INF 385T Special Topics in Information Science : Introduction to Machine Learning
*Final project presentations will take place Thursday, May 12 7:00pm-10:00pm.
This class will cover core and cutting edge concepts employed in machine learning to solve artificial intelligence problems. Students will learn the theory behind a range of machine learning tools and practice applying the tools to, for example, textual data (natural language processing), visual data (computer vision), and the combination of both textual and visual data.
Machine learning is all about finding patterns in data to get computers to solve complex problems. In this course we study machine representations and algorithms that allow machines to improve their performance on a defined task from experience. Instead of explicitly programming computers to perform a task, machine learning lets us program the computer to learn from examples and improve over time with or without human intervention. This requires addressing a difficult question: how to generalize beyond the examples that have been provided at training time to new examples that you see at test time. This course will show you how this generalization process can be formalized and implemented. We'll look at it from lots of different perspectives, illustrating the key concepts in the field. This course is a broad overview of existing methods for machine learning and an introduction to adaptive systems in general. Emphasis is given to practical aspects of machine learning algorithms. The class format is split between quizzes, assignments, and course project. Each class consists of a lecture session and in-class lab session. The learning objective for each student is, once the student can understand the basics of machine learning technology, and the close connection between theory and practice they will have the ability to apply it to a wide range of applications in multiple fields. REQUIRED MATERIALS SUGGESTED TEXTBOOK (S) and/or MATERIALS: • Hands-on Machine Learning with Scikit-Learn & TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems by Aurelien Geron Programming experience is strongly recommended for this course.
Restricted to graduate degree seekers in the School of Information during registration periods 1 and 2. Remaining seats will be made available to outside students on January 13th. In the meantime, interested non-iSchool students may request a seat reservation by completing this Registration Support Questionnaire.