Class notes

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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Uploaded on December 9, 2024
Number of pages 13
Written in 2024/2025
Type Class notes
Professor(s) Grzegorz chrupala
Contains All classes

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

Institution
Tilburg University (UVT)
Education
MSc Data Science & Society
Course
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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