December 2016

Projects

Projects for the month of December:

• Empirical model building - Github repository containing Jupyter notebooks illustrating various experimental design strategies
  • Website: https://charlesreid1.github.io/empirical-model-building
  • Repository: https://github.com/charlesreid1/empirical-model-building

• Rejoyce - Github repository containing Jupyter notebooks applying the Natural Language Toolkit to James Joyce's Ulysses
  • Website: https://charlesreid1.github.io/rejoyce
  • Repository: https://github.com/charlesreid1/rejoyce

• California Crime Stats - Github repository containing Jupyter notebooks applying various statistical tools to analyzing California crime data
  • Repository: https://github.com/charlesreid1/cali-crime
  • Kaggle Notebook: Cleaning Up the Crime Scene: Parsing the Data https://www.kaggle.com/csc142/d/fbi-us/california-crime/cleaning-up-the-crime-scene-parsing-the-data
  • Kaggle Notebook: California Crime Compendium: Comparing Campuses https://www.kaggle.com/csc142/d/fbi-us/california-crime/california-crime-compendium-comparing-campuses

• Abalone Baloney - Github repository containing Jupyter notebooks applying machine learning techniques to an abalone (sea snail) data set from the UCI machine learning repository
  • Repository: https://github.com/charlesreid1/abalone-baloney

References

UW Machine Learning Courses

ML Foundations: Case Study Approach https://www.coursera.org/learn/ml-foundations

ML Regression: https://www.coursera.org/learn/ml-regression

ML Classificaiton: https://www.coursera.org/learn/ml-classification

Predictive Analytics Models: https://www.coursera.org/learn/predictive-analytics

Scikit Learn

Examples

Scikit learn examples: http://scikit-learn.org/stable/auto_examples/index.html

Scikit learn map of algorithms: http://scikit-learn.org/dev/tutorial/machine_learning_map/index.html

Scikit learn SVR example: http://scikit-learn.sourceforge.net/0.6/auto_examples/svm/plot_svm_regression.html

Scikit learn more about SVR: http://scikit-learn.org/stable/modules/svm.html

Feature selection (picking out the high-variance variables): http://scikit-learn.org/stable/modules/feature_selection.html#l1-feature-selection

F regression: http://scikit-learn.org/stable/modules/generated/sklearn.feature_selection.f_regression.html

Gaussian Process Model regression: http://scikit-learn.org/stable/auto_examples/gaussian_process/plot_gpr_noisy_targets.html

Datasets

Abalone data set (UCI ML repo): http://archive.ics.uci.edu/ml/datasets/abalone

References

Tutorial on support vector regression: http://citeseerx.ist.psu.edu/viewdoc/download;jsessionid=C72B60C38C905614709A0C30D973E2CB?doi=10.1.1.114.4288&rep=rep1&type=pdf

Bioinformatics

Bioinformatics course from David Page: https://www.biostat.wisc.edu/bmi576/

David Page: http://pages.cs.wisc.edu/~dpage/

E. coli genome project: https://www.genome.wisc.edu/

Machine Learning

Tombone's computer vision blog: http://www.computervisionblog.com/

Extended mean field restricted boltzmann machine: https://github.com/charlesmartin14/emf-rbm

Books

Wiley Classics library: https://www.librarything.com/series/Wiley+Classics+Library