Hasso-Plattner-InstitutSDG am HPI
Hasso-Plattner-InstitutDSG am HPI

Competitive Problem Solving with Deep Learning (Sommersemester 2018)

Dozent: Dr. Haojin Yang (Internet-Technologien und -Systeme)
Tutoren: Christian Bartz Joseph Bethge Goncalo Filipe Torcato Mordido

Allgemeine Information

  • Semesterwochenstunden: 4
  • ECTS: 6
  • Benotet: Ja
  • Einschreibefrist: 20.04.2018
  • Lehrform: Vorlesung / Projekt
  • Belegungsart: Wahlpflichtmodul
  • Maximale Teilnehmerzahl: 50

Studiengänge, Modulgruppen & Module

IT-Systems Engineering MA
  • IT-Systems Engineering
    • HPI-ITSE-A Analyse
  • IT-Systems Engineering
    • HPI-ITSE-E Entwurf
  • IT-Systems Engineering
    • HPI-ITSE-K Konstruktion
  • IT-Systems Engineering
    • HPI-ITSE-M Maintenance
  • ISAE: Internet, Security & Algorithm Engineering
    • HPI-ISAE-S Spezialisierung
  • ISAE: Internet, Security & Algorithm Engineering
    • HPI-ISAE-T Techniken und Werkzeuge
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-K Konzepte und Methoden
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-S Spezialisierung
  • OSIS: Operating Systems & Information Systems Technology
    • HPI-OSIS-T Techniken und Werkzeuge


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". 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. 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 this lecture, we will study both theoretical foundations and practical use cases of deep learning techniques. Moreover, a subsequent competitive project will let you get hands-on experience.

The outline of the lecture is thus as follows:

Lecture part:

  • Theoretical Foundation   
    • Machine learning foundation
    • Neural Network I
    • Neural Network II
    • Architectures design and advanced techniques in Deep Learning
    • Deep Learning frameworks, understanding and visualization, latest research topics
  • Practical use case
    • Build neural network from scratch
    • Advanced techniques in practice
    • Deep learning for computer vision problems
    • Generative networks

Projekt part:

An Image Recognition Challenge

The participants of the course will freely group into several teams (the number of members of each team should be balanced) that try to solve a challenging image recognition problem. The teaching team is responsible for data preparation, prize preparation, organization, evaluation, and be ready for discussion with each team.

Course language: German and English


  • 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


The final evaluation will be based on:

  • Written exam 40%

  • Final presentation 20%

  • Report/Documentation and Implementation 40%

  • Participation in the lecture (bonus points)


Montag, 13.30-15.00

Room: HS 3 not in H-2.57 anymore (except 23.04.2018 and 18.06.2018 in HS 1)

Lecture part:


Course Introduction (PDF)

bis 20.04.2018 



Machine learning foundation (T) (PDF) (codes)


Neural Network I (T) (in HS 1) (PDF) (codes)


Build network from scratch (P) (PDF)(codes)


Neural Network II (T) (PDF)


Advanced techniques in practice (P) (PDF) (codes) (MP4)


CNN architectures and advanced techniques in Deep Learning (T) (PDF) (caffe prototxt files for Netscope tool) (Netscope)


Deep learning for computer vision problems (P) (PDF) (code)


Deep Learning visualization techniques, Competition Introduction (T) (PDF)


Generative networks (P) (in HS 1) (PDF)


Written exam (in HS 1)

(T: theoretical lecture, P: practical use cases)

Projekt part:


Challenge open: release training und validation data, grouping


Weekly individual meeting with each team


Release test set1


Release pre-ranking result based on the test result offered by participants


Model submission: Tutors will run the models using a secret test dataset


Final Presentation (all PDFs), release final ranking result, awards granting

bis 31. August

Implementation + Paper submission (LaTeX template)