NEURAL NETWORKS - DEEP LEARNING

Course Information
TitleΝΕΥΡΩΝΙΚΑ ΔΙΚΤΥΑ - ΒΑΘΙΑ ΜΑΘΗΣΗ / NEURAL NETWORKS - DEEP LEARNING
CodeNDM-07-05
FacultySciences
SchoolInformatics
Cycle / Level1st / Undergraduate
Teaching PeriodWinter
CoordinatorAnastasios Tefas
CommonYes
StatusActive
Course ID600016578

Programme of Study: PPS-Tmīma Plīroforikīs (2019-sīmera)

Registered students: 35
OrientationAttendance TypeSemesterYearECTS
GENIKĪ KATEUTHYNSĪYPOCΗREŌTIKO KATA EPILOGĪ745

Class Information
Academic Year2019 – 2020
Class PeriodWinter
Faculty Instructors
Weekly Hours4
Class ID
600154361
Course Type 2016-2020
  • Background
  • General Knowledge
  • Scientific Area
  • Skills Development
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
Erasmus
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
Prerequisites
General Prerequisites
Previous knowledge on linear algebra, calculus and pattern recognition is helpful
Learning Outcomes
Cognitive: Foundation and mathematical formalization of the learning and generalization concept and its importance in the computational intelligence field. Comparative conception of the basic neural network and deep learning types. Training principles and learning abilities of the learning machines. Exposition to the diverse applications of neural networks in the fields of pattern recognition, function approximation, data mining and information retrieval. Skills: Promoting analytic and implementation skills in classification and function approximation using neural networks and deep learning. Implementation of the corresponding neural network training algorithms. Use of deep learning libraries like pytorch or tensorflow for real problems. Promoting programming skills. Ability to complete a learning problem by implementing a neural network algorithm, running experiments on real data, writing a short paper and presenting the results.
General Competences
  • Apply knowledge in practice
  • Retrieve, analyse and synthesise data and information, with the use of necessary technologies
  • Make decisions
  • Work autonomously
  • Work in teams
  • Generate new research ideas
  • Design and manage projects
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Introduction to artificial Neural Networks (ANN). Learning procedures and prototypes. Applications of ANN. Knowledge representation in ANN. Training of Perceptron. The ADALINE algorithm. Multilayer perceptrons (MLPs) and the back-propagation algorithm. Deep Learning. Support vector machines (SVMs). Radial Basis Functions (RBFs). Hebbian learning. Principal Component Analysis Neural Networks (PCA-NN). Non-linear Hebbian models. Independent Component Analysis Neural Networks (ICA-NN). Learning theory and generalization. Applications of ANN to pattern recognition, function approximation, image compression and signal deconvolution.
Keywords
Neural Networks, Computational Intelligence, Deep Learning
Educational Material Types
  • Slide presentations
  • Multimedia
  • Interactive excersises
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Communication with Students
Description
Slides, Applications, Software libraries demonstration, e-learning communication.
Course Organization
ActivitiesWorkloadECTSIndividualTeamworkErasmus
Lectures102
Reading Assigment6
Project34
Written assigments6
Exams2
Total150
Student Assessment
Description
Project evaluation (source code, application, paper, presentation) 50%, written exams 50%. The evaluation criteria are explained in detail with the project anouncement. The evaluation criteria for the exams are discussed in the last lectures of the course.
Student Assessment methods
  • Written Exam with Short Answer Questions (Formative, Summative)
  • Written Assignment (Formative, Summative)
  • Oral Exams (Formative, Summative)
  • Performance / Staging (Summative)
  • Written Exam with Problem Solving (Summative)
  • Report (Summative)
Bibliography
Course Bibliography (Eudoxus)
1. ΤΕΧΝΗΤΑ ΝΕΥΡΩΝΙΚΑ ΔΙΚΤΥΑ, Κ. ΔΙΑΜΑΝΤΑΡΑΣ, ΚΛΕΙΔΑΡΙΘΜΟΣ, 2007, ΑΘΗΝΑ 2. ΝΕΥΡΩΝΙΚΑ ΔΙΚΤΥΑ ΚΑΙ ΜΗΧΑΝΙΚΗ ΜΑΘΗΣΗ, 3η ΕΚΔΟΣΗ, S. Haykin, Παπασωτηρίου, 2010, Αθήνα.
Additional bibliography for study
ΥΠΟΛΟΓΙΣΤΙΚΗ ΝΟΗΜΟΣΥΝΗ, Σ. ΤΖΑΦΕΣΤΑΣ, Σ.ΤΖΑΦΕΣΤΑΣ, 2η Εκδοση, ΑΘΗΝΑ, 2008
Last Update
07-12-2020