This Data Science course is taught at the Faculty of Science, Masaryk University, in the fall semester 2021/2022.
My plan is to give 12 lectures, each focused on one ML technique and dataset (typically from kaggle.com). The emphasis has been on coding and practicing data science skills, rather than the theoretical background.
The course is now in the IS Course Catalogue, look for M7DataSP Data Science Practicum (Praktikum z pokročilé datové vědy).
The course is scheduled for Thursdays, 10:00-11:30. The first lecture will take place on September 16. To be invited to the classes, enroll to the course in IS.
No special knowledge is expected but you should have at least one year of coding experience, either R or Python. I like diverse crowds; students from different faculties and specializations are encouraged to enroll (if still in doubt, let me know to be paired with a more experienced student). The course will be taught in English if at least two students will be interested, otherwise in Czech.
- Lesson 01 (16.9.): Introduction
- Lesson 02 (23.9.): Python
- Lesson 03 (30.9.): Convolutional Neural Networks
- Lesson 04 (7.10.): Transfer Learning
- Lesson 05 (14.10.): Backpropagation
- Lesson 06 (21.10.): Time Series: Statistical Methods
- Lesson 07 (4.11.): The Unix Shell
- Lesson 08 (11.11.): NLP
- Lesson 09 (18.11.): Transformers
- Lesson 10 (25.11.): Optimizers, NLP methods in Genomics
- Lesson 11 (2.12.): Recommenders, Tabular data
- Lesson 12 (9.12.): Explainable ML, Hyperparameters search, AutoML
Last year materials: https://github.com/simecek/dspracticum2020
We will be communicating through Slack channel https://app.slack.com/client/T02E6TBAJ0P/C02E6TBBHU7. Enroll to the course to be invited.
- Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd edition)
- Deep Learning with Python, Second Edition
- TensorFlow 2 in 30 days
- Deep Learning for Coders with fastai and PyTorch: AI Applications Without a PhD
- RStudio AI Blog
- The Missing Semester of Your CS Education
This work would be impossible without tutorials provided by TensorFlow and RStudio. I also get a lot of inspiration from numerous Kagle notebooks and blogs all over the internet. Sometimes, in a time pressure before the lecture, I might have forgotten to properly link all my sources. If this is the case, I would be grateful if you correct my mistake, either by a pull request or sending me a message. Thank you.