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: Position: Sessional Lecturer I (1 position available) Course title and code: Foundations of Data Analytics and Machine Learning - APS1070 Course description: (1) Python programming (basic structures
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: Position: Sessional Lecturer I (2 positions available) Course title and code: AI in Finance - APS1052 Course description: In this course we’ll give an overview of several applications of machine learning
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transformative learning, insights and public engagement, bringing together diverse views and initiatives around a defining purpose: to create value for business and society. We make a fundamental promise – Here’s
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Assistant Professor, Teaching Stream - Contractually Limited Term Appointment - Software Engineering
software systems. Knowledge of current software development practices involving artificial intelligence and machine learning is an asset. Candidates are expected to be able to teach courses in existing
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Date Posted: 04/18/2024 Req ID: 37024 Faculty/Division: Faculty of Applied Science & Engineering Department: Department of Electrical & Computer Engineering Campus: St. George (Downtown Toronto
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Life connects life to learning. We believe every student should have the opportunity to participate in university life actively and find connection and community while discovering new ways of thinking
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the best possible online, in-person, and hybrid language learning experiences. Our engaging experiential programming complements the in-class experience by providing opportunities for learners to use
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of Post-doctoral experience with demonstrated academic and research expertise in the field of distributed systems. Skills – Strong background in distributed systems and machine learning. Ability to work
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 11 days ago
. Must have strong computer skills, including the use of Microsoft Word, Excel, PowerPoint, Adobe creative suite, and email software, as well as an ability to adapt to and learn new technologies. Excellent
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to fairness and ethics issues surrounding machine learning. An applied approach will be taken, where students get hands-on exposure to the covered techniques through the use of state-of-the-art machine learning