Introduction to Machine Learning (100% correct answers)

What is Machine Learning? correct answers Machine Learning is the study towards algorithms and statistical models that computers use to perform tasks without using explicit instructions. What is connectionist AI? correct answers Connectionist AI, represent information through a distributed, less explicit form with a network. Biological processes underlying learning, task performance, and problem solving are imitated. This in contrast to symbolic AI, that represent information through symbols and their relationships. Specific algorithms are used to process these symbols to solve problems and/or deduce new knowledge. What is CRISP-DM? correct answers *cross industry standard process for data mining, it was accepted by IBM. It was designed to be cross industry because data mining models can use ML models but need to be consistent across industries that use the same ML campaign. It connects a business problem to data mining objectives, and it consists of 6 steps: Business understanding Data understanding Data preparation Modeling Evaluation Deployment What are Precision and Recall? correct answers They are both basic evaluation metrics: Precision is the fraction of retrieved docs that are relevant. → TP / (TP + FP) Recall is the fraction of relevant docs that are retrieved. → TP / (TP + FN) What is F-measure? What other evaluation metrics exist? How are they defined? correct answers Is a single evaluation metric that allows the same-time evaluation of precision and recall. It is the weighted harmonic mean of the precision and recall test. The harmonic mean is the most conservative average. F1 measure is calculated by 2 * precision * recall / ( precision + recall ) Other evaluation metrics that exist are AUC-ROC, accuracy, Matthew's correlation coefficient. What types of ML problems do you know? How do they differ? correct answers There are three types of machine learning problems. Firstly, there is supervised learning, secondly there is unsupervised learning and lastly there is reinforcement learning. With supervised learning the task of learning maps an input with a specific output. The data has already been labeled. With unsupervised learning, the algorithm will describe the structure of the unlabeled data. And with reinforcement learning, a software agent ought to make decisions in an environment so as to maximize some notion of cumulative reward