Machine Learning (Extended) Dr. Ata Kaban

1. Machine Learning (Extended)Dr. Ata Kaban • Algorithms to enable computers to learn • Learning = ability to improve performance automatically through experience • Experience = previously seen examples • Interdisciplinary field • AI • Probability & Statistics • Information theory • Philosophy • Control theory • Psychology • Neurobiology, etc

2. Syllabus 1.Overview of machine learning. Basic notions, literature 2.Supervised learning Generative methods Discriminative methods Computational learning theory basics Boosting and ensemble methods 3.Unsupervised learning Clustering Learning for structure discovery 4.Reinforcement learning basics 5.Topics in learning from high dimensional data and large scale learning

3. Some achievements of ML • Programs that can: • Recognize spoken words • Predict recovery rates of pneumonia patients • Detect fraudulent use of credit cards • Drive autonomous vehicles • Play games like backgammon – approaching the human champion!

4. Focus of the module • Understanding the fundamental principles • Types of ML tasks • General algorithms and how they work • Which method is good for what and why • What ML methods can and cannot do • Open research problems • This module is NOT a course on teaching to use a particular software package

5. Example: Which word a person is thinking about? FMRI brain activity data: Source: Tom Mitchell's research pages

6. Example: Find a specified object

9. s3 s4 s1 s2 a13 a12 a11 a14 x1 x2 x3 x4