Week 1 Flashcards
Wat zijn de 5 onderdelen van een leerprobleem?
1) Input x in X.
2) Output y in Y.
3) Doelfunctie f: X -> Y.
4) De dataset met (x1, y1)…
5) De geleerde hypothese g: X -> Y.
Wanneer is ML toepasbaar op een probleem? Geef 3 voorwaarden.
1) Er is een patroon.
2) Het lukt niet om dit patroon te beschrijven door het probleem te analyseren.
3) Er zijn gegevens waar we uit kunnen leren.
What is H in ML?
The hypothesis set of candidate formulas that are under consideration.
What is the role of h(x)?
A functional form that assigns the weights to the different components of the input vector.
When is a dataset linearly separable?
There is a choice for the parameters that classifies all the training examples correctly.
PLA
perception learning algorithm
What is the goal of the perceptron learning algorithm?
Finding a hypothesis that classifies all the data points in data set D correctly.
Supervised learning setting
When the training data contains explicit examples of what the correct output should be for given inputs.
Waar bestaat de hypotheseruimte uit bij k-nearest neighbors?
Bijna alle functies van inputs naar outputs.
Active learning
The data set is acquired by the learner through asking for a label for specific entries.
What is the standard formula for h(x)?
h(x) = sign(w.T * x)
Online learning
The data set is given to the algorithm one example at a time. Learning takes place as data becomes available.
Transfer learning
When training an algorithm on data results in a model, and that model is used on a new problem or task. It uses the info learned on the first problem to improve on the second one.
What is the update formula for w?
w(t+1) = w(t) + y(t)*x(t)
Reinforcement learning
The training example does not contain the target output, but contains some possible output together with a measure of how good that output is.
in-sample error
E.in(h): The error rate within a sample: the fraction of the data set where h and f disagree.
Example: the mistakes on a practice test.