@@ -31,7 +31,7 @@ videoconference to the class every monday from 15:45 to 17:45.
##### Lab7 instructions (October 24th and 25th)
- lab7_statement statement on clustering is [here](https://gricad-gitlab.univ-grenoble-alpes.fr/chatelaf/ml-sicom3a/-/blob/master/labs/lab7_statement.md)
- lab7 statement on clustering is [here](https://gricad-gitlab.univ-grenoble-alpes.fr/chatelaf/ml-sicom3a/-/blob/master/labs/lab7_statement.md)
- Upload **at the end of the session** your lab 7 *short report* in the [chamilo assigment task](https://chamilo.grenoble-inp.fr/main/work/work_list.php?cidReq=PHELMA5PMSAST6&id_session=0&gidReq=0&gradebook=0&origin=&id=1239(pdf file from your editor, or scanned pdf file of a handwritten paper; code, figures or graphics are not required)
##### Homework for **Monday, October 24 (Group2) or Tuesday October 25 (Group 1)**
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@@ -103,14 +103,20 @@ code, figures or graphics are not required)~~
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##### Homework before the first lab on **Friday, September 29**
- read and run the [introduction notebooks](https://gricad-gitlab.univ-grenoble-alpes.fr/chatelaf/ml-sicom3a/-/tree/master/notebooks/1_introduction/)`N1_Linear_Classification.ipynb` and `N2_Polynomial_Classification_Model_Complexity.ipynb`
- answer the questions of the notebook exercises and upload it (pdf file from your editor, or scanned pdf file of a handwritten sheet) under chamilo in the [assignment tool](https://chamilo.grenoble-inp.fr/main/work/work_list.php?cidReq=PHELMA5PMSAST6&id_session=0&gidReq=0&gradebook=0&origin=&id=117272)(those and only those who do not yet have an agalan account can send it to me by email):
- only text explanations are required, no need to copy/paste figure or graphics!
- must not exceed half a length of A4 paper
##### Lab1 instructions (Friday, September 29)
- Lab1 statement is [here](https://gricad-gitlab.univ-grenoble-alpes.fr/chatelaf/ml-sicom3a/-/blob/master/labs/lab1_statement.md)
<!-- - upload your lab 1 *short report* in the [chamilo assigment task](https://chamilo.grenoble-inp.fr/main/work/work_list.php?cidReq=PHELMA5PMSAST6&id_session=0&gidReq=0&gradebook=0&origin=&id=117582)(pdf file from your editor, or scanned pdf file of a handwritten paper;
code, figures or graphics are not required)
-->
##### ~~Homework before the first lab on **Friday, September 29**~~
- ~~read and run the [introduction notebooks](https://gricad-gitlab.univ-grenoble-alpes.fr/chatelaf/ml-sicom3a/-/tree/master/notebooks/1_introduction/)`N1_Linear_Classification.ipynb` and `N2_Polynomial_Classification_Model_Complexity.ipynb`~~
- ~~answer the questions of the notebook exercises and upload it (pdf file from your editor, or scanned pdf file of a handwritten sheet) under chamilo in the [assignment tool](https://chamilo.grenoble-inp.fr/main/work/work_list.php?cidReq=PHELMA5PMSAST6&id_session=0&gidReq=0&gradebook=0&origin=&id=117272)(those and only those who do not yet have an agalan account can send it to me by email):~~
- ~~only text explanations are required, no need to copy/paste figure or graphics!~~
- ~~must not exceed half a length of A4 paper~~
##### First course session will take place Monday 10:30, September 24 at Minatec Z108.
##### ~~First course session will take place Monday 10:30, September 24 at Minatec Z108.~~
## Welcome to the Statistical Learning course!
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@@ -151,6 +157,5 @@ In order to run them you have several possibilities:
### Miscellaneous remarks on the materials
- The slides are designed to be self-sufficient (even if the narrative side is often limited by the format).
- In addition to the slides and bibliographical/web references, we generally propose links or videos (at the beginning or end of the slides) specific to the concepts presented. These lists are of course not exhaustive, and you will find throughout the web many resources, often pedagogical. Feel free to do your own research.<!-- and share it on the [Riot room](https://riot.ensimag.fr/#/room/#sicom-ml:ensimag.fr) if you find it useful. -->
- In addition to the slides and bibliographical/web references, we generally propose links or videos (at the beginning or end of the slides) specific to the concepts presented. These lists are of course not exhaustive, and you will find throughout the web many resources, often pedagogical. Feel free to do your own research.
<!-- - [](https://mybinder.org/v2/git/https%3A%2F%2Fgricad-gitlab.univ-grenoble-alpes.fr%2Fchatelaf%2Fml-sicom3a/54301940e4486a8ece22a910c3efa1b2734ed82d?filepath=notebooks) link to run the examples, *except Deep learning ones* too computationally demanding for the JupyterHub server (use the first solution to run these notebooks with your own ressources...) -->
