Machine learning for data science 1

2022/2023
Programme:
Computer Science and Mathematics, Second Cycle
Year:
1 ali 2 year
Semester:
first or second
Kind:
optional
ECTS:
6
Language:
slovenian, english
Lecturers:

Blaž Zupan

Hours per week – 1. or 2. semester:
Lectures
3
Seminar
0
Tutorial
2
Lab
0
Content (Syllabus outline)

Linear models. Linear regression.
Linear discriminant analysis. Logistic
regression. Gradient descent.
Stochastic gradient descent.

The machine learning approach.
Cost functions. Empirical risk
minimization. Maximum likelihood
estimation. Model evaluation. Crossvalidation.

Feature selection. Search-based
feature selection. Regularization.

Tree-based models. Decision trees.
Random forest. Bagging. Gradient
tree boosting.

Clustering. k-means. Expectation
Maximization.

Non-linear regression. Basis
functions. Splines. Support vector
machines. Kernel trick.

Neural networks. Perceptron.
Activation functions.
Backpropagation.

Readings

James G, Witten D, Hastie T, Tibshirani T (2017) An Introduction to Statistical
Learning, Springer.

Hastie T, Tibshirani R, Friedman J (2003) The elements of statistical learning, Springer.

Objectives and competences

The course aims at familiarizing the
student with the fundamentals of machine
learning, classical machine learning models,
and the practicalities of applying machine
learning to real-world problems. The
course prepares students for the study of
advanced machine learning methods.

Intended learning outcomes

After successfully completing the course,
students should be able to:

  • Apply the machine learning
    approach to data analysis.

  • Evaluate different types of models.

  • Choose the correct model for the
    problem at hand.

  • Interpret machine learning results.

  • Identify potential issues.
Learning and teaching methods

Lectures, , homework, and a set of smaller
projects.

Assessment

Continuing (homework, projects)
Final (written exam)
Grading: 6-10 pass, 5 fail