Assignment 2: Regression, Classification and clustering
Given the following data points:
1.What is the cost function for linear regression?
2.If we use the gradient descent algorithm to minimize the cost function for linear regression, what are the θ values and cost values in the first three iterations? Suppose the initial θ values are [1, 0.5]Tand the learning rate α is 0.1.
- If we use one-vs-all strategy to a three class classification problem with three classes: -1, 0, 1, how many classifiers shall we train? What are they?
4.Describe the difference between linear regression and logistic regression. Please list at least three.
- Support Vector Machines
(a)Suppose we are using a linear SVM (i.e., no kernel) and are given the following data set. Draw the decision boundary of linear SVM. Give a brief explanation.
(b)In the following代写Regression, Classification and clustering image, circle the points such that by removing that example from the training set and retraining SVM, we would get a different decision boundary than training on the full sample. You do not need to provide a formal proof, but give a one or two sentence explanation.
6: K-means
(a)Consider the unlabeled two-dimensional data represented in the following figure. Using the two points marqued as squares as initial centroids, draw (on that same figure) the clusters obtained after one iteration of the k-means algorithm (k = 2).
(b)Does your solution change after another iteration of the k-means algorithm? Why? WX：codinghelp
