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Fundamental Techniques of Data Science:
This talk is intended for two groups of audiences: (a) those considering a career in data science, and (b) business executives, who want to learn about how data science can be used to solve their business problems and improve their business decision making. The talk will introduce the field of data science from both a business-oriented and a technical perspective, explaining how business problems can be converted into data problems can then be decomposed into a small set of canonical data science problems, including regression, classification, scoring, clustering, similarity matching, co-occurrence grouping, profiling, link prediction, and data reduction, for which well-known solutions exist. Several examples of these canonical data science techniques and their applications in solving various business use cases will be provided.
Feedback Learning for Supervised Machine Learning Systems:
This talk is intended for more advanced users of data science and concerns the concept of feedback learning and how it can be used to improve the performance of supervised machine learning systems through the interaction of the learning algorithms with human experts. Sampling approaches that are fundamental to the feedback process, including uncertainty sampling, diversity-based sampling, and misclassification-based sampling will be discussed.
6:15 - 6:45 - Eat, Greet, & Meet
6:45 - 7:00 - Introduction / Announcements
7:00 - 7:45 - Fundamental Techniques of Data Science
7:45 - 8:15 - Feedback Learning for Supervised Machine Learning Systems
8:15 - 8:30 - Questions..