Workshop on Machine Learning techniques

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THE REGISTRATION IS CLOSED

1. The Workshop will be fully held in English
2. In the current situation of the Covid pandemic the Workshops of Machine Learning will be taken remotely on Zoom platform. If the pandemic situation will not worsen we forsee to change the format to regular meeting in the Faculty of Physics.
3. We will use the Google’s cloud service: Google Colaboratory  for practical exercises. A login to Google account will be necessary. In case of  technical problems with registration, please send an email to: center4ml@uw.edu.pl
4. The details on Zoom meeting will be send by email to registered participants.

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The Workshops on Machine Learning techniques are directed to UW employees and students interested in the topic. We would like to present the theory and general basics of machine learning  – during the workshops will be construct a fully operational neural network performing a classification task. We will show two main of environments used in Machine Learning community: PyTorch and TensorFlow.  Prior experience and/or knowledge in this subject in not required.

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The Workshop program

 

26.03.2022 (Saturday)

09:00 – 09:10 – Open and presentation of Center for Machine Learning (PL) (B. Lesyng,  A. Kalinowski)

09:10 – 09:45 – practical session: Introduction to tools and packages used in Machine Learning community (A.Kalinowski)

10:00 – 11:00 – lecture: Introduction to methods of Machine Learning (J. Żygierewicz)

11:00 – 11:15 – break

11:15 – 12:15 – lecture: Introduction to Artificial Neural Networks (J. Żygierewicz)

12:15 – 13:15 – lunch break

13:15 – 14:00 – practical session: Introduction to loss functions (Sz. Nowakowski)

14:00 – 14:15 – break

14:15 – 15:00 – practical session: Simple net MLP – implementation in PyTorch (Sz. Nowakowski)

15:00 – 15:15 – break

15:15 – 16:00 – practical session:  Simple net MLP – implementation in TensorFlow (A. Kalinowski)

 

 

02.04.2022 (Saturday)

09:00 – 09:45 – lecture: Autoencoders – introduction and examples (M. Bukowicki)

09:45 – 10:00 – break

10:00 – 10:45 – practical session: Implementation of Autoencoders – part 1 (M. Bukowicki)

10:45 – 11:45 – lunch break

11:45 – 13:15 – lecture: Introduction to recurrent neural networks (P. Olbratowski)

13:15 – 13:30 – break

13:30 – 14:15 – practical session: Implementation of Autoencoders – part 2 (M. Bukowicki)