General information:
Code:
UBPJO-096
Name:
Knowledge discovery
Profile of education:
Academic (A)
Lecture language:
English
Semester:
Spring
Responsible teacher:
Śnieżyński Bartłomiej (Bartlomiej.Sniezynski@agh.edu.pl)
Academic teachers:
Śnieżyński Bartłomiej (Bartlomiej.Sniezynski@agh.edu.pl)
Module summary

Description of learning outcomes for module
MLO code Student after module completion has the knowledge/ knows how to/is able to Connections with FLO Method of learning outcomes verification (form of completion)
Skills
M_U001 Is able to use knowledge discovery software. Completion of laboratory classes
M_U002 Is able to choose a machine learning technique to a given practical problem. Completion of laboratory classes
Knowledge
M_W001 Knows and understands basic ideas of machine learning Examination
M_W002 Knows popular machine learning algorithms Examination
FLO matrix in relation to forms of classes
MLO code Student after module completion has the knowledge/ knows how to/is able to Form of classes
Lecture
Audit. classes
Lab. classes
Project classes
Conv. seminar
Seminar classes
Pract. classes
Zaj. terenowe
Zaj. warsztatowe
Others
E-learning
Skills
M_U001 Is able to use knowledge discovery software. - - + - - - - - - - -
M_U002 Is able to choose a machine learning technique to a given practical problem. - - + - - - - - - - -
Knowledge
M_W001 Knows and understands basic ideas of machine learning + - - - - - - - - - -
M_W002 Knows popular machine learning algorithms + - - - - - - - - - -
Module content
Lectures:
  1. Introduction and basic ideas

    Characteristics of the domain. Definitions. Types of algorithms. Demo.

  2. Concept learning

    Hipothesis space. Concept learning algorithms.

  3. Tree induction

    ID3, C4.5 algorithms. Pruning. Conversion to rules.

  4. Rule induction

    AQ and CN algorithms.

  5. Rough sets

    Definitions. Reducts. Rules.

  6. Reinforcement learning

    Markov precess definition. Q learning algorithm.

  7. Inductive logic programming

    Short introduction to Prolog. Idea of the learning algorithm. Examples.

Laboratory classes:
  1. Supervised learning

    Preparing the data and knowledge discovery in Weka software.

  2. Reinforcement learning

    Writing learning agents in java using PIQLE library.

  3. Rough sets

    Knowledge discovery with RSES system.

  4. Inductive Logic Programming

    Introduction to Prolog. Golem and Progol packages.

Student workload (ECTS credits balance)
Student activity form Student workload
Summary student workload 100 h
Module ECTS credits 4 ECTS
Contact hours 28 h
Preparation for classes 45 h
Realization of independently performed tasks 17 h
Completion of a project 10 h
Additional information
Method of calculating the final grade:

AVG_LAB := average of grades from laboratory classes
AVG := (AVG_LAB + EXAM_GRADE)/2
if AVG>4.5 then OK:=5.0 else
if AVG>4.0 then OK:=4.5 else
if AVG>3.5 then OK:=4.0 else
if AVG>3.0 then OK:=3.5 else
if AVG>2.75 then OK:=3.0 else
OK:=2.0

Prerequisites and additional requirements:

Basic knowledge about Artificial Intelligence.

Recommended literature and teaching resources:

Tom Mitchell, Machine Learning, McGraw Hill, 1997.
Ethem Alpaydin, Introduction to Machine Learning, second edition, MIT Press, 2009

Scientific publications of module course instructors related to the topic of the module:

Additional scientific publications not specified

Additional information:

None