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University of Toronto Faculty of Information Sessional Lecturer Fall Term 2024 (September - December) INF2404H – Explainability & Fairness for Responsible Machine Learning Course Description
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Course description: A half-year capstone design course in which students work in small teams to apply the engineering design, technical, and communication skills learned previously, while refining
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applications of machine learning for textual transcription; specialist imaging techniques including multi-spectral imaging and micro-CT scanning; the mediation and digitization of social processes; artistic and
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theoretical models with experimental data, predicting promising candidates with computational tools and machine learning algorithms, and elucidating structure-property relationships of emerging molecules
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machine learning algorithms, and elucidating structure-property relationships of emerging molecules, polymers, solid-state materials, formulations, etc. Tasks include: Implementing research and development
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: GLA2024H Intelligence and Cybersecurity in Global Politics Course Description: Information technology is ubiquitous. It powers the global economy, improves government administration, enhances military power
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, Information Engineering, Human Factors, and Applied Machine Learning, all of which seek to improve the systems we as humans rely on to navigate our world. On the Mechanical Engineering side, research areas
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, research areas include Operations Research, Information Engineering, Human Factors, and Applied Machine Learning, all of which seek to improve the systems we as humans rely on to navigate our world