- Write a compute program to solve theequations of Exercise 1.1, for the 10 data points of Figure 1.2 (you generate them according to the description there) for M=0,1,3, 9. Plot the fit curves and original data points as Figure 1.4.
- Modify the compute program of HW2 to solve the equations of Exercise 1.2, forthe 10 data points you generated in part 1A. Here the main focus is theregularization M=9. Show that as lambda of Eq.(1.4) increases, the overfit of Fig 1.4 (right-bottom figure) reduced significantly, to something like Figure 1.7.
Write a computer program that can read in data such as those in Table 4.1. (A) The program can represent each data instance ---these are “stored data”(B) When a new data instance is presented, the program compares the new data instance to every “stored data instances”to compute the distance, and find out the k nearest neighbors, and predict the class label for the new data instance. (C) Compute the multinomialdistribution for each attribute of the data instances and prior probability. When a new data instance is presented, compute the class label using NAÏVEBayes classification method.
Data Cluster using K-means algorithm provided by the system.1. Run k-means on AT&T 100 images, set K=10. Obtain confusion matrix. Re-order the confusion matrix using bipartite graph matching and obtain accuracy.2. Run k-means on AT&T 400 images, set K=40. Obtain confusion matrix. Re-order the confusion matrix and obtain accuracy.3. Run k-means on Hand-written-letters data, set K=26, as above.Computer Exam3 will depend on the codes you write for Project 3.