After completing the course the student shall be able to provide an overview of the different areas within artificial intelligence, explain fundamental principles and applications of machine learning, explain the benefits and drawbacks of different machine learning techniques/algorithms. You will also be able to explain different learning paradigms in machine learning, implement algorithms to solve typical machine learning tasks, represent data to facilitate machine learning,, select an appropriate model for a task and evaluate its performance, recognize the effects of bad initialization and parameter selection as well as over and under fitting, and reason about the effects that, e.g., bias in the training data can have on actual applications.

The course gives an overview of fundamental concepts and techniques within machine learning.
The following topics are covered:
-  An overview of artificial intelligence and machine learning.
-  Fundamental principles for machine learning.
-  Data preprocessing, feature extraction, and dimensionality reduction.
-  Model selection, generalization, and overfitting.
-  Optimization of training models.
-  Regression.
-  Nearest neighbor classifiers.
-  Logistic regression.
-  Naive Bayes.
-  Decision trees.
-  Artificial neural networks.
-  Ensemble methods.
-  Kernel methods and Support vector machines.
-  k-­means and hierarchical clustering.

The course will be given during the period: 20 Jan  - 22 Mar, 2020
Please sign up no later than 10/1 2021

For more information see the course plan: https://kursplan.lnu.se/kursplaner/syllabus-4DV657-1.pdf 

If you have any questions please turn to Welf Löwe, welf.lowe@lnu.se