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| ME 53900 - Introduction To Scientific Machine Learning |
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Credit Hours: 3.00. Introduction to the fundamentals of predictive modeling for advanced undergraduates and graduate science and engineering students that work in the intersection of data and theory.
3.000 Credit hours Syllabus Available Levels: Undergraduate, Graduate, Professional Schedule Types: Distance Learning, Lecture Offered By: School of Mechanical Engr Department: Mechanical Engineering Course Attributes: Upper Division May be offered at any of the following campuses: West Lafayette Continuing Ed West Lafayette Learning Outcomes: 1. Represent mathematically the uncertainty in the parameters of physical models. 2. Propagate parametric uncertainty through physical models to quantify the induced uncertainty in quantities of interest. 3. Calibrate the uncertain parameters of physical models using experimental data. 4. Combine multiple sources of information to enhance the predictive capabilities of models. 5. Pose and solve design optimization problems under uncertainty involving expensive simulations or experiments. 6. Improve scientific writing and data visualization skills. Restrictions: May not be enrolled as the following Classifications: Freshman: 15 - 29 hours Sophomore: 45 - 59 hours Freshman: 0 - 14 hours Sophomore: 30 - 44 hours Prerequisites: GR ME 53900 Requisites General Requirements: ( Course or Test: MA 41600 Minimum Grade of D- May not be taken concurrently. and Course or Test: ME 58100 Minimum Grade of D- May not be taken concurrently. ) or ( Student Attribute: GR May not be taken concurrently. ) |
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