update slides

docs/index.html

@@ -244,7 +244,7 @@ <h1>Schedule</h1>
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 <td style="text-align: center;">11:30 - 12:00</td>
-<td style="text-align: center;"><a href="./lecture_notes/StatPrinciples_ML_3.pdf">Outlook: Hierarchical models and structured penalties</a></td>
+<td style="text-align: center;"><a href="./lecture_notes/Theo.pdf">Hierarchical models and structured penalties</a></td>
 <td style="text-align: center;">Theophilus Asenso</td>
 </tr>
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docs/search.json

@@ -46,7 +46,7 @@
     "href": "index.html",
     "title": "Statistical Principles in Machine Learning for Small Biomedical Data",
     "section": "",
-    "text": "Date: Monday 11 December 2023, 9:00-12:00\nRoom: Perl (room 2453), Ole-Johan Dahls hus (OJD)\nInstructors: Manuela Zucknick (main), Theophilus Asenso\n\n\nWelcome!\n\nThe goal of the workshop is to introduce kep concepts in machine learning, such as regularisation.\nThe workshop is intended for students and researchers who are interested in applying machine learning methods to small data (few samples, but potentially many features) or noisy data (e.g. biomedical data)\nWorkshop material can be found in the workshop github repository.\n\n\nLearning Objectives\nAt the end of the tutorial, participants will be able to\n\nunderstand key concepts for training machine learning models such as regularisation;\nunderstand how to incorporate data structure in the regularisation process.\n\n\n\nPre-requisites\n\nBasic familiarity with R\nIntroductory level statistics, including regression\n\n\n\n\nSchedule\n\n\n\nTime\nTopic\nPresenter\n\n\n\n\nNow\nPreparations\n\n\n\n9:00 - 10:00\n(Supervised) machine learning with small data\nManuela Zucknick\n\n\n\nR lab 1\nManuela Zucknick\n\n\n10:15 - 11:15\nOverfitting, regularisation and all that\nManuela Zucknick\n\n\n\nR lab 2\nManuela Zucknick\n\n\n11:30 - 12:00\nOutlook: Hierarchical models and structured penalties\nTheophilus Asenso"
+    "text": "Date: Monday 11 December 2023, 9:00-12:00\nRoom: Perl (room 2453), Ole-Johan Dahls hus (OJD)\nInstructors: Manuela Zucknick (main), Theophilus Asenso\n\n\nWelcome!\n\nThe goal of the workshop is to introduce kep concepts in machine learning, such as regularisation.\nThe workshop is intended for students and researchers who are interested in applying machine learning methods to small data (few samples, but potentially many features) or noisy data (e.g. biomedical data)\nWorkshop material can be found in the workshop github repository.\n\n\nLearning Objectives\nAt the end of the tutorial, participants will be able to\n\nunderstand key concepts for training machine learning models such as regularisation;\nunderstand how to incorporate data structure in the regularisation process.\n\n\n\nPre-requisites\n\nBasic familiarity with R\nIntroductory level statistics, including regression\n\n\n\n\nSchedule\n\n\n\nTime\nTopic\nPresenter\n\n\n\n\nNow\nPreparations\n\n\n\n9:00 - 10:00\n(Supervised) machine learning with small data\nManuela Zucknick\n\n\n\nR lab 1\nManuela Zucknick\n\n\n10:15 - 11:15\nOverfitting, regularisation and all that\nManuela Zucknick\n\n\n\nR lab 2\nManuela Zucknick\n\n\n11:30 - 12:00\nHierarchical models and structured penalties\nTheophilus Asenso"
   },
   {
     "objectID": "about.html",

index.qmd

@@ -37,4 +37,4 @@ At the end of the tutorial, participants will be able to
 |               |                                    [R lab 1](part1_eda.qmd)                                     | Manuela Zucknick  |
 | 10:15 - 11:15 |       [Overfitting, regularisation and all that](/lecture_notes/StatPrinciples_ML_2.pdf)        | Manuela Zucknick  |
 |               |                                   [R lab 2](part2_model.qmd)                                    | Manuela Zucknick  |
-| 11:30 - 12:00 | [Outlook: Hierarchical models and structured penalties](/lecture_notes/StatPrinciples_ML_3.pdf) | Theophilus Asenso |
+| 11:30 - 12:00 | [Hierarchical models and structured penalties](/lecture_notes/Theo.pdf) | Theophilus Asenso |