In Fall 2019, IDRE RTG will offer a series of classes on machine learning to the campus. The objective of the series is to present overviews to the exciting machine learning techniques and to provide a practical guide for general audience to step into the field. This series is primarily appropriate for the beginners who want to learn the techniques and apply to their future research activities. Researchers with machine learning experiences are expected to get benefits from related discussions as well.

In the fourth session in the series, we will focus on deep learning and use Dogs-vs-Cats Kaggle Challenge as the case study. The topics will include:

1. Introduction to Data Augmentation techniques.
2. Using Data Augmentation to improve the solution for the Dogs-vs-Cats Kaggle Challenge.
3. Introducing transfer learning techniques to the Dogs-vs-Cats Kaggle Challenge.

No specific prerequisite is required to understand most parts of the talks. The current session assumes the knowledge of topics covered in the third session. Working experience of Python, Jupyter Notebooks and linear algebra will be helpful.

If you have any further questions regarding the workshop, please contact Qiyang Hu.