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ml-katas

Travis (.org)

Katas for Machine Learning.

Template for new kata

Pick a topic, create an exercise using the following suggested template (subject to change):

  1. what will we get to learn at the end of this exercise?
  2. how is your environment going to look like
  • datasets you are working with
  • tools, languages etc since people might want to try different things here, this information can go in the solution readmes
  1. reference documents, papers, baselines etc.

Work on the solution and keep pushing in this repo. The directory structure can look like this:

README.md  # this readme
<some-problem>
  README.md  # problem specification
  <solution-dir-1>  # someone's solution
  <solution-dir-2>  # someone else's solution

Topics list

Here is an ever increasing dump of randomly selected topics for inspiration.

  • optimization
    • convex, non convex
    • gradient based/free
    • population based and other random
  • search
  • game theory
  • constraint satisfaction, reasoning and planning
  • propositional and first order logic
  • programming
    • GPU programming, cuda and opengl
    • parallel computing, mpi etc.
    • working with memory effectively in large dataset situation
    • model quantization and other runtime optimizations
    • distributed learning
    • computer algebra systems (symbolics)
    • auto diff
  • tidy data and data wrangling
  • metrics
    • loss functions
    • surrogate loss and calibration
  • graphical models
    • CRF, HMMs etc.
    • variational inference
  • learning theory
    • complexity and runtime of learning algorithms
    • generalization
    • regularization
  • clustering
  • online learning
  • linear algebra
  • recommendation systems
  • neural nets
    • convolutional
    • recurrencies
    • energy based models
    • misc architectures
    • non differentiables
    • architecture search
  • MCMC and other sampling based inference techniques
  • randomized algorithms
  • generalized linear models
  • kernel methods
  • hypothesis testing
  • visualization and interpretation
  • semi supervised learning
  • active learning
  • ensembles, boosting etc.
  • knowledge representation
    • relational
    • gazetteers
  • GOFAI, expert systems etc.
  • parsing
  • reinforcement
    • MDPs, POMDPs

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