Teaching
2021/2022
- Computer Vision: Models, Learning, and Inference (Semester still undetermined)
- Introduction to Graphical Models and Deep Learning (Spring, for sure)
- Introduction to Graphical Models and Deep Learning (Fall, maybe)
2020/2021
- Computer Vision: Models, Learning, and Inference (Spring)
- Introduction to Graphical Models and Deep Learning (Spring)
- Topics in Unsupervised Learning -- min project (Fall)
Previous Years (at BGU, unless stated otherwise)
- Computer Vision: Models, Learning, and Inference (2019/2020, Spring; 2019/2020, Fall; 2018/2019, Spring)
- Machine Learning in Computer Vision (2017/2018, Spring).
- Methods in Computer Vision (2016/2017, Spring).
- Topics in Computer Vision -- mini project (2016/2017, Fall)
- Introduction to Numerical Analysis (2019/2020, Fall; 2018/2019, Fall; 2017/2018, Fall)
- At MIT. An unofficial grad-level class, for EECS students, on Lie groups and Stochastic Processes. Fall 2013
Previous Classes Taught as a TA or a Lab Instructor
- Brown University, 2008/2009, Spring. TA in AM 169 - Computational Probability & Statistics (lecturer: Luis Carvahlo).
- Brown University, 2008/2009, Fall. TA in AM 261 (graduate level) - Applications in Probability & Statistics (lecturer: Stuart Geman).
- TAU, 2005/2006, Spring. Instructor in Medical Image Processing Laboratory.
- TAU, 2005/2006, Fall. TA in Introduction to Radiation and Medical Imaging.