Course modules: MVE441 / MSA220 Statistical learning for big data

Planning

Planning Planning

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Plan for the course Plan for the course 1

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Examples of old exams

Examples of old exams Examples of old exams

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Old exams Old exams 1

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Page

Expectations on solutions for Exam June 2020 Expectations on solutions for Exam June 2020 2

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Projects

Projects Projects

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Updated: General instructions for projects Updated: General instructions for projects 1

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Attachment

Project 1 - 2023.html Project 1 - 2023.html 4

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Score at least Must score at least to complete this module item Scored at least Module item has been completed by scoring at least View Must view in order to complete this module item Viewed Module item has been viewed and is complete Mark completed Must mark this module item done in order to complete Marked completed Module item marked as done and is complete Contribute Must contribute to this module item to complete it Contributed Contributed to this module item and is complete Submit Must submit this module item to complete it Submitted Module item submitted and is complete
Assignment

[Project 1] Presentation [Project 1] Presentation 1 7

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Assignment

[Project 1] Code [Project 1] Code 1 8

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Context Module Subheader

Project 2 10

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Assignment

[Project 2] Presentation [Project 2] Presentation 1 13

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Assignment

[Project 2] Code [Project 2] Code 1 14

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Attachment

Project 2 description Project 2 description 17

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Attachment

CATSnDOGS.csv CATSnDOGS.csv 18

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Attachment

Labels.csv Labels.csv 19

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Attachment

Missing value imputation: Excerpt from Andrew Gelman's book Missing value imputation: Excerpt from Andrew Gelman's book 20

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Context Module Subheader

Project 3 21

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Assignment

[Project 3] Presentation [Project 3] Presentation 1 24

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Score at least Must score at least to complete this module item Scored at least Module item has been completed by scoring at least View Must view in order to complete this module item Viewed Module item has been viewed and is complete Mark completed Must mark this module item done in order to complete Marked completed Module item marked as done and is complete Contribute Must contribute to this module item to complete it Contributed Contributed to this module item and is complete Submit Must submit this module item to complete it Submitted Module item submitted and is complete
Assignment

[Project 3] Code [Project 3] Code 1 25

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Score at least Must score at least to complete this module item Scored at least Module item has been completed by scoring at least View Must view in order to complete this module item Viewed Module item has been viewed and is complete Mark completed Must mark this module item done in order to complete Marked completed Module item marked as done and is complete Contribute Must contribute to this module item to complete it Contributed Contributed to this module item and is complete Submit Must submit this module item to complete it Submitted Module item submitted and is complete
Attachment

Project3-2023.html Project3-2023.html 26

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Score at least Must score at least to complete this module item Scored at least Module item has been completed by scoring at least View Must view in order to complete this module item Viewed Module item has been viewed and is complete Mark completed Must mark this module item done in order to complete Marked completed Module item marked as done and is complete Contribute Must contribute to this module item to complete it Contributed Contributed to this module item and is complete Submit Must submit this module item to complete it Submitted Module item submitted and is complete
Attachment

Cancerdata.txt Cancerdata.txt 27

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Attachment

TCGAdata.txt TCGAdata.txt 28

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Attachment

TCGAlabels TCGAlabels 29

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Additional Material

Additional Material Additional Material

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Resources for self-studies Resources for self-studies 1

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Attachment

[Python] Programming tips and common mistakes [Python] Programming tips and common mistakes 2

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Attachment

[Python, Jupyter Notebook] Programming tips and common mistakes [Python, Jupyter Notebook] Programming tips and common mistakes 3

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Attachment

[R] Programming tips and common mistakes [R] Programming tips and common mistakes 4

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Attachment

[R, Jupyter Notebook] Programming tips and common mistakes [R, Jupyter Notebook] Programming tips and common mistakes 5

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Lecture 1: Introduction

Lecture 1: Introduction Lecture 1: Introduction

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Lecture 1 - Recommended Reading Lecture 1 - Recommended Reading 1

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Attachment

[PDF] Course information and formalities [PDF] Course information and formalities 2

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Attachment

Lecture1-2023.pdf Lecture1-2023.pdf 3

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Attachment

R code to generate figures in Lecture 1 R code to generate figures in Lecture 1 4

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Lecture 2: Model-based classification

Lecture 2: Model-based classification Lecture 2: Model-based classification

Complete one item

Page

Lecture 2 - Recommended Reading Lecture 2 - Recommended Reading 1

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Attachment

[pdf] Lecture 2: model-based classification [pdf] Lecture 2: model-based classification 7

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Lecture 3: Parametric classification, Model evaluation and bias-variance tradeoff

Lecture 3: Parametric classification, Model evaluation and bias-variance tradeoff Lecture 3: Parametric classification, Model evaluation and bias-variance tradeoff

Page

Lecture 3 - Recommended Reading-2 Lecture 3 - Recommended Reading-2 1

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Page

Lecture 3 - Additional Material Lecture 3 - Additional Material 3

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Context Module Subheader

Articles referenced in the PDF on variance of the CV error 12

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Attachment

Nadeau and Bengio (2003).pdf Nadeau and Bengio (2003).pdf 13

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Attachment

Bengio and Grandvalet (2004).pdf Bengio and Grandvalet (2004).pdf 14

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Attachment

Jiang and Wang (2017).pdf Jiang and Wang (2017).pdf 15

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Attachment

Markatou et al. (2005).pdf Markatou et al. (2005).pdf 16

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Attachment

Lecture3-2023.pdf Lecture3-2023.pdf 17

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Attachment

Demo-CV-classification (1).html [type corrected] Demo-CV-classification (1).html [type corrected] 21

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Lecture 4: Rule-based classification and regression

Lecture 4: Rule-based classification and regression Lecture 4: Rule-based classification and regression

Page

Lecture 4 - Recommended Reading and Additional Material Lecture 4 - Recommended Reading and Additional Material 1

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Context Module Subheader

Extra reading (not compulsory): Paper on why bagging improves unstable estimators 7

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Attachment

AnalyzingBagging.pdf Talks about how you bootstrap aggregating "smooths" hard decisions/unstable estimators, AnalyzingBagging.pdf Talks about how you bootstrap aggregating "smooths" hard decisions/unstable estimators, 8

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Attachment

Lecture 4 - pdf Lecture 4 - pdf 9

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Attachment

Rcode to generate lecture figures Rcode to generate lecture figures 10

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Lecture 5: Regularized and Flexible Discriminant Analysis

Lecture 5: Regularized and Flexible Discriminant Analysis Lecture 5: Regularized and Flexible Discriminant Analysis

Page

Lecture 5 - Recommended Reading Lecture 5 - Recommended Reading 1

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Attachment

Lecture5-2023.pdf Lecture5-2023.pdf 2

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Attachment

Lecture5-2023.R Lecture5-2023.R 3

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Lecture 6: Classification and dimension reduction

Lecture 6: Classification and dimension reduction Lecture 6: Classification and dimension reduction

Page

Lecture 6 - Recommended Reading Lecture 6 - Recommended Reading 1

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Attachment

Addtional reading: Some more theory on matrix factorisation techniques (PCA, SVD, ...) Addtional reading: Some more theory on matrix factorisation techniques (PCA, SVD, ...) 2

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Attachment

Lecture6-2023.pdf Lecture6-2023.pdf 3

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Attachment

lecture06-23.R lecture06-23.R 4

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Attachment

Demo-PCA-SVD.html Demo-PCA-SVD.html 5

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Attachment

R notebook: Demo-PCA-SVD.ipynb R notebook: Demo-PCA-SVD.ipynb 6

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Lecture 7: Feature selection and regularised regression

Lecture 7: Feature selection and regularised regression Lecture 7: Feature selection and regularised regression

Page

Lecture 7 - Recommended Reading-2 Lecture 7 - Recommended Reading-2 1

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Attachment

Lecture7-2023.pdf Lecture7-2023.pdf 8

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lecture07-23.R lecture07-23.R 9

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Lecture 8: Regularised regression (cont'd)

Lecture 8: Regularised regression (cont'd) Lecture 8: Regularised regression (cont'd)

Page

Lecture 8 - Recommended Reading Lecture 8 - Recommended Reading 1

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Demo from class [html] Demo from class [html] 2

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Demo jupyter notebook Demo jupyter notebook 3

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Lecture8-2023.pdf Lecture8-2023.pdf 4

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lecture8-2023.R lecture8-2023.R 5

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Adaptive Lasso paper Adaptive Lasso paper 6

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Lecture 9: Clustering

Lecture 9: Clustering Lecture 9: Clustering

Complete one item

Page

Lecture 9 - Recommended and Additional Reading Lecture 9 - Recommended and Additional Reading 1

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Lecture9-2023.pdf Lecture9-2023.pdf 8

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Lecture 9 - R code for slides Lecture 9 - R code for slides 9

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Lecture 10: Clustering (cont'd)

Lecture 10: Clustering (cont'd) Lecture 10: Clustering (cont'd)

Page

Lecture 10 - Recommended Reading Lecture 10 - Recommended Reading 1

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Mclust article Mclust article 2

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Paper on variable selection in clustering Paper on variable selection in clustering 3

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Lecture10-2023.pdf Lecture10-2023.pdf 4

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R code for slides R code for slides 5

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Demo-Clustering.html Demo-Clustering.html 7

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Demo-Clustering.ipynb Demo-Clustering.ipynb 8

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Lecture 11: Density-based and high-dimensional clustering

Lecture 11: Density-based and high-dimensional clustering Lecture 11: Density-based and high-dimensional clustering

Complete one item

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Lecture11-2023.pdf Lecture11-2023.pdf 2

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R code for slides R code for slides 3

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[PDF] Article that introduced CLIQUE - Agrawal et al. (1998) [PDF] Article that introduced CLIQUE - Agrawal et al. (1998) 4

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[PDF] Article that introduced ProClus - Aggarwal et al. (1999) [PDF] Article that introduced ProClus - Aggarwal et al. (1999) 5

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Slides on consensus clustering Slides on consensus clustering 6

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Consensus cluster paper Consensus cluster paper 7

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TCGAdata.RData TCGAdata.RData 8

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Clustering Demo (1).html Clustering Demo (1).html 9

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Clustering Demo.ipynb Clustering Demo.ipynb 10

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Lecture 12: Data representations - Linear Methods

Lecture 12: Data representations - Linear Methods Lecture 12: Data representations - Linear Methods

Page

Lecture 12 - Recommended Reading Lecture 12 - Recommended Reading 1

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Lee and Seung (1999) - The original Nature article which shows some applications of NMF Lee and Seung (1999) - The original Nature article which shows some applications of NMF 2

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Lee and Seung (2000) - A more technical NeurIPS article describing multiplicative updates for NMF Lee and Seung (2000) - A more technical NeurIPS article describing multiplicative updates for NMF 3

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Lecture12-2023.pdf Lecture12-2023.pdf 4

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Code for slides Code for slides 5

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Lecture 13: Data representations - Preserving local geometry

Lecture 13: Data representations - Preserving local geometry Lecture 13: Data representations - Preserving local geometry

Page

Lecture 13 - Recommended Reading + Preliminaries Lecture 13 - Recommended Reading + Preliminaries 1

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2008 - Van Der Maaten and Hinton - Article that introduced tSNE 2008 - Van Der Maaten and Hinton - Article that introduced tSNE 2

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Lecture13-2023b.pdf Lecture13-2023b.pdf 3

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lecture13 code lecture13 code 4

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introLecture13-2023 (1).html introLecture13-2023 (1).html 7

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SVD, NMF and SOMS.html SVD, NMF and SOMS.html 9

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SVD, NMF and SOMS.ipynb SVD, NMF and SOMS.ipynb 10

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Lecture 14: Data representations - Kernel Methods

Lecture 14: Data representations - Kernel Methods Lecture 14: Data representations - Kernel Methods

Page

Lecture 14 - Recommended Reading + Preliminaries Lecture 14 - Recommended Reading + Preliminaries 1

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Lecture14-2023.pdf Lecture14-2023.pdf 2

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Code for slides Code for slides 3

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kPCAdemo.html kPCAdemo.html 4

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kRR demo.html kRR demo.html 5

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kPCAdemo.ipynb kPCAdemo.ipynb 6

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kRR demo.ipynb kRR demo.ipynb 7

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Lecture 15: Multiple testing and high-dimensional inference

Lecture 15: Multiple testing and high-dimensional inference Lecture 15: Multiple testing and high-dimensional inference

Page

Recommened reading and preliminaries Recommened reading and preliminaries 1

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Lecture15-2023.pdf Lecture15-2023.pdf 5

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R notebook R notebook 6

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html version html version 7

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Lecture 15-16: High-dimensional inference

Lecture 15-16: High-dimensional inference Lecture 15-16: High-dimensional inference

Page

Lecture 15-16: Recommened reading and preliminaries Lecture 15-16: Recommened reading and preliminaries 1

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Lecture16-2023.pdf Lecture16-2023.pdf 5

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HDI-23.html HDI-23.html 6

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HDI-23.ipynb HDI-23.ipynb 7

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Lecture 17: Large n methods and considerations

Lecture 17: Large n methods and considerations Lecture 17: Large n methods and considerations

Page

Recommended reading-2 Recommended reading-2 1

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Lecture17-2023.pdf Lecture17-2023.pdf 2

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BigN-2023.html BigN-2023.html 6

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BigN-2023.ipynb BigN-2023.ipynb 7

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kc_house_data.csv kc_house_data.csv 8

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Lecture 18: Additional topics

Lecture 18: Additional topics Lecture 18: Additional topics

Page

Recommended reading Recommended reading 1

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Lecture18-2023.pdf Lecture18-2023.pdf 4

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Review Lecture

Review Lecture Review Lecture

Page

Recommended course & Master thesis projects Recommended course & Master thesis projects 1

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ReviewLecture2023.pdf ReviewLecture2023.pdf 4

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Exam June 2023

Exam June 2023 Exam June 2023

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Exam 2023 Exam 2023 1

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Exam August 23

Exam August 23 Exam August 23

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Exam August 2023 Exam August 2023 1

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Course modules