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

project1R.ipynb project1R.ipynb 11

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Attachment

project1Py.ipynb project1Py.ipynb 12

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Attachment

mnist_large.zip mnist_large.zip 13

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Attachment

A-survey-on-addressing-high‑class-imbalance.pdf A-survey-on-addressing-high‑class-imbalance.pdf 14

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Context Module Subheader

Project 2 15

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Context Module Subheader

Project 3 23

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Additional Material

Additional Material Additional Material

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, dimension reduction, performance evaluation.

Week 1, Introduction: classification, dimension reduction, performance evaluation. Week 1, Introduction: classification, dimension reduction, performance evaluation.

Page

Lecture 1 - Recommended Reading Lecture 1 - Recommended Reading 1

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Page

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

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Attachment

Intro2026.pdf Intro2026.pdf 20

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Attachment

Lecture1-2026.pdf Lecture1-2026.pdf 21

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Context Module Subheader

Lecture 2 22

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Attachment

Lecture2a-2026.pdf Lecture2a-2026.pdf 23

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Attachment

Lecture2b-2026.pdf Lecture2b-2026.pdf 24

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Context Module Subheader

Demo 1 25

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Context Module Subheader

Updated Demo1 code for better visualization 26

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Attachment

Demo1-2026.ipynb Demo1-2026.ipynb 27

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Demo1-2026 (2).html Demo1-2026 (2).html 28

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Demo1-2026-Python.ipynb Demo1-2026-Python.ipynb 29

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Demo1-2026-Python (2).html Demo1-2026-Python (2).html 30

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Context Module Subheader

Lecture 3 31

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Attachment

Lecture2b-update.pdf Lecture2b-update.pdf 32

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Attachment

Lecture3-2026.pdf Lecture3-2026.pdf 33

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Context Module Subheader

Demo 2 35

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Attachment

DimensionReduction-2026.ipynb DimensionReduction-2026.ipynb 36

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Attachment

DimensionReduction-2026-Python (1).ipynb DimensionReduction-2026-Python (1).ipynb 40

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Week 2: Dimension reduction, filtering, Analysis pipeline

Week 2: Dimension reduction, filtering, Analysis pipeline Week 2: Dimension reduction, filtering, Analysis pipeline

Page

Recommended reading - dimension reduction Recommended reading - dimension reduction 6

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Attachment

SVMs.ipynb SVMs.ipynb 27

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Attachment

ConstrainedOptimization.pdf ConstrainedOptimization.pdf 30

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Attachment

Lecture4-2026.pdf Lecture4-2026.pdf 31

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Attachment

IntroLocalDimRedR.ipynb IntroLocalDimRedR.ipynb 32

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IntroLocalDimRedPy.ipynb IntroLocalDimRedPy.ipynb 33

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kPCAdemoR-2026.ipynb kPCAdemoR-2026.ipynb 34

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kPCAdemoPy-2026.ipynb kPCAdemoPy-2026.ipynb 35

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Page

TSNE-UMAP TSNE-UMAP 36

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tsnevsumap.ipynb tsnevsumap.ipynb 37

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Week 3: Trees, Boosting, SVMs

Week 3: Trees, Boosting, SVMs Week 3: Trees, Boosting, SVMs

Page

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

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Context Module Subheader

Extra reading (not compulsory): Paper on why bagging improves unstable estimators 2

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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, 3

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Attachment

SVMs.ipynb SVMs.ipynb 6

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NNfromscratch.ipynb NNfromscratch.ipynb 8

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Attachment

CARTandRandomForest.pdf CARTandRandomForest.pdf 16

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CARTBaggingRF.ipynb CARTBaggingRF.ipynb 17

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CARTBaggingRF_Python.ipynb CARTBaggingRF_Python.ipynb 18

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Page

Recommended reading - kernels etc Recommended reading - kernels etc 19

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KernelMethods.pdf KernelMethods.pdf 20

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Context Module Subheader

Optimal reading: paper on highdimensional regression 21

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SURPRISES IN HIGH-DIMENSIONAL RIDGELESS LEAST SQUARES INTERPOLATION SURPRISES IN HIGH-DIMENSIONAL RIDGELESS LEAST SQUARES INTERPOLATION 22

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Context Module Subheader

Demos 23

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kRR_demoPy.ipynb kRR_demoPy.ipynb 24

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kRR_demoR.ipynb kRR_demoR.ipynb 25

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KRRGDdemoPy.ipynb KRRGDdemoPy.ipynb 26

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KRRGDdemoR.ipynb KRRGDdemoR.ipynb 27

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RegressionGDstartingfrom0Python.ipynb RegressionGDstartingfrom0Python.ipynb 28

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RegressionGDstartingfrom0R.ipynb RegressionGDstartingfrom0R.ipynb 29

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Page

Recommended reading - Boosting, SVMs Recommended reading - Boosting, SVMs 30

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Boosting1.pdf Boosting1.pdf 31

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GBMdemoPy.ipynb GBMdemoPy.ipynb 32

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GBMdemoR.ipynb GBMdemoR.ipynb 33

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BoostingSVMandFLDA.pdf BoostingSVMandFLDA.pdf 34

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ConstrainedOptimization.pdf ConstrainedOptimization.pdf 35

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BoostingComparison_MNIST.ipynb BoostingComparison_MNIST.ipynb 36

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SVMs.ipynb SVMs.ipynb 37

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SVMs_Python.ipynb SVMs_Python.ipynb 38

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Week 4: NNs, Feature Selection

Week 4: NNs, Feature Selection Week 4: NNs, Feature Selection

Page

Recommended reading Neural networks Recommended reading Neural networks 2

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NN2026.pdf NN2026.pdf 10

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Context Module Subheader

Demos - for illustration - use other NN packages in your pipelines 11

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NNfromscratch.ipynb NNfromscratch.ipynb 12

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NNfromscratch_Python.ipynb NNfromscratch_Python.ipynb 13

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Week 5: Feature selection, continued

Week 5: Feature selection, continued Week 5: Feature selection, continued

Week 6: Conformal predictions, UQ

Week 6: Conformal predictions, UQ Week 6: Conformal predictions, UQ

Week 7: Project 2 - no lectures

Week 7: Project 2 - no lectures Week 7: Project 2 - no lectures

Week 8: Conformal predictions, local explanations and Clustering

Week 8: Conformal predictions, local explanations and Clustering Week 8: Conformal predictions, local explanations and Clustering

Week 9: More on clustering, Review

Week 9: More on clustering, Review Week 9: More on clustering, Review

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Course modules

Planning

Projects

Project 1 3

Project 2 15

Project 3 23

Additional Material

Week 1, Introduction: classification, dimension reduction, performance evaluation.

Lecture 2 22

Demo 1 25

Updated Demo1 code for better visualization 26

Lecture 3 31

Demo 2 35

Week 2: Dimension reduction, filtering, Analysis pipeline

Week 3: Trees, Boosting, SVMs

Extra reading (not compulsory): Paper on why bagging improves unstable estimators 2

Optimal reading: paper on highdimensional regression 21