What is Kernel Trick in an SVM Algorithm?
Kernel Trick is a mathematical function which when applied on data points, can find the region of classification between two different classes. Based on the choice of function, be it linear or radial, which purely depends upon the distribution of data, one can build a classifier.
What are the advantages of Naive Bayes?
In Naïve Bayes classifier will converge quicker than discriminative models like logistic regression, so you need less training data. The main advantage is that it can’t learn interactions between features.
In what areas Pattern Recognition is used?
Pattern Recognition can be used in
- Computer Vision
- Speech Recognition
- Data Mining
- Statistics
- Informal Retrieval
- Bio-Informatics
What are the different types of Learning/ Training models in ML?
ML algorithms can be primarily classified depending on the presence/absence of target variables.
A. Supervised learning: [Target is present]
The machine learns using labelled data. The model is trained on an existing data set before it starts making decisions with the new data.
The target variable is continuous: Linear Regression, polynomial Regression, quadratic Regression.
The target variable is categorical: Logistic regression, Naive Bayes, KNN, SVM, Decision Tree, Gradient Boosting, ADA boosting, Bagging, Random forest etc.
B. Unsupervised learning: [Target is absent]
The machine is trained on unlabelled data and without any proper guidance. It automatically infers patterns and relationships in the data by creating clusters. The model learns through observations and deduced structures in the data.
Principal component Analysis, Factor analysis, Singular Value Decomposition etc.
C. Reinforcement Learning:
The model learns through a trial and error method. This kind of learning involves an agent that will interact with the environment to create actions and then discover errors or rewards of that action.
What are the last machine learning papers you’ve read?
Keeping up with the latest scientific literature on machine learning is a must if you want to demonstrate an interest in a machine learning position. This overview of deep learning in Nature by the scions of deep learning themselves (from Hinton to Bengio to LeCun) can be a good reference paper and an overview of what’s happening in deep learning — and the kind of paper you might want to cite
What is the difference between the Naive Bayes Classifier and the Bayes classifier?
Naive Bayes assumes conditional independence, P(X|Y, Z)=P(X|Z)
P(X|Y,Z)=P(X|Z)
P(X|Y,Z)=P(X|Z), Whereas more general Bayes Nets (sometimes called Bayesian Belief Networks), will allow the user to specify which attributes are, in fact, conditionally independent.
For the Bayesian network as a classifier, the features are selected based on some scoring functions like Bayesian scoring function and minimal description length(the two are equivalent in theory to each other given that there is enough training data). The scoring functions mainly restrict the structure (connections and directions) and the parameters(likelihood) using the data. After the structure has been learned the class is only determined by the nodes in the Markov blanket(its parents, its children, and the parents of its children), and all variables given the Markov blanket are discarded.
Explain the difference between supervised and unsupervised machine learning?
In supervised machine learning algorithms, we have to provide labelled data, for example, prediction of stock market prices, whereas in unsupervised we need not have labelled data, for example, classification of emails into spam and non-spam.
What is PAC Learning?
PAC (Probably Approximately Correct) learning is a learning framework that has been introduced to analyze learning algorithms and their statistical efficiency.
Differentiate between regression and classification.
Regression and classification are categorized under the same umbrella of supervised machine learning. The main difference between them is that the output variable in the regression is numerical (or continuous) while that for classification is categorical (or discrete).
Example: To predict the definite Temperature of a place is Regression problem whereas predicting whether the day will be Sunny cloudy or there will be rain is a case of classification.
What is a Recommendation System?
Anyone who has used Spotify or shopped at Amazon will recognize a recommendation system: It’s an information filtering system that predicts what a user might want to hear or see based on choice patterns provided by the user.