Seminar: An Introduction to Machine Learning (WS 20/21)
- Students those are interested can send an email to email@example.com. Students willing to participate in the seminar should register on URM.
- The first (introductory) meeting took place on Monday, 27th of July at 3pm local time. In this meeting, a motivation towards machine learning and the plans to conduct the seminars has been discussed.
Content of the Course
- Machine Learning is an application of artificial intelligence (AI) that provides systems with the ability to automatically learn and improve from experience without being explicitly programmed.
- 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 a basic introduction to Machine Learning. Subjects of seminar talks could include
- Supervised learning;
- Unsupervised learning;
- Reinforcement learning;
- Neural networks and Deep learning;
- Linear models for regression;
- Linear models for classification;
- Support Vector Machines;
Prerequisites Basic Calculus, Basic Linear Algebra.
Dates of meeting
- 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