Study unit

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3621675 Deep Learning, 6 Cp 
Code 3621675  Validity 01.01.1950 -
Name Deep Learning  Abbreviation DEL 
Credits6 Cp  Date of expiry  
TypeAdvanced Studies Subject0530 Computer Science 
ClassStudy Unit  Hours  
Study right   Grading0-5 
Recommended scheduling 
   
Organisation Computer Science (J,K) 

Description
Learning outcomes 

Students understand the theory of neural networks, Nature-Inspired Computing (NIC) methods, and deep learning algorithms. Students get familiar with the most popular neural networks models (Perceptron, Adaline, multilayer neural networks, Self-Organizing Map (SOM), etc.) and their learning algorithms (Widrow-Hoff rule, Back-Propagation (BP) training, Back-propagation Through Time (BTT) training, competitive learning, etc.). Students master the essential knowledge of deep learning techniques, such as regularization, momentum, batch normalization, and dropout. Students understand the principles, structures, and algorithms of typical deep learning neural networks, e.g., Convolutional Neural Networks (CNN). Students know how to build up deep learning algorithms from scratch. Students gain the hand-on experiences in using deep learning techniques to deal with practical problems

 
Content 

Elementary concepts and challenges of machine learning. Neural networks models (linear neural networks, feedforward neural networks, recurrent neural networks, Self-Organizing Map (SOM), etc.). Deep reinforcement learning. Concepts and challenges of deep learning. Deep learning models and techniques (deep neural networks, Convolutional Neural Networks (CNN), AutoEncoder, Long Short Term Memory (LSTM), etc.). Applications of deep learning in classification, prediction, pattern recognition, etc.

 
Modes of study 

Lectures, teaching materials, exercises, and examination

 
Teaching methods 

distance teaching and study                           

 
Study materials 

I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning, MIT Press, 2017, A. P. Engelbrecht, Fundamentals of Computational Swarm Intelligence, John Wiley & Sons Ltd, 2005, S. Haykin, Neural Networks, A Comprehensive Foundation, Prentice Hall, 2008, lecture slides and notes, selected papers from journals and conference proceedings

 
Evaluation criteria 

Grading (80% examination and 20% computer exercises): 0-5

 
Prerequisites 

Basic knowledge of linear algebra and probability theory              

 
Time 

Fall semester

 
Offering data 

This course is open to everyone

 
Further information 

Teaching language: English

 


Letter (J, K) in front of the name of the course/exam indicates the campus on which teaching or exam takes place: J = Joensuu, K = Kuopio.

Present and future teaching
Functions Name Type Cp Teacher Timetable
Register (KUOPIO): Deep Learning  Lecture and exercise course  Xiaozhi Gao  27.10.20 -05.02.21
Register (JOENSUU): Deep Learning  Lecture and exercise course  Xiaozhi Gao  27.10.20 -05.02.21

Future exams
No exams