planners face when applying these principles, and how can they be overcome?Part B: Critical Evaluation of Speck’s VisionWhile Speck’s vision of a walkable city is compelling, it may not be feasible for all cities or neighborhoods. Critically evaluate the potential drawbacks or limitations of Speck’s ideas. Are there any specific contexts or types of urban environments where his approach might not be as effective or applicable?

Determine P(w1|x)  at x = 1.0.

Problem 1   The following five questions pertain to this problem, where in Questions 1-4, P(w1)=P(w2) by default. and in Question 5, the prior probabilities are unequal.

Pair the machine learning tasks with the optimization criteria listed below by filling in each blank slot with one label of A, B, C, or D.  Training tasks: A.  principal component analysis  B.  Bayes classifier design C.  multivariate Gaussian density estimation D.  logistic regression estimation  Optimization criteria: Maximum likelihood estimation:  Minimum cross entropy: Minimum classification error:  Maximum variance preservation:

The figure below shows a set of 2-D data samples in two clusters: red for C1 and blue for C2. Could this outcome be produced by performing k-means clustering directly on the 2-D data samples?              

Update the centroids of the two clusters after the above assignment step. m1_new =   ]’,  m2_new = ]’  

Perform cluster assignments in the first iteration, indicating the cluster for each sample in the table below, with 1 for C1 and 2 for C2. x1 x4 x5 x7 x8  

Choose the property below that corresponds to this discriminant function. A. Maximum likelihood classifier B. Minimum error classifier C. Linear classifier  D. All of the above  E. None of the above

  Problem 5  The following two questions pertain to this problem.