Spring 2023

INF 385T Special Topics in Information Science : Introduction to Machine Learning

Unique ID: 28455


04:30 PM - 06:00 PM  UTA 1.210A
06:30 PM - 08:00 PM  WEB

In-person class meetings on Monday and synchronous online meetings on Tuesday.

Review Previous Course Iterations & Syllabi



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

• 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.


Graduate standing.


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 6. Interested non-iSchool students may request a seat reservation by completing this Registration Support Questionnaire.