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Professur Künstliche Intelligenz

Machine learning

Exam in SS2017

Appointments for the oral exams are announced below (room: 1/348). No change is possible!

You have to be present at the beginning of a block (10:45, 13:00 or 15:00) and wait for your turn.

Deregistration is only possible up to one week before your appointment. It has to be done on SBService and by sending me an email.

07.08.2017

  • 10:45 : Ria Armitha / Supreeth Srinivasa Reddy
  • 13:00 : Ara, Azmi / Balaji, Thangapavithraa / Bhavaraju, Divya / Bogolu, Srilaxmi
  • 15:00 : Bollineni, Tarun / Dixit, Sudhanshu / Donapati, Harshavardhan / Fathima, Nida

08.08.2017

  • 10:45 : Giregowdanahalli Ranga / Gnana Prakash, Deepikh
  • 13:00 : Hamza, Ameer / Heena, Sharma / Karemane Jayakumar, A / Karvala, Vasanthi
  • 15:00 : Kekud, Abhishek / Khan, Md. Moinuddin / Khazi, Manzoornawaz / Mahajan, Rahul

09.08.2017

  • 10:45 : Miryala, Hareesh Kumar / Mohite, Prafull
  • 13:00 : Morthala, Anil Kumar Reddy / Mumtaz, Ans / Pande, Himanshu
  • 15:00 : Puliadi Baghdad, Mohammed / Ravi, Keerthirajhan / Rayaprolu, Venkata Sury / Raza, Hussnain

14.08.2017

  • 10:45 : Sajid, Waleed / Salman Raj / Sangabathula, Nagalaxmi
  • 13:00 : Sarwar, Shazma / Shaik Touseef Ahmed, Shaik / Shivanand, Nandini / Singh, Deepti
  • 15:00 : Syed, Saiffuddin / Thataraju, Gowri Babu / Vasa, Prashanth / Yellutla Sankar, Rushi

15.08.2017

  • 13:00 : Zahid, Farwa / Bayzidi, Yasin / Kumar, Sumit / Oruc, Orcun

General Information

Prerequisites: Modules in Mathematics I to IV, basic knowledge in Python.

Exam: oral examination (20 minutes), 5 credit points.

Contact: julien dot vitay at informatik dot tu-chemnitz dot de.

Office hours: Mondays between 13:00 and 14:00

Content

The course will present an introduction to the research field of Machine Learning, including Supervised Learning, Unsupervised Learning and Reinforcement Learning methods. The different algorithms presented during the lectures will be studied in more details during the exercises, through implementations in Python. Previous knowledge of Python is a plus.

The plan of the course is:

  1. Pattern recognition
    1. Linear machines
    2. Learning Theory
    3. Neural Networks
    4. Support vector machines
    5. Deep Learning
    6. Recurrent neural networks
  2. Reinforcement Learning
    1. Formal definition of the RL-Problem
    2. Dynamic Programming
    3. Monte Carlo Methods
    4. Temporal Difference Learning (TD)
    5. Eligibility traces
    6. Function approximation
    7. Deep Reinforcement learning

Literature

Slides for the lectures

Chapter 01 - Introduction (pdf)
Chapter 02 - Linear learning machines (pdf)
Chapter 03 - Learning theory (pdf)
Chapter 04 - Neural networks (pdf)
Chapter 05 - Support-vector machines (pdf)
Chapter 06 - Deep learning (pdf)
Chapter 07 - Recurrent neural networks (pdf)
Chapter 08 - Reinforcement Learning (pdf)
Chapter 09 - Deep Reinforcement Learning (pdf)

Exercises

Exercise 01 - Introduction to Python and NumPy. (pdf , data , solution )
Exercise 02 - Linear classification. (pdf , data , solution )
Exercise 03 - Cross-validation. (pdf , data , solution )
Exercise 04 - Multi-layer perceptron. (pdf , data , solution )
Exercise 05 - Multi-layer perceptron on the MNIST dataset. (pdf , data , solution )
Exercise 06 - Support-vector machines. (pdf , data )
Exercise 07 - Convolutional neural networks. (pdf , data , solution )
Exercise 08 - Recurrent neural networks. Skipped.
Exercise 09 - Reinforcement learning. (pdf , solution )

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