Introduction to Machine Learning

In the course you will go through the following elements:

• Basic concepts (bias-variance dilemma, training-validation, overfitting-underfitting, classification-regression).
• Supervised learning with linear models.
• Nonlinear methods: neural networks.
• Evaluating model performance.
• Implementation practice on real-world data sets with PyTorch

With the introduction course in machine learning you will be able to:

• Formulate real-world data analysis tasks as machine problems.
• Use basic machine learning methods or their straightforward derivatives to solve these tasks.
• Evaluate, compare, and report performances of machine learning methods.
• Use existing programming technologies effectively to implement machine learning algorithms.

Any computer programmer with introductory knowledge on engineering calculus and applied linear algebra.

English.

Associate Professor Melih Kandemir studies a wide range of issues in machine learning.

Melih is fascinated by both popular and technical aspects of natural sciences, especially physics and biology. His leisure time goes to thinking wishfully about their applications to the development of environment-friendly technologies.

Melih is in love with his wife Fatos and his daughter Sehnaz.

The course is a workshop with a duration of 4 hours.

DKK 3.000,- (excl. VAT.)