Spring 2024

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

Unique ID: 27740


05:00 PM - 06:30 PM  UTA 1.210A
05:00 PM - 06:30 PM  WEB

*As of January 9, this class has been canceled and will no longer be offered in Spring 2024.

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In Person


This course will cover fundamental concepts in Machine Learning (ML). The course will provide conceptual and practical knowledge on a wide range of modern machine learning algorithms; including supervised learning (multiple linear regression, logistic regression, neural networks, and decision trees), unsupervised learning (clustering, dimensionality reduction, recommender systems), reinforcement learning & deep learning models (CNN, RNN, Autoencoders & Transformers) and also introduce the importance of Prompt Engineering and Retrieval Augmented Generation. The goal is for students to be comfortable and confident in machine learning concepts and have the ablity to build machine learning model solution to challenging real-world problems. If you’re looking to break into AI or build a career in machine learning, this is a great place to start.


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.


Graduate standing.


Restricted to graduate students in the School of Information through registration periods 1 and 2. Outside students will be permitted to join our waitlists beginning with registration period 3.