members:aguilloux:enseignements:machinelearningpython
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members:aguilloux:enseignements:machinelearningpython [2017/11/17 10:23] Agathe Guilloux |
members:aguilloux:enseignements:machinelearningpython [2017/11/27 09:59] Agathe Guilloux |
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| === Slides === | === Slides === | ||
| {{:members:aguilloux:enseignements:machinelearningpython:cours_boosting.pdf|Boosting}} | {{:members:aguilloux:enseignements:machinelearningpython:cours_boosting.pdf|Boosting}} | ||
| + | {{:members:aguilloux:enseignements:machinelearningpython:cours_random_forests.pdf| Random forest}} | ||
| === Labs === | === Labs === | ||
| - | = Lab 1= | + | == Lab 1== |
| * {{:members:aguilloux:enseignements:machinelearningpython:ml_lab_1.ipynb.zip| Lab 1}} | * {{:members:aguilloux:enseignements:machinelearningpython:ml_lab_1.ipynb.zip| Lab 1}} | ||
| * {{:members:aguilloux:enseignements:machinelearningpython:ml_lab_1_corrected.ipynb.zip| Lab 1 corrected}} | * {{:members:aguilloux:enseignements:machinelearningpython:ml_lab_1_corrected.ipynb.zip| Lab 1 corrected}} | ||
| - | = Lab 2 = | + | == Lab 2 == |
| * {{:members:aguilloux:enseignements:machinelearningpython:mnist.ipynb.zip| Lab 2}} | * {{:members:aguilloux:enseignements:machinelearningpython:mnist.ipynb.zip| Lab 2}} | ||
| - | = Lab 3 = | + | == Lab 3 == |
| * Lab 3 : comparer les algorithmes de ML sur https://archive.ics.uci.edu/ml/datasets/spambase | * Lab 3 : comparer les algorithmes de ML sur https://archive.ics.uci.edu/ml/datasets/spambase | ||
| * https://codeshare.io/5zPpwk | * https://codeshare.io/5zPpwk | ||
| Line 16: | Line 17: | ||
| === Challenge === | === Challenge === | ||
| The course will be evaluated via the Kaggle competition https://www.kaggle.com/c/porto-seguro-safe-driver-prediction#timeline. Vous devez former des équipes de 3 personnes maximum. Vous devrez rendre un rapport de 10 pages maximum (hors annexes) expliquant votre démarche. | The course will be evaluated via the Kaggle competition https://www.kaggle.com/c/porto-seguro-safe-driver-prediction#timeline. Vous devez former des équipes de 3 personnes maximum. Vous devrez rendre un rapport de 10 pages maximum (hors annexes) expliquant votre démarche. | ||
| + | Your reports have to be send by **January 12.** | ||
members/aguilloux/enseignements/machinelearningpython.txt · Last modified: 2017/11/27 09:59 by Agathe Guilloux
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