Seminars on: Special topics on Machine Learning (WS 21/22)
Current Issues
- For any queries write an email to panda@na.uni-tuebingen.de.
- The first (introductory) meeting took place on Monday, 2nd of August at 4pm local time. In this meeting, a motivation towards special topics on machine learning and the plans to conduct the seminars has been discussed.
Content of the Course
- While artificial intelligence (AI) is the broad science of mimicking human abilities, machine learning is a specific subset of AI that trains a machine how to learn. It was born from pattern recognition and the theory that computers can learn without being programmed to perform specific tasks.
- We are completely surrounded by machine learning based technologies such as anti-spam software learns to filter our emails, credit card transactions are secured by a software that learns how to detect frauds etc. Due to machine learning algorithms, self driving cars are capable of sensing its surroundings and moving safely with little or no human input and smart-phones learn to detect faces and have personal assistance applications that learn to recognize voice commands. Machine learning is also widely used in scientific applications such as bioinformatics, medicine, and astronomy.
- We plan seminars on special topics in Machine Learning, which include
- Supervised learning;
- Unsupervised learning;
- Reinforcement learning;
- Linear models for regression;
- Linear models for classification;
- Logistic regression;
- Neural networks;
- Deep learning;
- Probabilistic graphical models;
- Kernel Methods;
- Support Vector Machines.
Prerequisites
Basic Calculus, Basic Linear Algebra, Probability.
Dates of Seminars
- Every Wednesday from 16:30 to 18:30. The zoom meeting details are sent via email before the talks.
References
- Pattern Recognition and Machine Learning by Christopher M. Bishop
- Machine Learning: a Probabilistic Perspective by Kevin P. Murphy
- Introduction to Machine Learning with Python by Andreas C. Müller & Sarah Guido
Course Co-ordinator: Dr. Akash Ashirbad Panda
