- push forward the state of the art knowledge in machine learning regarding methods for neural networks complexity reduction; - development of methods for the evaluation of biases, fairness, overestimation and related metrics; - study, development and comparison of diverse approaches to reduce the complexity of neural networks; - development of benchmarking approaches that go beyond the traditional Accuracy vs Complexity trade-off; - exercise critical thinking in evaluating the research process and the results obtained.
Students enrolled in a professional higher technical course, in a degree, in an integrated master's degree or in a master's degree.
Minimum profile required
Minimum or equal grade of 16 in the Bachelor studies.
Experience in Computer Vision and Machine Learning.
Since 17 Nov 2022 to 02 Dec 2022
Cluster / Centre
Networked Intelligent Systems / Telecommunications and Multimedia