Course modules

Planning

Planning Planning

Page

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

Page

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

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

Project 1 - 2024.html Project 1 - 2024.html 8

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Page

Project 1 - papers and data Project 1 - papers and data 9

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Attachment

TCGAdata.txt TCGAdata.txt 10

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Attachment

TCGAlabels TCGAlabels 11

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Context Module Subheader

Project 2 13

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Attachment

Project2-2024.html Project2-2024.html 19

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Attachment

Project2-2024.ipynb Project2-2024.ipynb 20

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Attachment

CATSnDOGS.csv CATSnDOGS.csv 21

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Attachment

Labels.csv Labels.csv 22

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Page

Project2: discussion summary Project2: discussion summary 23

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Context Module Subheader

Project 3 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

Project3-2024.html Project3-2024.html 30

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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, Untroduction: classification and dimension reduction.

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

Page

Lecture 1 - Recommended Reading Lecture 1 - Recommended Reading 1

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Attachment

[PDF] Introduction to the course [PDF] Introduction to the course 5

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Attachment

[PDF] Lecture 1: Introduction, Basics of Statistical Learning [PDF] Lecture 1: Introduction, Basics of Statistical Learning 6

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Page

Lecture 2 - Recommended Reading Lecture 2 - Recommended Reading 7

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Attachment

[pdf] Lecture02a-2024.pdf [pdf] Lecture02a-2024.pdf 8

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Attachment

[pdf, read up to page 20] Lecture02b-2024.pdf [pdf, read up to page 20] Lecture02b-2024.pdf 9

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Attachment

[pdf, read up to page 24] Lecture02c-2024.pdf [pdf, read up to page 24] Lecture02c-2024.pdf 10

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Attachment

[notebook] Demo-1and2-2024.ipynb [notebook] Demo-1and2-2024.ipynb 11

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Attachment

[html] Demo-1and2-2024.html [html] Demo-1and2-2024.html 12

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Attachment

[Skip ROC curves and metrics for now] ClassificationBasics2024.ipynb [Skip ROC curves and metrics for now] ClassificationBasics2024.ipynb 13

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Attachment

ClassificationBasics2024.html ClassificationBasics2024.html 14

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Attachment

DimensionReduction-2024.ipynb DimensionReduction-2024.ipynb 15

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Attachment

DimensionReduction-2024.html DimensionReduction-2024.html 16

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Attachment

[data for Dim Red demmo] puppy.txt [data for Dim Red demmo] puppy.txt 17

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Week 2: Rule-based classification and regression

Week 2: Rule-based classification and regression Week 2: Rule-based classification and regression

Page

Questions and discussions from class Questions and discussions from class 1

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Page

Lecture 4 - Recommended Reading and Additional Material Lecture 4 - Recommended Reading and Additional Material 2

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Context Module Subheader

Extra reading (not compulsory): Paper on why bagging improves unstable estimators 8

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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, 9

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Attachment

Rcode to generate lecture figures Rcode to generate lecture figures 12

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Attachment

Lecture4-2024b.pdf Lecture4-2024b.pdf 13

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Attachment

random_forest_demo.ipynb random_forest_demo.ipynb 14

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Week 3: Kernel methods

Week 3: Kernel methods Week 3: Kernel methods

Page

Course update Course update 1

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Page

Recommended reading Recommended reading 2

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Attachment

Lecture5-2024.pdf Lecture5-2024.pdf 3

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Attachment

IntroLocalDimRed.ipynb IntroLocalDimRed.ipynb 4

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Attachment

IntroLocalDimRed.html IntroLocalDimRed.html 5

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Attachment

Lecture6-2024.pdf Lecture6-2024.pdf 6

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Attachment

kPCAdemo-2024.ipynb kPCAdemo-2024.ipynb 7

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Attachment

kPCAdemo-2024.html kPCAdemo-2024.html 8

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Attachment

kRR demo-2024.ipynb kRR demo-2024.ipynb 9

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Attachment

kRR demo-2024.html kRR demo-2024.html 10

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Week 4: Boosting and SVMs

Week 4: Boosting and SVMs Week 4: Boosting and SVMs

Page

Course update Course update 1

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Page

Recommended reading - updated Recommended reading - updated 2

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Attachment

LectureBoosting1-2024.pdf LectureBoosting1-2024.pdf 4

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Attachment

KRRGDdemo.ipynb KRRGDdemo.ipynb 5

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Attachment

KRRGDdemo (1).html KRRGDdemo (1).html 6

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GBMdemo.ipynb GBMdemo.ipynb 7

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GBMdemo (1).html GBMdemo (1).html 8

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LectureSVMFDA (2).pdf LectureSVMFDA (2).pdf 9

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SVMs.ipynb SVMs.ipynb 11

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SVMs.html SVMs.html 12

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LectureFeatureSelection.pdf LectureFeatureSelection.pdf 13

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slidecode.R slidecode.R 14

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Week 5: regularized regression

Week 5: regularized regression Week 5: regularized regression

Page

Recommended reading: Updated!! Recommended reading: Updated!! 1

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LectureRegReg2.pdf LectureRegReg2.pdf 2

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LassoAlassoDemo2.html LassoAlassoDemo2.html 3

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LassoAlassoDemo2.ipynb LassoAlassoDemo2.ipynb 4

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LectureSelectionStability.pdf LectureSelectionStability.pdf 5

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HDIdemo.ipynb HDIdemo.ipynb 6

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HDIdemo.html HDIdemo.html 7

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LectureMultipleTesting.pdf LectureMultipleTesting.pdf 8

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Demo-MultipleTesting.ipynb Demo-MultipleTesting.ipynb 9

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Demo-MultipleTesting.html Demo-MultipleTesting.html 10

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Week 6: Neural networks

Week 6: Neural networks Week 6: Neural networks

Page

Recommended reading Recommended reading 1

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LectureNN.pdf LectureNN.pdf 2

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NNfromscratch.ipynb NNfromscratch.ipynb 3

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NNfromscratch.html NNfromscratch.html 4

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Autoencoder and NN classification.ipynb Autoencoder and NN classification.ipynb 5

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Autoencoder and NN classification.html Autoencoder and NN classification.html 6

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Week 7: Clustering

Week 7: Clustering Week 7: Clustering

Page

Recommended reading - updated Recommended reading - updated 1

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LectureClustering1-2024.pdf LectureClustering1-2024.pdf 2

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Clustering Demo 2024.ipynb - corrected typos Clustering Demo 2024.ipynb - corrected typos 4

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Clustering Demo 2024.html Clustering Demo 2024.html 5

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Clustering Demo 2 2024.html Clustering Demo 2 2024.html 6

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LectureClustering2-2024.pdf LectureClustering2-2024.pdf 7

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Clustering Demo 2 2024.ipynb Clustering Demo 2 2024.ipynb 8

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Paper on modelbased clustering Paper on modelbased clustering 9

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Paper on variable selection for GMMs Paper on variable selection for GMMs 10

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LectureNMF-2024.pdf LectureNMF-2024.pdf 11

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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 12

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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 13

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DEMO - SVDNMF2024.ipynb DEMO - SVDNMF2024.ipynb 14

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SVDNMF2024.html SVDNMF2024.html 15

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Week 8: Big N, conformal predictions and local explanations

Week 8: Big N, conformal predictions and local explanations Week 8: Big N, conformal predictions and local explanations

Page

Comments on Week 8 lectures Comments on Week 8 lectures 1

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LectureBigN.pdf LectureBigN.pdf 2

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BigN-2024.ipynb BigN-2024.ipynb 3

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BigN-2024.html BigN-2024.html 4

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kc_house_data.csv kc_house_data.csv 5

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Leveraging.pdf Leveraging.pdf 6

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LectureAdditionalTopics.pdf LectureAdditionalTopics.pdf 7

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Conformal.ipynb - updated Conformal.ipynb - updated 9

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Conformal.html Conformal.html 10

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Ryan Tibshirani's tutorial on Conformation Predictions Ryan Tibshirani's tutorial on Conformation Predictions 11

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LIMEpaper.pdf LIMEpaper.pdf 12

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Review2024.pdf Review2024.pdf 13

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Exam June 2024

Exam June 2024 Exam June 2024

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Exam: June 2024 Exam: June 2024 1

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Exam-June2024 notebook Exam-June2024 notebook 2

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Fish3.txt Fish3.txt 3

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MyMNIST.csv MyMNIST.csv 4

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fish2.jpg fish2.jpg 5

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bream2.jpg bream2.jpg 6

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