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.

Topic: Two Decades of Low-Rank Optimization Big data matrices are often low rank, and so modern optimization algorithms often require the use of low-rank decision variables. Such variables also arise naturally in certain optimization problems, irrespective of the input data. As a result, over the past two decades, low-rank optimization has grown as a critical area of research in optimization and machine learning. However, enforcing low-rank structure on a variable gives rise to severe...

The Concord Coalition and the Economics Department at the Tippie College of Business are hosting a panel discussion focused on the growing federal debt crisis, the future of Social Security, how to lower the cost of healthcare and how to stimulate long-term growth in the economy.

The Clarence Tow Lectures in Economics presents Cameron Ellis, University of Iowa. This event is sponsored by the Clarence Tow Lecture Fund.