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- Sum of Squared Errors: Remember from your statistics courses that if two random variables X and Y are related by a relation YaX+b and you had a set of observations {(1, 1), (2, 2)..... (z.)). then for every estimated values of a and b, the Sum of Squared Errors (SSE) was defined as in the following formula. SSE(-ar-b)². 1-1 The less SSE, the better a and b are estimated. Consider the following fixed list. In this list each sub-list of length two is standing for one pair (z.). Write a function that recieves two numbers a and b and returns the associated SSE. L- [[1, 2], [1.1, 2], [2, 7.1), (2.5, 7.21, (3, 11]]You build a model predicting blood pressure as a function of three variables: weight (numeric) age (numeric) income (categorical: low, medium, high) You first specify your model as: blood pressure ~ age * income + weight How many parameters (k) does your model have? (Remember, we do not count the grand mean in k) You change the above model specification to be: blood pressure ~ age * income + weight * income How many parameters does your model have now? You change your model to include the three-way interaction (which, remember, includes all two-way interactions and main effects, too!) Your model now looks like this: blood pressure ~ age * income * weight How many parameters does your model have now?1.) We want to build a model to predict the weight (in Ibs) of a car. This prediction will be based on multiple features of the car, such as: "Number of Cylinders", "Miles Per Gallon", "Production Year", etc. To train the model, we are given 1000 examples of cars along with the feature values and class for each car. What technique could we use in this case? Multiple Linear Regression Simple Linear Regression O K-Means Clustering K-Nearest Neighbors
- Design and describe a general m-round winner tournament, Round-Winner-Tournament(P,m), for players P, where in each round 0, 1,...,m − 1 the players are paired randomly and the winners proceed to the next round. After round m − 1, the champion is selected randomly from the remaining players. Interestingly, this tournament structure has the following special cases: m = 0 is a random selection tournament, m = 1 is a random pairing tournament, and m = lg |P| is a single elimination seeding tournamentLogistic regression aims to train the parameters from the training set D = {(x(i),y(i)), i 1,2,...,m, y ¤ {0,1}} so that the hypothesis function h(x) = g(0¹ x) 1 (here g(z) is the logistic or sigmod function g(z) can predict the probability of a 1+ e-z new instance x being labeled as 1. Please derive the following stochastic gradient ascent update rule for a logistic regression problem. 0j = 0j + a(y(¹) — hz(x)))x; ave. =Show that in every stable matching, m₁, m₂ are matched to w₁, W₂, i.e., (m₁,w₁), (m₂, W₂) or (m₁,w₂), (m₂,w₁) must be part of any stable matching.
- Given: NFA M1 = ({A,B,C}, {a,b}, delta, A, {C}) where delta: (A,a) -> {A}, (A,b) -> {A,B}, (B,a) -> {C}, (B,b) -> null, (C,a) -> null, (C,b) -> null. Get the equivalent DFA of the given NFA.Given: NFA M1 = ({A,B,C}, {a,b}, delta, A, {C}) where delta: (A,a) -> {A}, (A,b) -> {A,B}, (B,a) -> {C}, (B,b) -> null, (C,a) -> null, (C,b) -> null. Get the equivalent DFA of the given NFA. How many self-loop edge/s in the equivalent DFA?Given: NFA M1 = ({A,B,C}, {a,b}, delta, A, {C}) where delta: (A,a) -> {A}, (A,b) -> {A,B}, (B,a) -> {C}, (B,b) -> null, (C,a) -> null, (C,b) -> null. Get the equivalent DFA of the given NFA. How many 4-symbol strings are accepted in the given NFA M1 or in the equivalent DFA?
- 1.The features A1 and A2 were measured for each member of two groups X and Y. The measurements are given below: Group X Feature A1 120 100 130 120 140 Feature A2 75 80 80 70 70 Group Y Feature A1 80 70 90 90 100 Feature A2 70 70 60 70 60 You are given that: Query1 has features A1 - 120, A2 - 85, and Query2 has features A1 - 90, A2 - 50. Classify Query1 and Query2 by using: Euclidean distance method 2.Apply the Apriori algorithm to the following transactional data. The data has been obtained from a popular supermarket chain in Nairobi during the school's opening week. Assume minimum support of minimum support of 25%, and minimum confidence 55%, and derive a rule or rulesImagine a regression model on a single feature, defined by the function f (x) = wx + b where X, W, and b are scalars. We will use the MSE loss loss(w, b) = E;(f(x;) – t;)² . n Work out the gradient with respect to b. Which is the correct answer? Read the four equations carefully, so you notice all the differences. 1. E:(f(x;) – t;)x; 2.E(f(x;) – t;) n 3.- E;(wx; + b – t;)x; n 4. E: (wa; +b - t;) n 41. Your friend has a strong opinion on cars: he either likes a car or does not like it at all. We have collected the following training data about his preferences using the features Color taking values: Red, Yellow; Type taking values: Sports, SUV; - Origin taking values: Domestic, Imported. No color likes origin Sports Domestic Yes Sports Domestic No Sports Domestic Yes Yellow Sports| Domestic No Yellow Sports| Imported Yes Imported No Imported Yes Domestic No Imported No Sports | Imported Yes type 1 Red 2 Red Red 4 5 6 Yellow SUV 7 Yellow SUV 8 Yellow SUV Red SUV 10 | Red Use a naive Bayes' classifier to predict whether your friend likes a Red Domestic SUV. Explain your computation in detail.