Machine Learning (ML), as part of Artificial Intelligence, is an interdisciplinary field that combines techniques from learning algorithms and statistics to make predictions or decisions without human intervention. Machine learning applications include business intelligence, biomedical systems, security, and automation. This class will introduce students to the fundamental concepts and algorithms for machine learning. We will learn supervised learning and unsupervised learning techniques such as hidden Markov models, support vector machines, clustering, and dimensionality reduction using Python. Students will acquire skills and knowledge on incorporating ethical issues in machine learning. Students will learn concepts such as dehumanization effects and amplification of human biases that are transferred into training data affecting machine learning.