• What is my background
• How I got into ML
• Overview of ways I've used ML
• What I wish I had known earlier

Background

• High school "computing" and maths
• Bioscience Undergraduate: insigificant courses and research project
• Finishing Bioinformatics PhD looking
• So no significant formal training in ML, maths or computer science

MOOCs!

• Philip Klein's (Brown) "Coding the Matrix" *
• Andrew Ng's (Stanford) "Machine Learning" *
• Yaser Abu-Mostafa's (Caltech) "Learning from Data"
• Sriram Sankaranarayanan & Shalom D. Ruben's (UCBoulder) "Linear and Integer Programming"
• Daphne Koller's (Standford) "Probabilistic Graphical Models"

Textbooks

• Christopher Bishop's "Pattern Recognition and Machine Learning"
• Kevin Murphy's "Machine Learning: A Probabilistic Perspective"
• Gilbert Strang's "Linear Algebra and Its Applications"

Practice

• Provided Dataset
• Evaluation Metric
• Public-Private scoreboards
• Top N winners methods and code are released publically (on Kaggle's No Free Hunch Blog)

Warning

Other media

• Zygmunt Zajac's FastML Blog
• Andrej Karpathy's Blog (DNNs)
• Neural Information Processing Systems workshops
• Ryan Adams' and Katherine Gorman's Talking Machines Podcast
• Scikit-Learn Documentation
• Cross Validated - StackOverflow Q&A System for ML and stats

• 400M 150bp PE reads (159GB)

• K-means clustering
• Fit GMM using Expectation-Maximisation
• Python (SKLearn) still hadn't finished PARSING input after 48 hours
• C++ (MLPACK/ARMADILLO): 12 hours (6GB of memory) single threaded
• Lesson: use the right tool for the job (Python: Scikit-Learn, R: Caret, Java: Weka, C++: MLPACK etc)

Data processing and feature extraction

• Data preprocessing: downsampling, cleaning
• Channel correlations: independent component analysis, common spatial patterns, MVARs
• Approximately 850 different features
• Recursive Feature Elimination

Machine learning

• Random forests
• Support Vector Machines
• Logistic Regression
• Adaboost
• Ensembles!

Performance

• Top 5% (16/504)
• Reasonable accurate prediction - 0.77014 AUC
• Team: Gavin Gray and Scott Lowe
• Lesson: planning and ensembles

• Metatranscriptome origin classification
• Goal: classify transcripts into origin species

• Features: Phylogenetic tree and sequence features (GC, trinucleotide)
• SVM/RVM
• F1 Score nearly as good as manual (ad hoc self-consistency...)

• Diabetic retinopathy detection
• Two eyes, 5 ratings for each eye
• Same ML approach as NeuKrill-Net
• Difficult cost function (quadratic negative kappa)

• de novo assembly parameter optimisation (k-mer size, minimum coverage, normalisation)
• Bayesian optimisation of assembly likelihood
• Spearmint experimentation evaluated using RSEM-eval
• Key challenges i.e. representative subset, generalising for assemblers

• Short-form spoken word recommender system
• Goal: Recommender system for audioclips
• Hare brained startup
• Audiotranscription and metadata (project Gutenberg)
• Very much a work in progress
• RNN and LSTM are really cool

Plot everything

• Exploratory data analysis
• Plot density, scatter, structure of data

No Free Lunch

• "any two [...] algorithms are equivalent when their performance is averaged across all possible problems"
• No universally best optimiser, classifier, or metric
• Wolpert and McCreedy proofs
• Real world not so nicely random

ML = Method + Evaluation + Optimisation

• The ML algorithm (e.g. KNN, SVM, RF etc) is only part of the problem
• Important to be aware of evaluation (e.g. sum of squares distance, error)
• and optimisation method (e.g. gradient descent, expectation-maximisation)
• Can make a big difference to performance (and run time)
• Ties into NFL theory

Curse of dimensionality

• Intuitions will fail in high dimensions

Highly iterative process

• Most of the work is the boring stuff
• Data exploration
• Data gathering
• Data cleaning
• More data can beat smarter algorithms (but not always)

• Worth taking time setting up tools and data
• UNIT TESTS
• Literate programming e.g. Rmd, Jupyter
• Version control e.g. git

If in doubt combine models!

• Bagging (average estimators trained on random subsets of data)
• Merging (model averaging)
• Stacking (feed estimators into other estimators)
• Boosting (iteratively train estimators on data that previous models misclassify)

• Machine learning can be used to do cool things
• It is not as opaque as it appears
• Optimisation and Evaluation is as important as ML algorithm
• Highly iterative process
• Use version control and literate programming to save yourself a lot of difficulty