Machine Learning/AI track

Plans and notes for the ML/AI group

AI & ML 101 – Jan 25

Intro to the workshop series

  • My expectation: Explaining and discussing algorithms, discussing political and societal implications, ideally have a software project.
  • I’d like a collaborative approach, I hope some members have some ML background
  • Ideally everybody can understand everything
  • do we want to have a project? If so, what?

The workhorse of Machine Learning (linear regression)

  • Explain how to make predictions from data by fitting a line through data points
  • Ideally, program a simple algorithm to do that (linear regression)
    • Set up Python, PyCharm and a virtualenv for everybody who’s interested in programming
    • slope of a line fitted through a list of points x and y is described by:
      B1 = sum((x(i) - mean(x)) * (y(i) - mean(y))) / sum( (x(i) - mean(x))^2 )
    • and the y value at x = 0 by:
      B0 = mean(y) - B1 * mean(x)
  • idea of the cost function cost function and that we want to minimize it
  • The “magic” of ML derives from using many, many data and dimensions
  • how is this aspect of ML (regression/extrapolation) relevant for us? How can we game it?
  • maybe: tavsiye as an example for a simple recommender system (but probably no time for that)

Learning recommendations:

Multidimensional data (Feb 22)

Forgot the reading list last time, sorry, here again:

Generic description of the process used in “all” ML algorithms

  • Flow chart:

In one-dimensional case (straight line that best fits data points) f equals the set of parameters that fully describe a line through the data: slope and intercept.

  • Quality metric = “cost function” = error = sum of all individual deviations of actual data from prediction

Again for one-dimensional case

  • Error for a single data point:
  • Cumulative error:

  • ML algorithm: steps to minimize that error
  • WWYD?
  • This is how the error looks like:

  • Finding the minimum means finding the point where derivatives wrt w0 and w1 are zero
  • Analytically this is possible in one-dimensional case (hence the algorithm I gave you last week): setting the derivatives wrt w0 and w1 to 0 and solving for w0 and w1
  • Another possibility without doing actual calculus: Finding minimum by following the slope (aka derivative) downhill until there is no further downhill.

(here: maximum instead of minimum)

Gradient descent

The gradient always points along the steepest slope of a function at that point, analogous to the derivative which just points along the slope.

  • in higher dimensions it is (in general) not feasible to do it analytically due to high complexity, but gradient descent always works (for Machine Learning stuff).

Higher dimensions? WTF?

  • Housing prices dataset: there is more to a house than its size!
    • What, for example?
    • # of rooms, floor, age, …
    • even bools like “has garage”, “is on lake front” etc.
    • we also can add artificial features generated from the present ones by plugging them into a mathematical function (x^2, log(x), sin(x) etc.) to generate nonlinear models!

Gradient descent with multiple features

Neural networks (April 27)

  • Number recognition (MNIST)
    • in nn_wtf or keras

Further reading:

ML is not neutral

  • Algorithms are neutral, the data they are fed aren’t
  • Whoever owns the data has the advantage
  • Hence the data hunger of modern IT companies: Intense economical pressure to collect as many data as possible

ML as an oppression tool

  • police prediction software (e.g.
  • facial recognition software as surveillance tool, FRS biased against people of color
  • this (long) article about enshrining human biases in algorithms:

ML as a (possibly) unintentional oppression tool

  • news feed curators (Facebook/US elections), search results (google autocomplete, see e.g. ) and, in general, hacking of these algorithms by outsiders
  • hiring software

ML as a liberation tool

  • Bias in Machine Learning link list:

Evolutionary/genetic algorithms

This is maybe too technical so early. Should have a Neural Networks intro first. Or move it into NN module.

Natural Language Processing (if we find somebody with enough expertise to hold a workshop)

  • Bayes filtering as an example everybody knows

Recommender systems?

  • tavsiye as an example for a simple recommender system

Unsupervised learning/clustering

Dumping ground (stuff that has no home)

Also (possibly) on the menu

  • brief introduction to scikit-learn and pandas
    • virtualenvs?
  • overfitting & underfitting
  • regularization
  • finding interesting parameters (lasso regularization)
  • Training/validation/test data

Possible topics:

  • Current status quo
  • Where does all this lead?
    • Social implications of technical development
    • Commodification of ML (“everybody” can do ML stuff)
  • What are you planning? Projects, ideas?
  • Practical examples, other Languages, ML as a service

Link list

Drop your links here!

Deep Learning Frameworks

Tricking ML

Data sets

  • Twitter Sentiment Analysis Training Corpus


  • (deep learning and image recognition blog)
  • TensorKart: self-driving MarioKart with TensorFlow