https://www.tu-chemnitz.de/informatik/KI/edu/ml/
Machine learning
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:
- Supervised Learning
- Linear classification and regression
- Learning Theory
- Neural Networks
- Support vector machines
- Unsupervised Learning
- Clustering
- PCA, LDA
- Deep learning
- Reinforcement Learning
- Formal definition of the RL-Problem
- Dynamic Programming
- Monte Carlo Methods
- Temporal Difference Learning (TD)
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.
Exam dates in 2016: 16.02, 22.02, 23.02, 29.02, 01.03, 02.03 and 03.03.
Registration closed! Deregistration is only possible until one week prior to the appointment. No rescheduling possible.
Exam location: in my office 1/348.
Surya Narayana Varma Ruddaraju: empty your mailbox! Your exam is on 02.03 at 10:45.
Literature
- "Neural Networks and Learning Machines", Haykin.
- "Support Vector Machines and other kernel-based learning methods", Cristianini & Shawe-Taylor
- "Reinforcement Learning", Sutton & Barto
Slides for the lectures
Chapter 01 - IntroductionChapter 02 - Linear learning machines
Chapter 03 - Learning theory
Chapter 04 - Neural networks
Chapter 05 - Support-vector machines
Chapter 06 - Clustering, dimensionality reduction
Chapter 07 - Deep learning.
Chapter 08 - Reinforcement Learning.
Exercises and solutions
Exercise 01 - Introduction to Python and NumPy. Text - Data - Solution.Exercise 02 - Linear classification. Text - Data - Solution.
Exercise 03 - Cross-validation. Text - Data - Solution.
Exercise 04 - Multi-layer perceptron. Text - Data - Solution.
Exercise 05 - Multi-layer perceptron on the MNIST dataset. Text - Data - Solution.
Exercise 06 - Support-vector machines. Text - Data.
Exercise 07 - K-means. Text - Data - Solution.
Exercise 08 - PCA. Text - Data.
Exercise 09 - Reinforcement learning. Text - Solution.
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exercise1-solution.zip
exercise1.pdf
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