Introduction to Deep Learning with TensorFlow

  • 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.

Introduction to Programming with Python

  • 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.

Evaluation Measures

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

Linear Classifiers and Support Vector Machines

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