Week 4 - Revision history

Wiki: /* X: Experiment planning */

2025-03-13T10:50:21Z

X: Experiment planning

Wiki: /* X: Experiment planning */

2025-03-13T10:49:53Z

X: Experiment planning

Wiki: /* Resources to read */

2025-03-13T10:48:12Z

Resources to read

Wiki: /* Resources to read */

2025-03-13T10:43:14Z

Resources to read

Wiki: /* Resources to read */

2025-03-13T10:40:01Z

Resources to read

Wiki: /* Resources to read */

2025-03-13T10:39:31Z

Resources to read

Wiki: /* Homework */

2025-03-13T10:38:41Z

Homework

Wiki at 10:33, 13 March 2025

2025-03-13T10:33:57Z

Wiki: /* R: Preliminary report */

2025-03-13T09:27:27Z

R: Preliminary report

Wiki: /* Site and docs generators */

2025-02-27T13:27:15Z

Site and docs generators

@@ Line 9: / Line 9: @@
 ==X: Experiment planning ==
-Plan your computational experiment
+Plan your computational experiment.
-# Discuss the experiment goal with your adviser and team.
+# Discuss the experiment goal with your adviser
 #* and put this goal in the section Computational experiment
 # Describe your basic data set, a synthetic, or a simple real one:

@@ Line 9: / Line 9: @@
 ==X: Experiment planning ==
-Plan your computational experiment.
+Plan your computational experiment
 # Discuss the experiment goal with your adviser and team.
-#* Put this goal in the section Computational experiment
+#* and put this goal in the section Computational experiment
 # Describe your basic data set, a synthetic, or a simple real one:
 #* put in the text the title, source, and set up of measurements (it is the technical description, the theoretical one is in the problem statement section),

@@ Line 57: / Line 57: @@
 # [http://svn.code.sf.net/p/mlalgorithms/code/Group474/Uvarov2017DynamicGraphicalModels/slides/Uvarov2017DynamicGraphicalModels.pdf Н. Уваров]
-Example of the measurement description,
+An example of the measurement description is [http://www.machinelearning.ru/wiki/images/3/35/Old_Faithful_dataset_description.pdf Old Faithful] by Bishop C.P. Pattern recognition and machine learning, 2006. Pp. 677-683.
 <!-- * Построение выборки в задачах прогнозирования, [http://svn.code.sf.net/p/mvr/code/lectures/DataFest/Strijov2016Tutorial.pdf слайды]. EXTRACT The feature generation part-->
 <!-- * Постановка задачи прогнозирования дефолтов по картам на год вперед, [[Media:Strijov2018ProbStCardScoring.pdf|слайды]] -->

@@ Line 64: / Line 64: @@
 Dataset sources
-* [https://medium.datadriveninvestor.com/top-8-sources-for-machine-learning-and-analytics-datasets-5d2d94ada8ab Top 8 Sources For Machine Learning Datasets]
+# [https://medium.datadriveninvestor.com/top-8-sources-for-machine-learning-and-analytics-datasets-5d2d94ada8ab Top 8 Sources For Machine Learning Datasets]
-* [https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research List of data-sets for Machine Learning projects]
+# [https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research List of data-sets for Machine Learning projects]
 ==Homework==

@@ Line 65: / Line 65: @@
 Dataset sources
 * [https://medium.datadriveninvestor.com/top-8-sources-for-machine-learning-and-analytics-datasets-5d2d94ada8ab Top 8 Sources For Machine Learning Datasets]
-* [https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research List of data-sets for Machine Learning projects]]
+* [https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research List of data-sets for Machine Learning projects]
 ==Homework==

@@ Line 55: / Line 55: @@
 #[http://svn.code.sf.net/p/mlalgorithms/code/Group574/Rogozina2018StructurePredictionRNA/doc/slides/Rogozina2018RNAPredictionsSlides.pdf А. Рогозина],
 # [https://github.com/Intelligent-Systems-Phystech/Group594/raw/master/Igashov2018ProteinLigandComplexes/presentation/presentation.pdf И. Игашов],
-# [http://svn.code.sf.net/p/mlalgorithms/code/G roup474/Uvarov2017DynamicGraphicalModels/slides/Uvarov2017DynamicGraphicalModels.pdf Н. Уваров]
+# [http://svn.code.sf.net/p/mlalgorithms/code/Group474/Uvarov2017DynamicGraphicalModels/slides/Uvarov2017DynamicGraphicalModels.pdf Н. Уваров]
 Example of the measurement description,

@@ Line 76: / Line 76: @@
 ## '''X''' put the goal of your experiment into the section Computational Experiment.
 ## '''R''' put the plan of the hypothesis testing and illustration after.
 ## '''B''' upload the notebook or code with a basic experiment into your repository.
 ==References==

@@ Line 34: / Line 34: @@
 Select the basic algorithm and run it using a simple data set.
-# Run your basic algorithm:
+# Run your basic algorithm: select the simplest algorithm to get the fastest draft solution of the problem you set.
 # Collect a synthetic data set or download a simple real-world data set of small size.
-# Upload your data to the repository (in case the data size exceeds 5MB or the data set consists of numerous files, please discuss with your adviser and team).
+# Upload your data to the repository. If the data size exceeds 5MB or the data set consists of numerous files, please discuss with your adviser and team how to keep and share these data.
 # Run the basic algorithm on the synthetic data set, and estimate the error.
 # Describe the basic algorithm, analyze its features, and list competitive models. Here the examples of the description style.
@@ Line 44: / Line 44: @@
 ## Description of the model and algorithm for optimizing its parameters in the form of pseudocode.
-==Resources==
+==Resources to read==
-* [http://www.machinelearning.ru/wiki/images/4/45/M1p_lect4.pdf Slides for week 4]. Slides [http://www.machinelearning.ru/wiki/images/c/c3/M1p2022lect4.pdf 2022].
+See examples inside the reports.
-* [https://youtu.be/8viZLYFfBsM  Video for week 4].
+# [https://m1p.org/papers/Bakhteev2016AWS.pdf Системы и средства глубокого обучения], Бахтеев О.Ю.
-* See examples of the reports.
+# [https://m1p.org/papers/MolybogMotrenko2017DimRed.pdf Повышение качества классификации], Мотренко А.П.
-*# Бахтеев О.Ю. Системы и средства глубокого обучения, [http://strijov.com/papers/Bakhteev2016AWS.pdf статья]
+# [https://github.com/Intelligent-Systems-Phystech/2017-Isachenko-PLS/raw/master/doc/Isachenko2017PLS.pdf Снижение размерности в задаче декодирования],  Исаченко Р.В.
-*# Мотренко А.П. Повышение качества классификации, [http://strijov.com/papers/MolybogMotrenko2017DimRed.pdf статья]
-*# Исаченко Р.В. Снижение размерности в задаче декодирования, [https://github.com/Intelligent-Systems-Phystech/2017-Isachenko-PLS/raw/master/doc/Isachenko2017PLS.pdf статья]
+Mimic the goals of computational experiments.
-*# The goals of computational experiments [http://svn.code.sf.net/p/mlalgorithms/code/Group574/Grabovoy2018OptimalBrainDamage/doc/slides/Grabovoy2018OptimalBrainDamage.pdf А. Грабовой], [http://svn.code.sf.net/p/mlalgorithms/code/Group474/Alekseev2017IntraTextCoherence/doc/Alekseev2017Presentation.pdf В. Алексеев], [http://svn.code.sf.net/p/mlalgorithms/code/Group574/Rogozina2018StructurePredictionRNA/doc/slides/Rogozina2018RNAPredictionsSlides.pdf А. Рогозина], [https://github.com/Intelligent-Systems-Phystech/Group594/raw/master/Igashov2018ProteinLigandComplexes/presentation/presentation.pdf И. Игашов], [http://svn.code.sf.net/p/mlalgorithms/code/Group474/Uvarov2017DynamicGraphicalModels/slides/Uvarov2017DynamicGraphicalModels.pdf Н. Уваров]
+#[http://svn.code.sf.net/p/mlalgorithms/code/Group574/Grabovoy2018OptimalBrainDamage/doc/slides/Grabovoy2018OptimalBrainDamage.pdf А. Грабовой],
-*# Example of the measurement description, [http://www.machinelearning.ru/wiki/images/3/35/Old_Faithful_dataset_description.pdf Bishop C.P. Pattern recognition and machine learning, 2006. Pp. 677-683.]]
+#[http://svn.code.sf.net/p/mlalgorithms/code/Group474/Alekseev2017IntraTextCoherence/doc/Alekseev2017Presentation.pdf В. Алексеев],
 <!-- * Построение выборки в задачах прогнозирования, [http://svn.code.sf.net/p/mvr/code/lectures/DataFest/Strijov2016Tutorial.pdf слайды]. EXTRACT The feature generation part-->
 <!-- * Постановка задачи прогнозирования дефолтов по картам на год вперед, [[Media:Strijov2018ProbStCardScoring.pdf|слайды]] -->
 <!-- * [http://www.machinelearning.ru/wiki/images/4/49/Strijov2019IDEF0.pdf The IDEF standard for project planning] OLD version -->
 * [https://medium.datadriveninvestor.com/top-8-sources-for-machine-learning-and-analytics-datasets-5d2d94ada8ab Top 8 Sources For Machine Learning Datasets]
 * [https://en.wikipedia.org/wiki/List_of_datasets_for_machine_learning_research List of data-sets for Machine Learning projects]]
 ==Homework==
 # Write the goal of your computational experiment. A couple of sentences help you focus your efforts.
-# Write a draft of your desired report and draw a plot for the error analysis next step.
+# Find the code that works to run the preliminary experiment.
 # Run the code on the simplest dataset.
+# Write a draft of your desired report and draw a plot for the error analysis.
 ==References==
 # Research Methodology: Methods and Techniques by C.R. Kothari, 2004 [https://www.academia.edu/22328603/Kothari_Research_Methodology_Methods_and_Techniques pdf] or [http://ndl.ethernet.edu.et/bitstream/123456789/79439/5/Research%20Methodology%20-%20Methods%20and%20Techniques%202004.pdf pdf]
+# Experiment planning: [https://arxiv.org/pdf/2407.12220 Questionable practices in machine learning] by Gavin Leech et al., 2024
+===Site and docs generators===
 # [https://www.mkdocs.org/ MkDocs] is an easy one
 # [https://www.sphinx-doc.org/en/master/ Sphinx] is more complex
-## An example is [https://intsystems.github.io/relaxit/index.html RelaxIt]
+# An example is [https://intsystems.github.io/relaxit/index.html RelaxIt]

@@ Line 24: / Line 24: @@
 ==R: Preliminary report ==
-# Make sure that the obtained results are in not logical (sic!) contradiction with the goals of the computational experiment.
+# Make sure that the obtained results ''do not logically contradict'' the goals of the computational experiment.
-# Illustrate the obtained results with the preliminary plot. Optimally this plot is hand-made. '''Just draw it with a pencil on a piece of paper.''' See [http://www.machinelearning.ru/wiki/images/3/30/Likelihood_handdrawn.pdf an example]. For the final version [http://www.machinelearning.ru/wiki/index.php?title=JMLDA/Fig use this format].
+# Illustrate the obtained results with the preliminary plot. Optimally this plot is hand-made. '''Just draw it with a pencil on a piece of paper.''' See [http://www.machinelearning.ru/wiki/images/3/30/Likelihood_handdrawn.pdf for an example]. For the final version [http://www.machinelearning.ru/wiki/index.php?title=JMLDA/Fig use this format].
 # Write a mini-report on the results with
 ## a short description of the figure: what the reader could see, what are the consequences,

@@ Line 74: / Line 74: @@
 # [https://www.mkdocs.org/ MkDocs] is an easy one
 # [https://www.sphinx-doc.org/en/master/ Sphinx] is more complex
 ==Experiment planning==
 # [https://arxiv.org/pdf/2407.12220 Questionable practices in machine learning] by Gavin Leech et al., 2024