Course modules

Planning

Planning Planning

Page

Plan for the course Plan for the course 1

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Projects

Projects Projects

Page

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

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Context Module Subheader

Project 1 3

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Attachment

TCGAdata.txt TCGAdata.txt 10

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Attachment

TCGAlabels TCGAlabels 11

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Attachment

Project1-2025.html Project1-2025.html 13

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Attachment

Project1-2025.ipynb Project1-2025.ipynb 14

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Attachment

Data set - Numbers.txt Data set - Numbers.txt 15

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Attachment

The outer/inner CV loop discussion from class The outer/inner CV loop discussion from class 16

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Context Module Subheader

Project 2 17

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Attachment

Project2-2025.html Project2-2025.html 29

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Attachment

Project2-2025.ipynb Project2-2025.ipynb 30

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Attachment

catdogdata.txt catdogdata.txt 31

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Context Module Subheader

Project 3 32

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Attachment

Project3-2025.html Project3-2025.html 38

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Additional Material

Additional Material Additional Material

Page

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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Week 1, Introduction: classification and dimension reduction.

Week 1, Introduction: classification and dimension reduction. Week 1, Introduction: classification and dimension reduction.

Page

Lecture 1 - Recommended Reading Lecture 1 - Recommended Reading 1

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Attachment

[data for Dim Red demmo] puppy.txt [data for Dim Red demmo] puppy.txt 16

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Attachment

IntroToCourse-2025.pdf IntroToCourse-2025.pdf 17

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Lecture1-2025.pdf Lecture1-2025.pdf 18

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Page

Lecture 2 - Recommended Reading Lecture 2 - Recommended Reading 19

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Lecture2a-2025.pdf Lecture2a-2025.pdf 20

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Attachment

Lecture2b-2025.pdf Lecture2b-2025.pdf 21

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Attachment

Demo-1and2-2025.ipynb Demo-1and2-2025.ipynb 22

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Attachment

Demo-1and2-2025.html Demo-1and2-2025.html 23

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Page

Lecture 2&3 - Recommended Reading Lecture 2&3 - Recommended Reading 24

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Lecture3-2025.pdf Lecture3-2025.pdf 25

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Attachment

DimensionReduction-2025.html DimensionReduction-2025.html 26

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Attachment

DimensionReduction-2025.ipynb DimensionReduction-2025.ipynb 27

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puppy.txt puppy.txt 28

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Week 2: Tree-based methods, SVM and NN

Week 2: Tree-based methods, SVM and NN Week 2: Tree-based methods, SVM and NN

Page

Recommended Reading - Tree-based methods Recommended Reading - Tree-based methods 1

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Attachment

Regression and Classification trees + ensembles Regression and Classification trees + ensembles 2

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Context Module Subheader

Extra reading (not compulsory): Paper on why bagging improves unstable estimators 3

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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, 4

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Page

Recommended reading - dimension reduction Recommended reading - dimension reduction 5

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Preserving local geometry - flexible dimension reduction Preserving local geometry - flexible dimension reduction 6

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IntroLocalDimRed.ipynb IntroLocalDimRed.ipynb 7

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IntroLocalDimRed.html IntroLocalDimRed.html 8

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kPCAdemo-2025.ipynb kPCAdemo-2025.ipynb 9

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kPCAdemo-2025.html kPCAdemo-2025.html 10

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kRR demo-2025.ipynb kRR demo-2025.ipynb 11

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kRR demo-2025.html kRR demo-2025.html 12

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Page

Recommended reading - Boosting, SVMs Recommended reading - Boosting, SVMs 13

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Kernel methods Kernel methods 14

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KRRGDdemo.ipynb KRRGDdemo.ipynb 15

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KRRGDdemo (1).html KRRGDdemo (1).html 16

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RegressionGDstartingfrom0.ipynb RegressionGDstartingfrom0.ipynb 17

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RegressionGDstartingfrom0.html RegressionGDstartingfrom0.html 18

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Gradient descent, forward fitting, a first look at Boosting Gradient descent, forward fitting, a first look at Boosting 20

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GBMdemo.ipynb GBMdemo.ipynb 21

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GBMdemo (1).html GBMdemo (1).html 22

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SURPRISES IN HIGH-DIMENSIONAL RIDGELESS LEAST SQUARES INTERPOLATION SURPRISES IN HIGH-DIMENSIONAL RIDGELESS LEAST SQUARES INTERPOLATION 24

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Boosting, SVM and a first look at Neural Networks Boosting, SVM and a first look at Neural Networks 25

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SVMs.html SVMs.html 26

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SVMs.ipynb SVMs.ipynb 27

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Context Module Subheader

Handwritten notes from class 28

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FromFGDtoGradientBoosting.pdf FromFGDtoGradientBoosting.pdf 29

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ConstrainedOptimization.pdf ConstrainedOptimization.pdf 30

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Week 3: Stochastic training, NN ctd, feature selection

Week 3: Stochastic training, NN ctd, feature selection Week 3: Stochastic training, NN ctd, feature selection

Page

Recommended reading Recommended reading 1

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Neural networks Neural networks 2

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SVMs.ipynb SVMs.ipynb 3

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SVMs (1).html SVMs (1).html 4

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NNfromscratch.ipynb NNfromscratch.ipynb 5

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NNfromscratch.html NNfromscratch.html 6

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Autoencoder and NN classification.ipynb Autoencoder and NN classification.ipynb 7

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Autoencoder and NN classification.html Autoencoder and NN classification.html 8

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Page

Recommended reading - regularized regression Recommended reading - regularized regression 9

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RegularizedRegression.pdf RegularizedRegression.pdf 10

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LassoAlassoDemo.ipynb LassoAlassoDemo.ipynb 11

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LassoAlassoDemo html LassoAlassoDemo html 12

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Week 4: Feature selection, dimension reduction and analysis pipeline

Week 4: Feature selection, dimension reduction and analysis pipeline Week 4: Feature selection, dimension reduction and analysis pipeline

Page

Recommended reading Recommended reading 1

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RegularizedRegressionCtd.pdf RegularizedRegressionCtd.pdf 2

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HighDimensionalInference.pdf HighDimensionalInference.pdf 3

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HDI-2025.ipynb HDI-2025.ipynb 4

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HDI-2025.html HDI-2025.html 5

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MultipleTesting.pdf MultipleTesting.pdf 6

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Demo-MultipleTesting.ipynb Demo-MultipleTesting.ipynb 7

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Demo-MultipleTesting.html Demo-MultipleTesting.html 8

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Week 5: Feature selection

Week 5: Feature selection Week 5: Feature selection

Attachment

AnalysisPipeline.pdf AnalysisPipeline.pdf 1

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Week 6: Clustering

Week 6: Clustering Week 6: Clustering

Page

Recommended reading Recommended reading 1

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Clustering-part1.pdf Clustering-part1.pdf 3

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Clustering-part2.pdf Clustering-part2.pdf 4

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Clustering Demo 2025.ipynb Clustering Demo 2025.ipynb 5

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Clustering Demo 2025.html Clustering Demo 2025.html 6

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TCGAdata.RData TCGAdata.RData 7

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TCGAdata.txt TCGAdata.txt 8

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TCGAlabels TCGAlabels 9

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TheEMAlgorithm TheEMAlgorithm 10

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EM-illustration EM-illustration 11

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Context Module Subheader

Optional Reading 12

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ModelbasedClusteringPaper ModelbasedClusteringPaper 13

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VariableSelectionModelbasedClustering VariableSelectionModelbasedClustering 14

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Week 7: Clustering. BigN

Week 7: Clustering. BigN Week 7: Clustering. BigN

Page

Recommended reading Recommended reading 1

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Attachment

NMF.pdf NMF.pdf 2

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DBclustering.pdf DBclustering.pdf 3

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Demo-Clustering-1.html Demo-Clustering-1.html 4

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Demo-Clustering-1.ipynb Demo-Clustering-1.ipynb 5

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Attachment

Consensus cluster paper Consensus cluster paper 6

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Attachment

NMF-2025-1.ipynb NMF-2025-1.ipynb 7

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NMF-2025-1.html NMF-2025-1.html 8

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Page

Recommended reading-2 Recommended reading-2 9

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BigN.pdf BigN.pdf 10

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BigN-2025.html BigN-2025.html 11

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BigN-2025.ipynb BigN-2025.ipynb 12

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kc_house_data.csv kc_house_data.csv 13

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Context Module Subheader

Optional on subspace clustering 14

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Attachment

CLIQUE - Agrawal et al. (1998).pdf CLIQUE - Agrawal et al. (1998).pdf 15

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ProClus - Aggarwal et al. (1999).pdf ProClus - Aggarwal et al. (1999).pdf 16

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Week 8: Analysis pipeline, Missing values, Uncertainty quantification

Week 8: Analysis pipeline, Missing values, Uncertainty quantification Week 8: Analysis pipeline, Missing values, Uncertainty quantification

Page

Recommended reading Recommended reading 1

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UQandMore.pdf UQandMore.pdf 2

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Conformal25.ipynb Conformal25.ipynb 3

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Conformal25.html Conformal25.html 4

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Counterfactuals.ipynb Counterfactuals.ipynb 5

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Counterfactuals.html Counterfactuals.html 6

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DetectingMislabeledObservations.ipynb DetectingMislabeledObservations.ipynb 7

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DetectingMislabeledObservations.html DetectingMislabeledObservations.html 8

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conformal.pdf conformal.pdf 10

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LIMEpaper.pdf LIMEpaper.pdf 11

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counterfactuals review counterfactuals review 12

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Week 9: Review

Week 9: Review Week 9: Review

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Reviewlecture Reviewlecture 1

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Exam-June2023-Expectations.html Exam-June2023-Expectations.html 2

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Context Module Subheader

Exam June 2025 3

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Exam-June2025 Exam-June2025 4

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Cancer2025exam.csv Cancer2025exam.csv 5

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Course modules

Planning

Projects

Project 1 3

Project 2 17

Project 3 32

Additional Material

Week 1, Introduction: classification and dimension reduction.

Week 2: Tree-based methods, SVM and NN

Extra reading (not compulsory): Paper on why bagging improves unstable estimators 3

Handwritten notes from class 28