Your active participation in class is crucial in making the course successful. You are expected to come to class having already critically read any assigned material. Lectures are meant to summarize the readings and stress the important points.
To develop the basic skills necessary to pursue research in ML. To develop programming skills that will help you to build intelligent, adaptive artifacts. To develop a deeper understanding of several major topics in machine learning. To provide a broad survey of approaches and techniques in machine learning. There are four objectives for the course: We will cover a variety of topics, including decision trees, logistic regression, support vector machines, ensemble methods, Bayesian methods, neural networks, clustering, and dimensionality reduction. Discussions and questions related to the material and assignemnts will take place on Piazza.Ĭourse Description: This course will introduce the fundamental concepts and algorithms that enable computational artifacts to modify and improve their performance through experience. 
Sujie Zhou: Friday 3:00pm-5:00pm, CSE2 151Ĭontact: PLEASE COMMUNICATE TO THE INSTUCTOR AND TAS ONLY THROUGH THIS EMAIL (unless there is a reason for privacy).Īnnouncements and Resources will be posted via Canvas.Romain Camilleri: Thursday 1:00pm-3:00pm, CSE2 150.
Office hours: 10:30-11:30am Monday and Wednesday (after class), CSE2 131 Monday / Wednesday / Friday 9:30-10:20am, CSE2 G20
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