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Machine Intelligence with Deep Learning (Wintersemester 2018/2019)

Dozent: Dr. Haojin Yang (Internet-Technologien und -Systeme)
Tutoren: Mina Rezaei Christian Bartz Joseph Bethge

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 26.10.2018
  • Lehrform: Seminar / Projekt
  • Belegungsart: Wahlpflichtmodul
  • Maximale Teilnehmerzahl: 20

Studiengänge & Module

IT-Systems Engineering MA
Data Engineering MA
Digital Health MA
  • DICR-Concepts and Methods
  • DICR-Technologies and Tools
  • DICR-Specialization
  • APAD-Concepts and Methods
  • APAD-Technologies and Tools
  • APAD-Specialization

Beschreibung

Artificial intelligence (AI) is the intelligence exhibited by computer. This term is applied when a machine mimics "cognitive" functions that humans associate with other human minds, such as "learning" and "problem solving". Currently researchers and developers in this field are making efforts to AI and machine learning algorithms which intend to train the computer to mimic some human skills such as "reading", "listening", "writing" and "making inference" etc. From the year 2006 "Deep Learning" (DL) has attracted more and more attentions in both academia and industry. Deep learning or deep neural networks is a branch of machine learning based on a set of algorithms that attempt to learn representations of data and model their high level abstractions. In a deep network, there are multiple so-called "neural layers" between the input and output. The algorithm is allowed to use those layers to learn higher abstraction, composed of multiple linear and non-linear transformations. Recently DL gives us break-record results in many novel areas as e.g., beating human in strategic game systems like Go (Google’s AlphaGo), self-driving cars, achieving dermatologist-level classification of skin cancer etc. In our current research we focus on video analysis and multimedia information retrieval (MIR) by using Deep-Learning techniques.

Course language: German and English

Topics in this seminar:

  • coming soon!

Voraussetzungen

  • Strong interests in video/image processing, machine learning (Deep Learning) and/or computer vision

  • Software development in C/C++ or Python

  • Experience with OpenCV and machine learning applications as a plus

Literatur

  • Ian J. Goodfellow and Yoshua Bengio and Aaron Courville, "Deep Learning", online version
  • Pedro Domingos “The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World”
  • Christopher M. Bishop “Pattern Recognition and Machine Learning” google it
  • cs231n tutorials: Convolutional Neural Networks for Visual Recognition
  • Deep Learning courses at Coursera
  • "Deep Learning - The Straight Dope” deep learning tutorials created by MXNet team
  • Caffe: Deep learning framework by the BVLC
  • Chainer: A flexible framework of neural networks
  • MXNet: A Flexible and Efficient Library for Deep Learning
  • Tensorflow: An open-source machine learning framework

Leistungserfassung

The final evaluation will be based on:

  • Initial implementation / idea presentation, 10%

  • Final presentation, 20%

  • Report/Documentation, 12-18 pages, 30%

  • Implementation, 40%

  • Participation in the seminar (bonus points)

Termine

Montag, 15.15-16.45

Room H-E.51

15.10.2018 15:15-16:45

Vorstellung der Seminar Themen (PDF)

bis 22.10.2018 

Wahl der Themen  (Anmelden on Doodle)

23.10.2018

Bekanntgabe der Themen- und Gruppenzuordnung

wöchentlich

Individuelle Meetings mit dem Betreuer

03.12.2018

Technologievorträge und geführte Diskussion (je 15+5min)

04.02.2019

Präsentation der Endergebnisse (je 15+5min)

bis Ende Februar 2019

Abgabe von Implementierung und Dokumentation

bis Ende März 2019

Bewertung der Leistungen

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