Sort by
Refine Your Search
-
machine learning algorithms. It also serves as a foundation for more advanced ML courses. The students will learn about ML problems (supervised, unsupervised, and reinforcement learning), models (linear and
-
: Course number and title: MIE1624F/S – Introduction to Data Science and Analytics Course description: The objective of the course is to learn analytical models and overview quantitative algorithms
-
University of Toronto | Downtown Toronto University of Toronto Harbord, Ontario | Canada | 18 days ago
sensing concepts and utilizing remotely sensed data for monitoring land resources and environmental change. Topics include surface-energy interactions, sensor systems, image interpretation, and applications
-
technical subjects such as programming, data science, machine learning, and algorithmic fairness is highly desirable. Candidates must have teaching experience in a degree-granting program, including lecture
-
, Python, or C/C++, with the ability to develop custom scripts and algorithms for data analysis and modeling. Familiarity with rheological characterization techniques, such as rheometry or viscometry
-
edge AI for localized knowledge preservation; AI governance and data sovereignty in digital heritage institutions and collections; study and design of recommendation systems and ranking algorithms used
-
), debuggers, code verifiers and unit test frameworks and gain experience in graphical user interface design and algorithm development. Posting end date: July 11, 2025 Number of positions (est): One (1) position
-
, strings, pointer-based data structures and searching and sorting algorithms. The laboratories reinforce the lecture topics and develops essential programming skills. Estimated course enrolment: ~150
-
), students will explore the implications of choices in architecture across the range from mainframes and personal computing to mobile devices and sensors, understand the nature of different network and service
-
, lists, maps. Program structure: control flow, functions, classes, objects, methods. Algorithms and problem solving. Searching, sorting, and complexity. Unit testing. Floating-point numbers and numerical