Tutorial 2: Machine Learning and AI – From Theory to Practice

AI and machine learning has emerged as the cornerstone of the 4th industrial revolution, transforming almost every aspect of human life. Though the wide scope and rapid research advances in AI might appear intimidating, most learning algorithms rely on few fundamental principles. In this tutorial, we cover some of the most important techniques and principles that are common across popular machine learning and deep learning algorithms, and learn to apply them on few real-world problems. Covered topics shall include basic principles of supervised, unsupervised and reinforcement learning techniques, along with popular machine learning algorithms such as linear regression, logistic regression, Naive Bayes, KNN, K-means,  decision tree and random forest, multi-layer perceptron and deep learning models such as CNN. We shall also touch up on applications of AI in emerging areas such as autonomous marketing and intelligent customer engagement.

Requirements: This tutorial attempts to be a blend of theory and practice. Participants must bring laptops having at least two cores and 4 GB of RAM, with docker installed. A docker image will be shared for this session.