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lecture notes Machine Learning
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  • Course
  • Machine Learning (880083M6)
  • Institution
  • Tilburg University (UVT)

The document contains lecture notes for the course Machine Learning given by Grzegorz Chrupala at Tilburg University. It contains notes from the year '23/'24 and one lecture from the year '24/'25 is added. It contains the theoretical aspects of the course.

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Document information

  • December 9, 2024
  • 13
  • 2024/2025
  • Class notes
  • Grzegorz chrupala
  • All classes

Subjects

  • decision trees
  • perceptron
  • random forest
  • gradient descent
  • logistic regression
  • representations
  • feature engineering
  • artificial neural networks

Written for

  • Tilburg University (UVT)
  • MSc Data Science & Society
  • Machine Learning (880083M6)
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Machine Learning ‘23/’24 Michiel de Folter



Machine Learning

Lecture 1: Introduction
Applications of Machine Learning:
- Regression
- Binary classification
- Multilabel classification
- Ranking
- Sequence to sequence prediction
- Many to one prediction
- One to many prediction
- Many to many prediction

Training, Validation & Test set: subsets of the data
- Training set: Used for inferring rules and learning by an update rule.
- Validation set: Used to choose the best learning parameters for the model to use.
- Test set: Used to generalize the model to real-world appliances.

Stratification:
A way of making sure that the sample is representative of the population by the standards of
your choosing. (Be careful about manually selecting the population for selection biases)

Regression evaluation metrics:

Mean Absolute Error (MAE):
N
1
MAE= ∗∑ ¿ y n−^
y n∨¿ ¿
N n=1
Mean Squared Error (MSE):
N
1
∗∑ ( y n−^
2
MSE= y n)
N n=1
R-squared:
2 MSE
R =1−
Variance of the True values




Classification evaluation metrics:

, Machine Learning ‘23/’24 Michiel de Folter




TRUE

Positive Negative


Positive TP FP Recall

PREDICTED
Negative FN TN Negative
prediction
power

Sensitivity Specificity Accuracy




Lecture 2: Decision trees

Decision tree: A repetition of ‘if-else’ statements called decision rules.

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