Course Catalog 2014-2015
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Course Catalog 2014-2015

SGN-42006 Machine Learning, 5 cr

Additional information

Lectures in English or in Finnish.
Suitable for postgraduate studies

Person responsible

Ari Visa

Lessons

Study type P1 P2 P3 P4 Summer Implementations Lecture times and places
Lectures
Excercises


 
 4 h/per
 4 h/per


 


 


 
SGN-42006 2014-01 Monday 12 - 14 , TB104
Friday 14 - 16 , TB103

Requirements

Final exam, attendance at the classroom exercises and assignment.
Completion parts must belong to the same implementation

Learning Outcomes

Learning outcomes: The student can describe the difference artificial intelligence and machine learning. The student can list the mentioned learning rules. The student can describe them and is capable to apply them to train neural networks. The student is capable to list to analyse the lectured neural networks (MLP,SVM,SOM and recurrent networks). The student is capable to analyse the own problem and to select the most suitable, lectured neural network. The student has a certain capability to create new solutions based on the lectured material.

Content

Content Core content Complementary knowledge Specialist knowledge
1. Learning processes     
2. Learning machines with a teacher  Multilayer Perceptrons Radial-Basis Function Networks Support Vector Machines Committee Machines Boltzmann Machine   
3. Learning machines without a teacher  Principal Component Analysis with Neural Networks Self-Organizig Maps    
4. Nonlinear dynamical systems  Temporal Processing Using Feed Forward Network Dynamically Driven Recurrent Network   

Instructions for students on how to achieve the learning outcomes

Exam and approx. 70% attendance at the exercises.

Assessment scale:

Numerical evaluation scale (1-5) will be used on the course

Partial passing:

Completion parts must belong to the same implementation

Study material

Type Name Author ISBN URL Edition, availability, ... Examination material Language
Book   "Neural Networks: a Comprehensive Foundation"   Haykin, S.       2nd edition, Prentice-Hall Inc, 1999   Yes    English  

Prerequisites

Course Mandatory/Advisable Description
SGN-11000 Signaalinkäsittelyn perusteet Advisable   1
SGN-11006 Basic Course in Signal Processing Advisable   1
SGN-13000 Johdatus hahmontunnistukseen ja koneoppimiseen Advisable   2
SGN-13006 Introduction to Pattern Recognition and Machine Learning Advisable   2

1 . SGN-11000 tai SGN-11006

2 . SGN-13000 tai SGN-13006

Prerequisite relations (Requires logging in to POP)



Correspondence of content

Course Corresponds course  Description 
SGN-42006 Machine Learning, 5 cr SGN-2806 Neural Computation, 5 cr  

More precise information per implementation

Implementation Description Methods of instruction Implementation
SGN-42006 2014-01 . Learning outcomes: The student can describe the difference artificial intelligence and machine learning. The student can list the mentioned learning rules. The student can describe them and is capable to apply them to train neural networks. The student is capable to list to analyse the lectured neural networks (MLP,SVM,SOM and recurrent networks). The student is capable to analyse the own problem and to select the most suitable, lectured neural network. The student has a certain capability to create new solutions based on the lectured material.        

Last modified25.02.2015