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Catalog Description
Recently Deep Learning (DL) techniques have shown a lot of promise for tasks in various modalities such as speech, language, and vision and DL has become a go-to machine learning paradigm for Artificial Intelligence (AI) based applications. The course aims to cover theoretical and applied aspects of Deep Learning and how it is used to solve real-world problems. Classes in each week may be divided into two segments: (a) Theory and Methods, a concise description of a deep learning algorithm, and (b) Lab Tutorial, a hands-on session on applying the algorithm on multimodal real world data such as textual, visual and audio data.
Prerequisites
Graduate standing.
Restrictions
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.
Notes
This course introduces the fundamentals of web application development with a focus on accessibility. Students will learn to apply heuristic and accessibility principles to create web applications that are user-friendly and inclusive. Students will be walked through all the steps involved in developing a web application such as requirements gathering, designing, coding, testing, deployment, and maintenance. Topics include, but are not limited to software development life cycle, software requirements, accessibility, user interface design, database management, software testing, and software maintenance. Students will be required to engage in class discussions, complete in-class and homework assignments, give oral presentations, work in small groups, and complete a semester project where they will deliver an artifact at the end. This course assumes students may have some prior knowledge of user experience (UX) and/or human-computer interaction (HCI), however, it is not a requirement. Also, no prior programming experience is required.