Seminar: An Introduction to Machine Learning (WS 20/21)
- The short-notes on the topics can be found here.
- Supervised learning pdf ,
- Unupervised learning pdf ,
- Linear models for regression pdf ,
- Linear classification and Logistic regression pdf ,
- Neural networks pdf ,
- Kernel methods pdf ,
- Probabilistic graphical models pdf ,
- For any queries write an email to firstname.lastname@example.org.
- 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. The individual meetings with the students to plan for the seminars have been done.
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 include
- Supervised learning;
- Unsupervised learning;
- Neural networks and Deep learning;
- Linear models for regression;
- Linear models for classification;
- Logistic regression;
- Probabilistic graphical models;
- Kernel Methods;
- Mixture Models.
Prerequisites Basic Calculus, Basic Linear Algebra.
Dates of Seminars
- The first talk will be on "Supervised Learning" by Joschka Braun on 9th of November, Monday (from 3:30pm to 4:30pm) via Zoom. The details of the Zoom meeting will be sent via email later. The handout will be sent soon.
- The second talk will be on "Unsupervised Learning" by Hannah Van Santvliet on 16th of November, Monday (from 3:30pm to 4:30pm) via Zoom. The details of zoom meeting will be sent via email later.
- 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