Machine Learning

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.

Course Details:

• Lecturer: Professor Qi (Rose) Yu
• TA: Celeste Hollenbeck (hollenbeck.c@husky.neu.edu), Berk Can Gurel (gurel.b@husky.neu.edu)
• 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

Course Assessment:

• Home work: 10% x 4
• Kaggle mini-project: 20%
• project: 40%

Course Syllabus

Additional Reading Material:

• matrix cookbook
• probability review
• entropy and maxent

Homework [Latex Template]

• Homework 1 [Train Data][Test Data]

Final Project [Latex Template]

• Suggested Topic [Topics]
• Project page and final report

References:

• The Elements of Statistical Learning
• Deep Learning Book
• Pattern Recognition and Machine Learning