Answer the following in the context of the Adaboost algorithm. (No formulas, only language description). (a) (4) Which points are given higher/lower weights after learning each weak-classifier?   (b) (4) How is the weight for each data point used by the algorithm?   (c) (4) How do we assign weights to weak classifiers for their contribution in the global decision?

Consider the following training data for a perceptron: X.  Y.  Z.  Class 0.  3.  5.     1 1.  4.  8.     0 7.  1.  2.     1 -1. 5. 5.     0 2.  6.  7.     0   Use (2 3 1 4) as the initial weight vector. Execute the perceptron training algorithm as discussed in class and report the following: 1. (4) The updated weight vector after the second data point, (1,4,8), is processed.   2. (4) The updated weight vector after the third data point, (7, 1, 2), is processed.   3. (6) The updated weight vector after the fifth data point, (2, 6, 7), is processed.

Consider the following data points and their class labels: [P1: (1, 3, 4), A], [P2: (7, 2, 4), B], [P3: (8, 1, 6), B], [P4: (7, 7, 1), A], [P5, (2, 1, 1), A]. We want to use the distance weighted 3-NN classification with this data.  Each point at a distance of d has a vote of (1/d) for predicting thee class label.   (a) (8) For the query point (4, 4, 4) find and report the data points in its 3-nearest-neighbor list, their weights for decision, and the class label that should be assigned to this query point.     (b) (6) What is gained and what is lost when we increase the value of K in a K-NN classifier?    

(10) Credit card fraud detection systems operate at very low precision values, say 5%, for the class of fraudulent transactions. Why is this still a good idea? Explain with a small example.

Consider the following three data vectors. D1: (4, 6, 7, 9), D2:(6, 9, 10, 14), and D3: (4, 6, 2, 1). (a) (3) What are the Manhattan distances for the data-point pairs: D1-D2: D1-D3: D2-D3: (b) (5) What are the cosine similarities for the data-point pairs: D1-D2: D1-D3: D2-D3: (c) (2) Which two pints are the closest as per the Manhattan distance?   (d) (2) Which two points are the closest as per the Cosine similarity?  

(10) What specific function or operation is performed by the Kernel-Trick when we design a non-linear SVM?

Which of the following amino acids would most likely be present in the transmembrane domain of an integral membrane protein?

The energy diagram of the enzyme-catalyzed reaction below indicates that:   

Place a checkmark next to every way that phospholipids and phospholipid bi-layers may vary:

Correctly identify the membrane components in the image below with the most correct term: