Hasso-Plattner-Institut20 Jahre HPI
Hasso-Plattner-Institut20 Jahre HPI
  
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Machine Intelligence with Deep Learning (Wintersemester 2019/2020)

Lecturer: Dr. Haojin Yang (Internet-Technologien und -Systeme) , Christian Bartz (Internet-Technologien und -Systeme) , Joseph Bethge (Internet-Technologien und -Systeme) , Ting Hu (Internet-Technologien und -Systeme)

General Information

  • Weekly Hours: 4
  • Credits: 6
  • Graded: yes
  • Enrolment Deadline: 01.10.-30.10.2019
  • Teaching Form: Seminar / Project
  • Enrolment Type: Compulsory Elective Module
  • Course Language: English
  • Maximum number of participants: 12

Programs & Modules

IT-Systems Engineering MA
Data Engineering MA
  • DATA-Konzepte und Methoden
  • DATA-Techniken und Werkzeuge
  • DATA-Spezialisierung
  • CODS-Konzepte und Methoden
  • CODS-Techniken und Werkzeuge
  • CODS-Spezialisierung
Digital Health MA
Cybersecurity MA

Description

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

Requirements

  • 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

Literature

Books

  • Alex Smola, Mu Li et al., Dive into deep learning
  • 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

Online courses:

  • cs231n tutorials: Convolutional Neural Networks for Visual Recognition
  • Deep Learning courses at Coursera

Deep Learning frameworks:

Examination

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)

Dates

Montag, 15.15-16.45

Room H-E.51

14.10.2019

Vorstellung der Seminar Themen (PDF)

20.10.2019

Wahl der Themen  (Anmelden on Doodle)

21-23.10.2019

Bekanntgabe der Themen- und Gruppenzuordnung

wöchentlich

Individuelle Meetings mit dem Betreuer

02.12.2019

Technologievorträge und geführte Diskussion (je 15+5min)

03.02.2020

Präsentation der Endergebnisse (je 15+5min)

bis Ende Februar 2020

Abgabe von Implementierung und Dokumentation (Latex template)

bis Ende März 2020

Bewertung der Leistungen

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