Autumn
Winter
Spring
Summer

1 - 10 of 25 results for: Jef Caers

EARTHSYS 100A: Introduction to Data Science for Geoscience (EPS 6)

This course provides an overview of the most relevant areas of data science to address geoscientific challenges and questions as they pertain to the environment, earth resources & hazards. The focus lies on the methods that treat common characters of geoscientific data: multivariate, multi-scale, compositional, geospatial and space-time. In addition, the course will treat those statistical method that allow a quantification of the human dimension by looking at quantifying impact on humans (e.g. hazards, contamination) and how humans impact the environment (e.g. contamination, land use). The course focuses on developing skills that are not covered in traditional statistics and machine learning courses.
Terms: Win | Units: 3 | UG Reqs: WAY-AQR | Repeatable 3 times (up to 9 units total)
Instructors: Caers, J. (PI)

EARTHSYS 240: Data Science for Geoscience (ENERGY 240, EPS 140, EPS 240, ESS 239)

Overview of some of the most important data science methods (statistics, machine learning & computer vision) relevant for geological sciences, as well as other fields in the Earth Sciences. Areas covered are: extreme value statistics for predicting rare events; compositional data analysis for geochemistry; multivariate analysis for designing data & computer experiments; probabilistic aggregation of evidence for spatial mapping; functional data analysis for multivariate environmental datasets, spatial regression and modeling spatial uncertainty with covariate information (geostatistics). Identification & learning of geo-objects with computer vision. Focus on practicality rather than theory. Matlab exercises on realistic data problems.
Terms: Win | Units: 3
Instructors: Caers, J. (PI)

ENERGY 240: Data Science for Geoscience (EARTHSYS 240, EPS 140, EPS 240, ESS 239)

Overview of some of the most important data science methods (statistics, machine learning & computer vision) relevant for geological sciences, as well as other fields in the Earth Sciences. Areas covered are: extreme value statistics for predicting rare events; compositional data analysis for geochemistry; multivariate analysis for designing data & computer experiments; probabilistic aggregation of evidence for spatial mapping; functional data analysis for multivariate environmental datasets, spatial regression and modeling spatial uncertainty with covariate information (geostatistics). Identification & learning of geo-objects with computer vision. Focus on practicality rather than theory. Matlab exercises on realistic data problems.
Terms: Win | Units: 3
Instructors: Caers, J. (PI)

ENVRES 398: Directed Reading in Environment and Resources

For current matriculated E-IPER PhD and MS graduate students only. Under supervision of an E-IPER affiliated faculty member, students review the academic literature on a specific topic. Students work with a faculty instructor to develop reading lists and deliverables. E-IPER MS students may use five units of independent study course units towards their elective requirement for the degree and an additional one to three units toward preparation for their capstone project. E-IPER program consent required to enroll. Students interested in taking the course are required to fill out this proposal form: https://app.smartsheet.com/b/form/f0617c9ba0354dc6bcaf464d063ea329. Students who do not fill out this form will NOT receive credit for the course.
Terms: Aut, Win, Spr | Units: 1-15 | Repeatable for credit
Instructors: Algee-Hewitt, M. (PI) ; Anderson, M. (PI) ; Ardoin, N. (PI) ; Arrigo, K. (PI) ; Azevedo, I. (PI) ; Barnett, W. (PI) ; Barry, M. (PI) ; Barry, M. (PI) ; Basurto, X. (PI) ; Bendavid, E. (PI) ; Bennon, M. (PI) ; Benson, S. (PI) ; Billington, S. (PI) ; Boehm, A. (PI) ; Brandt, A. (PI) ; Burke, M. (PI) ; Burkett, M. (PI) ; Burney, J. (PI) ; Caers, J. (PI) ; Cain, B. (PI) ; Constantino, S. (PI) ; Crowder, L. (PI) ; Daily, G. (PI) ; Davis, S. (PI) ; De Leo, G. (PI) ; Diffenbaugh, N. (PI) ; Dirzo, R. (PI) ; Dunbar, R. (PI) ; Fendorf, S. (PI) ; Field, C. (PI) ; Flewellen, A. (PI) ; Frank, Z. (PI) ; Freyberg, D. (PI) ; Fukami, T. (PI) ; Fukuyama, F. (PI) ; Gardner, C. (PI) ; Goulder, L. (PI) ; Hayden, T. (PI) ; Heilpern, S. (PI) ; Hidalgo Reese, E. (PI) ; Holmes, R. (PI) ; Horne, R. (PI) ; Hoyt, A. (PI) ; Iancu, D. (PI) ; Jackson, R. (PI) ; Jacobson, M. (PI) ; Jain, R. (PI) ; Jean-Baptiste, R. (PI) ; Jones, J. (PI) ; Karaduman, O. (PI) ; Koseff, J. (PI) ; Kovscek, A. (PI) ; LaBeaud, D. (PI) ; Lambin, E. (PI) ; Leape, J. (PI) ; Lee, A. (PI) ; Lepech, M. (PI) ; Lo, N. (PI) ; Lobell, D. (PI) ; Luby, S. (PI) ; Maher, K. (PI) ; Martin, A. (PI) ; Martinez, J. (PI) ; Mayse, E. (PI) ; McColl, D. (PI) ; Micheli, F. (PI) ; Mordecai, E. (PI) ; Naylor, R. (PI) ; O'Connell, J. (PI) ; Osman, K. (PI) ; Plambeck, E. (PI) ; Rajagopal, R. (PI) ; Sapolsky, R. (PI) ; Satz, D. (PI) ; Seetah, K. (PI) ; Shaw, G. (PI) ; Sivas, D. (PI) ; Suckale, J. (PI) ; Tal, A. (PI) ; Tarpeh, W. (PI) ; Thompson, B. (PI) ; Weyant, J. (PI) ; White, E. (PI) ; Wilcox, M. (PI) ; Wong-Parodi, G. (PI)

ENVRES 399: Directed Research in Environment and Resources

For current matriculated E-IPER PhD and MS graduate students only. Under supervision of an E-IPER affiliated faculty member, students work on a research project. Students work with a faculty instructor to develop research expectations and deliverables. E-IPER MS students may use five units of independent study course units towards their elective requirement for the degree and an additional one to three units toward preparation for their capstone project. E-IPER program consent required to enroll. Students interested in this course are required to fill out this proposal form: https://app.smartsheet.com/b/form/f0617c9ba0354dc6bcaf464d063ea329. Students who do NOT fill out this form will not receive credit for the course.
Terms: Aut, Win, Spr | Units: 1-15 | Repeatable for credit
Instructors: Algee-Hewitt, M. (PI) ; Anderson, M. (PI) ; Ardoin, N. (PI) ; Arrigo, K. (PI) ; Azevedo, I. (PI) ; Bailenson, J. (PI) ; Baltaduonis, R. (PI) ; Barnett, W. (PI) ; Barry, M. (PI) ; Barry, M. (PI) ; Basurto, X. (PI) ; Bendavid, E. (PI) ; Benjamin-Chung, J. (PI) ; Bennon, M. (PI) ; Benson, S. (PI) ; Billington, S. (PI) ; Boehm, A. (PI) ; Brady, S. (PI) ; Brandt, A. (PI) ; Burke, M. (PI) ; Burkett, M. (PI) ; Burney, J. (PI) ; Caers, J. (PI) ; Cain, B. (PI) ; Constantino, S. (PI) ; Coslet, J. (PI) ; Crowder, L. (PI) ; Daily, G. (PI) ; Davis, S. (PI) ; De Leo, G. (PI) ; Demszky, D. (PI) ; Diffenbaugh, N. (PI) ; Dirzo, R. (PI) ; Dunbar, R. (PI) ; Epstein, J. (PI) ; Fendorf, S. (PI) ; Field, C. (PI) ; Flewellen, A. (PI) ; Freyberg, D. (PI) ; Fukami, T. (PI) ; Fukuyama, F. (PI) ; Gardner, C. (PI) ; Garfinkel, J. (PI) ; Gorelick, S. (PI) ; Goulder, L. (PI) ; Gu, W. (PI) ; Hayden, T. (PI) ; Hayes, D. (PI) ; Hidalgo Reese, E. (PI) ; Holmes, R. (PI) ; Honigsberg, C. (PI) ; Hoyt, A. (PI) ; Hsiang, S. (PI) ; Hummel, H. (PI) ; Iancu, D. (PI) ; Jackson, R. (PI) ; Jacobson, M. (PI) ; Jain, R. (PI) ; Jones, J. (PI) ; Karaduman, O. (PI) ; King, A. (PI) ; Klass, A. (PI) ; Konings, A. (PI) ; Koonin, S. (PI) ; Koseff, J. (PI) ; Kosinski, M. (PI) ; Kovscek, A. (PI) ; LaBeaud, D. (PI) ; Lambin, E. (PI) ; Leape, J. (PI) ; Lee, A. (PI) ; Lepech, M. (PI) ; Lo, N. (PI) ; Lobell, D. (PI) ; Luby, S. (PI) ; Martin, A. (PI) ; Martinez, J. (PI) ; McColl, D. (PI) ; Micheli, F. (PI) ; Moanga, D. (PI) ; Monk, A. (PI) ; Mordecai, E. (PI) ; Moxley, J. (PI) ; Mukerji, T. (PI) ; Nation, J. (PI) ; Naylor, R. (PI) ; O'Shea, T. (PI) ; Onori, S. (PI) ; Plambeck, E. (PI) ; Rajagopal, R. (PI) ; Rivas-Davila, J. (PI) ; Rodriguez Espinosa, P. (PI) ; Rogers, D. (PI) ; Rogers, M. (PI) ; Satz, D. (PI) ; Seetah, K. (PI) ; Seiger, A. (PI) ; Shaw, G. (PI) ; Singh, H. (PI) ; Sivas, D. (PI) ; Suckale, J. (PI) ; Tal, A. (PI) ; Tarpeh, W. (PI) ; Thille, C. (PI) ; Thompson, B. (PI) ; Weyant, J. (PI) ; White, E. (PI) ; Wilcox, M. (PI) ; Wong-Parodi, G. (PI)

ENVRES 801: TGR Project

TGR Project
Terms: Aut, Win, Spr | Units: 0 | Repeatable for credit

ENVRES 802: TGR Dissertation

TGR Dissertation
Terms: Aut, Win, Spr | Units: 0 | Repeatable for credit

EPS 6: Introduction to Data Science for Geoscience (EARTHSYS 100A)

This course provides an overview of the most relevant areas of data science to address geoscientific challenges and questions as they pertain to the environment, earth resources & hazards. The focus lies on the methods that treat common characters of geoscientific data: multivariate, multi-scale, compositional, geospatial and space-time. In addition, the course will treat those statistical method that allow a quantification of the human dimension by looking at quantifying impact on humans (e.g. hazards, contamination) and how humans impact the environment (e.g. contamination, land use). The course focuses on developing skills that are not covered in traditional statistics and machine learning courses.
Terms: Win | Units: 3 | UG Reqs: WAY-AQR | Repeatable 3 times (up to 9 units total)
Instructors: Caers, J. (PI)

EPS 190: Research in the Field (EPS 295)

Month-long courses that provide students with the opportunity to collect data in the field as part of a team-based investigation of researchquestions or topics under the expert guidance of knowledgeable faculty and graduate students. Topics and locations vary. May be taken multiple timesfor credit. Prerequisites: EPS 1, EPS 102, EPS 105. During the summer, juniors participate in this course. Please collaborate with Department Student ServiceOffice regarding the location, tuition, and the overall process. This is Capstone Option 1 for EPS Undergraduate majors.
Terms: Aut, Win, Spr | Units: 3-6 | Repeatable 3 times (up to 12 units total)

EPS 192: Undergraduate Research in Earth & Planetary Sciences

(Former GEOLSCI 192) Field-, lab-, or literature-based. Faculty supervision. Written reports. May be repeated for credit. Change of Department Name: Earth & Planetary Sciences (Formerly Geological Science)
Terms: Aut, Win, Spr | Units: 1-10 | Repeatable for credit
© Stanford University | Terms of Use | Copyright Complaints