Events Calendar

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

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 health care, 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. By attending this talk, participants will gain a better understanding of the principles of active machine learning and its potential applications in real-world scenarios.

Robert Nowak holds the Nosbusch Professorship in Electrical and Computer Engineering at the University of Wisconsin-Madison, where  he directs the AFOSR/AFRL University Center of Excellence on Data Efficient Machine Learning. His research focuses on machine learning, optimization, signal processing, and statistics. He serves on the editorial boards of the SIAM Journal on the Mathematics of Data Science and the IEEE Journal on Selected Areas in Information Theory.