Pages

16 February 2018, what a lovely day it was! A daughter set on the journey to meet her dad whom she didn’t see for 3 long months. I settled here in Istanbul for my master’s in Data Sciences from Istanbul Sehir University. With the end of my semester, my dad planned a trip to see me at Istanbul for three days and then fly to California to meet my older sister who was waiting to see him with same impatience.

Natural Language Inference - A large Annonated Corpus

Hello people! Here I will be giving you an overview to a research paper related to Natural Language Inference. This is one of my favorite topics of study for why I chose this particular amazing research paper. Natural language along being interesting is one of the hot topics of study today and there is lots and lots of research happening in this domain.

Machine Learning in Simple Words: Statistical Significance Tests

Developing Intuition: “Statistically Significant” or “By Chance”?

Statistical tests are used to determine how likely are observed relationships between two variables or groups NOT a result of some random guess, luck, erratic fluctuations, noise or sampling error, but due to the fact that they really are related to one another. It is a mathematical way of stating if we have enough evidence to reject a ‘thought’(or in statistical jargon a ‘Hypothesis’) that a certain relationship between two variables just happened by chance.

 Figure Source: Agresti and Franklin, Statistics: The Art and Science of Learning from Data (p. 468)

Machine Learning in Simple Words: Decision Tree Series

Decision trees are one of the most widely used machine learning algorithms used today. In this part, let us learn at a very high level, how they work.

PART 1: A visual guide towards their working

Example Outline:

Suppose we are trying to find out whether a patient is a positive case of a certain disease, using the length and width of the suspect virus found in his/her blood. We obtained test results of 16 patients. Since decision tree is a form of ‘supervised learning’, we will first have to train the decision tree model by providing it with the sets of virus lengths and widths that resulted in positive casesand the ones which did not, before it is able to do predictions on its own. Upon plotting this data obtained from each patient, let us assume we landed up with a graph shown below: