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Which of the following is the example of Bayes Classifiers? NOTATIONS QDA: Quadratic discriminate analysis LDA: Linear discriminate analysis #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience
Bayesian Inference can be summarized into 4 major steps: a. Calculate Likelihood b. Calculate/Collect prior c. Calculate posterior d. Inference. Which one represents the correct order ? #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience
Given the data 'D' and the model (parameter) 'θ', which of the following is called likelihood ? #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience #bayesianlearning #quiz
Given the Bayes Theorem, P(parameter|data) = [P(data|parameter) * P(parameter)] / P(data) We can reduce it to: P(parameter|data) ∝ P(data|parameter) * P(parameter) #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience
7 vote · Final results
Given the Bayes Theorem, P(Y|X) = [P(X|Y) * P(Y)] / P(X), P(Y|X) refers to - " how likely is the occurrence of Y given X is already observed? " #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience #data #DataMining #programming
15 vote · Final results
Given the Bayes Theorem, P(Y|X) = [P(X|Y) * P(Y)] / P(X), P(X|Y) refers to - "how likely is it to observe X if Y was known ?". #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience #data #DataMining #programming
7 vote · Final results
Given the Bayes Theorem, P(Y|X) = [P(X|Y) * P(Y)] / P(X), What is P(Y) refers to ? NOTATIONs: BP : beliefs prior BA : beliefs after #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience #data #DataMining #programming
Generative machine learning is training a model to learn parameters maximizing the joint probability - P(X, Y), between the target variable Y and features X. #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience #data #DataMining #programming
7 vote · Final results
Discriminative models estimates: p ( y | x ) —the probability of a label y given observation x. #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #AI #ML #DataScience #data #DataMining #tech #bigdat #programming
6 vote · Final results
Which of the following is not a generative machine learning model? #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #AI #ML #DataScience #data #DataMining #gradient #tech #bigdata #TechNews #Artificial #intelligence #programming
Hierarchical clustering can be divided into two groups: Agglomerative and Divisive. Agglomerative clustering is a top down approach whereas Divisive clustering a buttom up approach. #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #DataScience #DataScientist
5 vote · Final results
In hierarchical clustering, we don't need to pre-specify the number of clusters. #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #AI #ML #DataScience #data #DataMining #gradient #tech #bigdata #TechNews #Artificial #intelligence #programming
8 vote · Final results
What is true about DBSCAN clustering algorithm? #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #AI #ML #DataScience #data #DataMining #gradient #tech #bigdata #TechNews #Artificial #intelligence #programming
DBSCAN clustering algorithm can find arbitrary shaped clusters. #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #AI #ML #DataScience #data #DataMining #gradient #tech #bigdata #TechNews #Artificial #intelligence #programming
12 vote · Final results
DBSCAN clustering algorithm separates clusters of high density from clusters of low density. #MachineLearning #DeepLearning #NeuralNetworks #ArtificialIntelligence #AI #ML #DataScience #data #DataMining #gradient #tech #bigdata #TechNews #Artificial #intelligence #programming
11 vote · Final results
To avoid k-means clustering from getting stuck to a bad local minima, we should try using multiple random initializations. #MachineLearning #DeepLearning #ArtificialIntelligence #DataScience #tech #programming #data
9 vote · Final results
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