https://github.com/yandexdataschool/mlatimperial2017
Materials for the course of machine learning at Imperial College organized by Yandex SDA
https://github.com/yandexdataschool/mlatimperial2017
deep-learning imperial-college keras lectures machine-learning practice scikit-learn sklearn theano yandex
Last synced: about 2 months ago
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Materials for the course of machine learning at Imperial College organized by Yandex SDA
- Host: GitHub
- URL: https://github.com/yandexdataschool/mlatimperial2017
- Owner: yandexdataschool
- Created: 2017-01-13T19:07:22.000Z (over 8 years ago)
- Default Branch: master
- Last Pushed: 2017-02-09T14:44:07.000Z (over 8 years ago)
- Last Synced: 2023-10-20T23:54:36.620Z (over 1 year ago)
- Topics: deep-learning, imperial-college, keras, lectures, machine-learning, practice, scikit-learn, sklearn, theano, yandex
- Language: Jupyter Notebook
- Homepage:
- Size: 100 MB
- Stars: 79
- Watchers: 19
- Forks: 50
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
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README
# Machine Learning, Imperial College London 2017
[](https://gitter.im/MLatImperial2017/Lobby?utm_source=badge&utm_medium=badge&utm_campaign=pr-badge&utm_content=badge)
[](https://everware.rep.school.yandex.net/hub/oauth_login?repourl=https://github.com/yandexdataschool/MLatImperial2017.git)A two-weeks in-depth course of machine learning organized by [Yandex Data School](https://yandexdataschool.com) at Imperial College. Contains theory and **much** practice!
Main topics:
- python, scientific python (numpy, scipy, matplotlib)
- python for data science (pandas, sklearn)
- metric models
- linear models
- tree-based models and ensembles, in particular boosting
- dimensionality reduction
- tensor computations and neural networks (theano and keras)## Challenges
There were two challenges during the course:
- restaraunt reviews classification
- flavour tagging of B mesons