AAI611O Machine Learning Fundamentals and Applications

[3–0, 3 cr.]

This course covers the essential machine learning techniques and algorithms and their applications. Topics include supervised and unsupervised learning, clustering, classification algorithms, linear regression, support vector machines, decision trees, random forests, neural network, deep learning, and reinforcement learning. Throughout the course, students will be exposed to real-world industrial, business, medical and social problems, where the obtained skills are employed to handle data and develop machine learning based solutions. The material and structure of the course are designed with a preference for the practical knowledge of AI more than mathematical or theoretical concepts. Different Machine Learning applications will be discussed including computer vision, natural language processing, time-series prediction, speech recognition, sentiment analysis, cybersecurity, among others.