Tutor profile: Phat H.

What is supervised and unsupervised learning in Machine Learning?

There are two big branches of Machine Learning: supervised learning and unsupervised learning Supervised learning can be separated into two types of problems when: classification and regression: • Classification problems use an algorithm to accurately assign test data into specific categories, such as separating apples from oranges. Or, in the real world, supervised learning algorithms can be used to classify spam in a separate folder from your inbox. Linear classifiers, support vector machines, decision trees and random forests are all common types of classification algorithms. • Regression is another type of supervised learning method that uses an algorithm to understand the relationship between dependent and independent variables. Regression models are helpful for predicting numerical values based on different data points, such as sales revenue projections for a given business. Some popular regression algorithms are linear regression, logistic regression and polynomial regression.

Explain simply in words what is a derivative and an integral?

+ The derivative of a function f(x) of a real variable measures the "instantaneous rate of change" or "instantaneous velocity" if x is the object position, which is the ratio of the change of the function value f(x) to that of the variable x and the change in x is infinitesimally small. + The integral of a function f(x) can be considered as "area under the curve" or more specifically the sum of all small "parts", whose "area" is equal to f(x)dx (dx is infinitesimally small positive change in x). However, the "area" can be negative since f(x) can take negative values.

You are provided with a data set of the students' height, in which there are different 30 male students and 40 female students. How do we know if the height of male and female students are statistically significant?

To answer the question, we can use the notion of hypothesis testing. Hypothesis testing in statistics is a useful way for use to test different hypotheses. In this problem, we can use either t-test and z-test to perform the hypothesis testing. In those tests, we need to define the following: + Null hypothesis: The difference between males and females is zero + Alternative hypothesis: Otherwise Next, a statistic is computed to be the difference in the sample means of male and female students divided by the pooled standard deviation. This estimated statistic will correspond to a p-value, which is the probability that the statistic is at least as extreme as the observed statistic. If p-value < 0.05, then we reject the null hypothesis and confirm that there is sufficient evidence to confirm a statistically significant difference in the height of male and female students.