Active Machine Learning: Combining Human and Artificial Intelligence for Improved Learning Efficiency and Accuracy

You are cordially invited to the weekly CS Machine Learning Lunch Meetings. This is a chance to get to know machine learning professors, and talk to your fellow researchers.  Our next meeting will be on Tuesday April 18 12:12-1:30pm in CS 1240. Professor Rob Nowak will explain active learning, see abstract below.

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Abstract: Active machine learning combines the power of artificial intelligence with human intelligence to improve the efficiency and accuracy of machine learning algorithms. The fundamental idea behind active learning is to actively engage the human user in the learning process by selecting the most informative data points for labeling, thus reducing the amount of labeled data required to achieve a given level of accuracy. In this talk, we will discuss the theory and applications of active machine learning, including various sampling strategies, query selection methods, and active learning algorithms. We will also explore how active learning can be applied to different domains, such as crowdsourcing and recommendation systems, AI-assisted education, computer vision, and healthcare, and discuss the challenges and limitations of active learning techniques. Finally, we will highlight some recent developments in the field and provide some directions for future research.