Deep Learning and Multimedia Information Analysis

Course Information
TitleΒαθιά Μάθηση και Ανάλυση Πολυμεσικών Δεδομένων / Deep Learning and Multimedia Information Analysis
Cycle / Level2nd / Postgraduate
Teaching PeriodSpring
CoordinatorAnastasios Tefas
Course ID600016145

Programme of Study: PMS PSĪFIAKA MESA - YPOLOGISTIKĪ NOĪMOSYNĪ (2018 éōs sīmera) PF

Registered students: 20
OrientationAttendance TypeSemesterYearECTS
KORMOSElective Courses belonging to the selected specialization217.5

Class Information
Academic Year2018 – 2019
Class PeriodSpring
Faculty Instructors
Weekly Hours3
Class ID
Course Type 2021
Specialization / Direction
Course Type 2016-2020
  • Background
  • General Knowledge
  • Scientific Area
  • Skills Development
Course Type 2011-2015
Specific Foundation / Core
Mode of Delivery
  • Face to face
Digital Course Content
The course is also offered to exchange programme students.
Language of Instruction
  • Greek (Instruction, Examination)
General Prerequisites
Knowledge of linear algebra, pattern recognition and machine learning, deep learning and neural networks will help the students follow easier the course schedule.
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 architectures. Training principles and learning abilities of the learning machines. Exposition to the diverse applications of neural networks and deep learning in the fields of pattern recognition, function approximation, data mining and information retrieval. Skills: Promoting analytic and implementation skills in pattern recognition and function approximation using neural networks deep learning and implementation of the corresponding deep neural network training algorithms. Use of deep learning libraries 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
  • Adapt to new situations
  • Make decisions
  • Work autonomously
  • Generate new research ideas
  • Design and manage projects
  • Be critical and self-critical
  • Advance free, creative and causative thinking
Course Content (Syllabus)
Neural Nwtworks and Deep Learning introduction. Back-propagation. Deep Autoencoders and representation learning. Deep Boltzmann Machines. Convolutional Neural Networks. Reccurent Neural Networks. Generative adversarial networks. Deep Reinforcement Learning. Knowledge transfer. Optimization, regularization, over-training and generalization in deep learning. DL model architecture design and training. DL libraries and frameworks. Computational complexity of DL models and use of parallel programming in GPUs. DL for embedded systems. Applications of DL for data analysis, classification, clustering and retreival. Applications on text, audio, image and video. Applications on autonomous systems and robotics.
Deep Learning, Neural Networks, Multimedia Data Analysis
Educational Material Types
  • Notes
  • Slide presentations
  • Video lectures
  • Multimedia
  • Book
Use of Information and Communication Technologies
Use of ICT
  • Use of ICT in Course Teaching
  • Use of ICT in Communication with Students
Slides, demos, software libraries, communication through web and mail.
Course Organization
Reading Assigment86
Written assigments15
Student Assessment
Project evaluation (source code, application, paper, presentation) 60%, written exams 40%. 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 (Summative)
  • Written Exam with Extended Answer Questions (Summative)
  • Written Assignment (Formative, Summative)
  • Oral Exams (Formative, Summative)
  • Performance / Staging (Formative, Summative)
  • Written Exam with Problem Solving (Summative)
  • Report (Summative)
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. 3. Διαφάνειες και υλικό σε ηλεκτρονική μορφή 4. Free E-books στα Αγγλικά
Additional bibliography for study
Deep Learning, Ian Goodfellow and Yoshua Bengio and Aaron Courville, MIT Press, 2016,
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