Seminars on: Introduction to Machine Learning (SS 22)
Current Issues
For any queries write an email to panda@na.uni-tuebingen.de.
The first (introductory) meeting (via zoom) took place on Wednesday, 2nd of March 2022 at 3pm local time. In this meeting, a motivation towards machine learning and the plans to conduct the seminars were discussed. The allotted topics and the individual meetings with the students to plan for the seminars are allotted to the students.
Seminar timings
The seminars will be on Wednesdays from 16:30-18:30 hrs (via zoom).
Content of the Course
- Machine learning is a branch of artificial intelligence (AI) which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy from experience without being explicitly programmed to do so.
- 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 may include
- Supervised learning handout pdf ;
- Unsupervised learning handout pdf ;
- 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
Probability, Basic Calculus, Basic Linear Algebra, and Hilbert space theory for the last two topics.
Dates of Seminars
- The seminar on Supervised learning will be conducted on the 4th of May 2022. The zoom details are shared via email.
- The seminar on Unupervised learning will be conducted on the 11th of May 2022. The zoom details will be shared before the seminar.
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
