1. Machine Learning Lecture 1

2. Course Information Text book “Introduction to Machine Learning” by Ethem Alpaydin, MIT Press. Reference book “Data Mining Introductory & Advanced Topics” by Margaret H. Dunham.

3. Course Objective Introduction to the basic principles, techniques, and applications of Machine Learning e.g. –data-driven knowledge discovery (data mining). –Search engine architectures. –Speech recognition. –Face recognition. Preparation of job skills for machine learning Be able to conduct original research in machine learning.

4. Course Contents Machine learning applications. Supervised learning. Bayesian decision theory. Parametric methods. Multivariate methods. Principal Component Analysis. Clustering

5. Course Contents contd. Decision trees. Linear discriminants. Multilayer perceptrons. Competitive learning & Radial Basis Functions. Hidden Markov Models. Comparing Classification Algorithms. Combining multiple learners. Re-inforcement Learning

6. Grading Policy Internal Marks = 40 –Lab performance = 8 Marks –Assignments=20 Marks –Theory Exams=12 Marks External Marks= 10

7. Learning? Intelligence is based upon learning. Learning is based upon past experience and knowledge about the problem. Prediction – we make predictions all the times but rarely investigate the underlying process. Specific knowledge (data) processing approach for a particular problem.

8. Machine Learning? Machine Learning is subfield of Artificial Intelligence concerned with design and development of algorithms and techniques that allow computers to learn (Wikipedia). Machine learning is area of AI that examines how to write programs that can learn.

9. Components in Machine Learning Data acquisition or Knowledge from Experts. Development of Training Data or Rules. Specific model development.

10. Applications of Machine Learning Data mining. Search engines. Game playing. Object recognition. Robot locomotion. Bioinformatics. Cheminformatics. Natural language processing etc.

11. Data Mining Data Mining is a set of processes related to analyzing and discovering useful, actionable knowledge buried deep beneath large volumes of data sets.

12. Typical Job Structures of Machine Learning Professionals Object Oriented Programming –C++ –Perl –Python –Smaltalk Database environments –Lotus. –SQL server. –Excel. –Oracle. Machine Learning Concepts.