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.
- 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
- 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
- 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
- 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
- 13:00 : Zahid, Farwa / Bayzidi, Yasin / Kumar, Sumit / Oruc, Orcun
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
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:
- Pattern recognition
- Linear machines
- Learning Theory
- Neural Networks
- Support vector machines
- Deep Learning
- Recurrent neural networks
- Reinforcement Learning
- Formal definition of the RL-Problem
- Dynamic Programming
- Monte Carlo Methods
- Temporal Difference Learning (TD)
- Eligibility traces
- Function approximation
- Deep Reinforcement learning
- "Deep Learning", Goodfellow, Bengio & Courville
- "Support Vector Machines and other kernel-based learning methods", Cristianini & Shawe-Taylor
- "Reinforcement Learning", Sutton & Barto (1st and 2nd editions)
Slides for the lecturesChapter 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)
ExercisesExercise 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 )