Answer / Amardeep Singh Sethi

Bayesian and Frequentist are two main philosophical schools of statistical inference. Bayesian methods update the probability of a hypothesis based on observed data, using prior knowledge to inform the estimation process. In contrast, Frequentist methods estimate parameters as fixed quantities from the sample data, without incorporating prior knowledge. Bayesian methods often require more computational resources and complex models but can provide probabilistic interpretations of results.

Explain what is the function of ‘Unsupervised Learning’?

1 Answers  

---

What is false positive and false negative and how are they significant?

1 Answers  

---

Why machine learning?

1 Answers  

---

Explain Classification and Regression?

1 Answers  

---

What are the areas in robotics and information processing where sequential prediction problem arises?

1 Answers  

---

Compare sas, r and python programming?

1 Answers  

---

Explain latent dirichlet allocation (lda).

1 Answers  

---

Which are the two components of bayesian logic program?

1 Answers  

---

Tell me what is a recommendation system?

1 Answers  

---

What is the difference between inductive machine learning and deductive machine learning?

1 Answers  

---

How can you ensure that you are not overfitting with a particular model?

1 Answers  

---

Do gradient descent methods at all times converge to a similar point?

1 Answers  

---