All programmes > Machine Learning and Inductive Inference

Machine Learning and Inductive Inference (B-KUL-H02C1A)

4 ECTSEnglish35 First termCannot be taken as part of an examination contract
Blockeel Hendrik
POC Artificial Intelligence

Aims

This course will familiarise the students with the domain of machine learning, which concerns techniques to build software that can learn how to perform a certain task (or improve its performance on it) by studying examples of how it has been accomplished previously, and in a broader sense the discovery of knowledge from observations (inductive inference).
After following this course, students will:

  • have a basic understanding of the general principles of learning
  • have an overview of the existing techniques for machine learning and data mining
  • understand how these techniques work, and why they work
  • be able to implement programs that learn or exhibit adaptive behavior, using these techniques
  • be up-to-date with the current state of the art in machine learning research
  • be able to contribute to contemporary machine learning research

Previous knowledge

Students should be familiar with:

  • algorithms and programming
  • some elements from higher mathematics, probability theory and statistics
  • predicate logic

 

 

Introductory courses on these topics (at the Bachelor level) suffice.

 

Order of Enrolment


This course unit is a prerequisite for taking the following course units:
I0U20A : Integrated Bioinformatics Project
H00Y4A : Big Data Analytics Programming
H0O23A : Reinforcement Learning

Is included in these courses of study

Activities

3 ects. Machine Learning and Inductive Inference: Lecture (B-KUL-H02C1a)

3 ECTSEnglishFormat: Lecture20 First term
Blockeel Hendrik
POC Artificial Intelligence

Content

1. introduction to machine learning, connections with other subjects
2. general principles of learning:
- concept learning, version spaces
- evaluation of learning algorithms
- theory of learnability
- representation of inputs and outputs of learning algorithms
3. specific learning approaches:
- decision trees
- rules, association rules
- instance based learning
- clustering
- neural networks
- support vector machines
- Bayesian learning
- genetic algorithms
- ensemble methods (bagging, boosting, ...)
- reinforcement learning
- inductive logic programming
 

Course material

Course Text
Lecture slides

Format: more information

Ten lectures of 2 hours each.
 

1 ects. Machine Learning and Inductive Inference: Exercises (B-KUL-H00G6a)

1 ECTSEnglishFormat: Practical15 First term
Blockeel Hendrik
POC Artificial Intelligence

Content

Exercises are made on the subjects discussed during the lectures. These are mostly pen-and-paper exercises where students gain insight in the workings of learning algorithms by manually mimicking the computations of certain learning algorithms, graphically describing the result of a learning algorithm (by drawing decision surfaces), etc. There are also exercises on evaluation of machine learning models and algorithms.

Course material

  • A list of exercises.
  • Solutions are made available on Toledo.

Format: more information

Students try to independently solve the exercises during some time. A teaching assistant provides help where necessary, and discusses the solution afterwards.

Evaluation

Evaluation: Machine Learning and Inductive Inference (B-KUL-H22C1a)

Type : Exam during the examination period
Description of evaluation : Written
Type of questions : Multiple choice, Open questions, Closed questions
Learning material : Calculator, List of formulas

Explanation


The exam consists of questions about the theory and exercises. A formula sheet can be consulted during the exam. 

If the evaluation shows that the student does not meet one or more objectives of the course, the global result may differ from a weighted average of the parts.