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learning models, including their strengths, deficiencies, and strategies for (hyper)parameter optimization. Prior use of Bayesian optimization or other relevant active learning algorithms is preferred
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University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | about 1 month ago
- Population Genetics Course Description: This course introduces students to the genetic variation between and within populations. The topics include evolutionary forces, quantitative genetics, and Bayesian
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Studies Course description: The use of proxy data (terrestrial and aquatic microfossils) to infer past environmental conditions. The nature and extent of Quaternary environmental change is considered in
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Sessional Lecturer, INF2205H - Designing Sustainable & Resilient Machine Learning Systems with MLOps
Description: Decision-makers in modern organizations rely on machine learning systems to infer insights from information by analyzing meaningful patterns in the connections and associations within data. Leaders
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experience Minimum five (5) years recent and related experience Experience in applied statistics (multivariate models and distributions, jump processes, inference), e.g. simulation tools such as Monte Carlo