Machine learning for data science 1

There are no prerequisites.

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.

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.

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.

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.

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

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