Course Description: Machine learning is at the heart of many advances and new applications in science and technology: from self-driving cars, machine translation, speech recognition, recommender systems, to understanding the human genome. This course provides a broad introduction to machine learning, data mining, and statistical pattern recognition.
The course will focus not only on the theoretical underpinnings of learning techniques, but also on the practical know-how needed to effectively apply these techniques to new problems. The course will also draw from numerous case studies and applications, so that students also learn how to apply learning algorithms to computer vision, text processing, medical informatics, audio, database mining, and other areas.
- Lecturer: Professor Qi (Rose) Yu
- TA: Celeste Hollenbeck (firstname.lastname@example.org), Berk Can Gurel (email@example.com)
- Piazza: https://piazza.com/class/jln5galdaes3y6
- Office Hours: Tuesdays 4:30 pm - 5:30 pm 362 WVH, Friday 1:00 - 2:00 pm ISEC 605
- Home work: 10% x 4
- Kaggle mini-project: 20%
- project: 40%
Note: the syllabus is tentative and is subject to change.