Research - A Gentle Introduction to Deep Learning with Python and R

Machine learning methods are nowadays used in a wide variety of applications such as image and text classification, and weather forecasting. In this course, you will learn how a very popular method, namely Deep Learning (or Neural Network), works and may be applied in practice by using either Python or R programming. 

 Objectives 

 Acquire the key competencies needed to apply deep learning methods to simple datasets 

 Target audience 

 Any PhD students, post-docs, researchers of UNIL who would like to use deep learning methods in their research 

 Content 

 At the end of the course, the participants are expected to: 

 Understand how the deep learning (neural network) algorithm works 

 Run a simple machine learning code in Python or R 

 Be able to choose properly the hyper-parameters of the model 

 Length 

 1 half-day 

 Organization 

 Once per year 

 Location 

 In-person only (no online option) 

 Practicals 

 The practicals can be done on the UNIL JupyterLab (available exclusively during this course and for one week following its completion), on your laptop (but you will need to install the required libraries), or on the UNIL cluster called Curnagl. See the installation page for more information. 

 Prerequisites 

 Basic knowledge of statistics, including simple linear algebra techniques such as vectors, matrices and matrix multiplication 

 Be confortable with either Python or R programming 

 IMPORTANT: To do the practicals - On UNIL JupyterLab: Access requires that you connect either via the eduroam Wi-Fi with your UNIL account or through the UNIL VPN. This point is especially crucial for researchers from the CHUV. - On your laptop: No account requirement - On Curnagl: Please register using your UNIL email address - Note that in all cases you need to bring your own laptop 

 

 Course dates and registration