A Primer in Machine Learning

Explore the fundamentals of machine learning in this 31-minute talk by Mariusz Różycki. Discover how computers can learn from data to solve complex problems traditionally challenging for machines. Delve into core concepts such as hypotheses, data representation, and algorithm selection. Examine practical examples including linear and polynomial regression, naïve Bayesian classifiers, and logistic regression. Learn how to begin implementing machine learning approaches in your own programs. Cover topics like supervised and semi-supervised learning, cost functions, gradient descent, regularization, and performance metrics for unseen data. Gain insights into using MATLAB for machine learning applications.

Intro
WHAT IS MACHINE LEARNING?
SEMI-SUPERVISED LEARNING
REPRESENTING THE DATA
HYPOTHESIS
COST FUNCTION
GRADIENT DESCENT
LINEAR REGRESSION (2)
POLYNOMIAL REGRESSION (3)
LOGISTIC REGRESSION (3)
MATLAB
UNSEEN DATA
EXAMPLE METRICS
REGULARISATION