CSE 350/450 Special Topic:Privacy-Aware Data Analytics With the tremendous success of data-driven services and applications (e.g., personalized recommendation, customized news, targeted ads) follows their immense threat to the privacy of people’s sensitive information. This course discusses how to design and implement data analytical methods and systems that respect individuals’ data privacy while still enabling high-quality analysis results. Main topics covered in the course include: privacy-aware data publishing, privacy-aware data mining, privacy-aware mobile services, privacy-aware web services, and secure multiparty computation. The course will be a combination of lectures and paper presentations by the students. Students will also pursue a course research project. The final outputs of the project include a presentation and a short report.
CSE 347/447:Data Mining Overview of modern data mining techniques: data cleaning; attribute and subset selection; model construction, evaluation and application. Fundamental mathematics and algorithms for decision trees, covering algorithms, association mining, statistical modeling, linear models, neural networks, instance-based learning and clustering covered. Practical design, implementation, application and evaluation of data mining techniques in class projects. Credit will not be given for both CSE 347 and CSE 447. Prerequisites: (CSE 17 or CSE 018) and (MATH 231 or ECO 045). See detials here.