• Introduced 10-15 staff scientists and PhD students to writing linear regression and classification models using low-level TensorFlow.
  • Included introduction to TensorBoard and canned estimators.
  • Prepared interactive Jupyter notebooks and accompanied exercises.

  • Introduced 10-15 staff scientists and PhD students with various backgrounds, including non-computational, to the basics of programming in Python.
  • Included introduction to popular Python modules numpy, matplotlib, and pandas.

Introduced students to common performance measures for supervised and unsupervised machine learning methods.

  • Introduced 20–30 computer science master students to basic concepts of support vector machines.
  • Prepared and graded accompanied exercises.
  • Supervised students in final projects.