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Catalog Description
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.
Instructor Description
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.
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 togeneralize 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. Emphasis is given to practical aspects of machine learning algorithms. 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.
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 August 19th. Interested non-iSchool students may request a seat reservation by completing this Registration Support Questionnaire.