Machine learning is the study and design of algorithms, which enable computers/machines to learn from data. This course is an introduction to machine learning. It provides a broad view of models and algorithms, discusses their methodological foundations, as well as issues of practical implementation, use, and techniques for assessing the performance.
At the end of the course the students will (1) understand and implement common machine learning methods, (2) recognize the problems that are amenable to machine learning, and perform appropriate data analysis, and (3) recognize failure points and threats to validity of the results.
Instructor: Prof. Olga Vitek. Email:o.vitek@northeastern.edu Office hours: Tue and Fri after the class, or by appointment.
Teaching assistants: Mr. Ishan Biswas. Office hours Fridays from 3:00pm-4:00pm on zoom (see canvas), or by appointment Mr. Chengyu Li. Office hours Wednesday 10:00am-11:00am on zoom (see canvas) and Thursday 11:00am-12:00pm Snell library (room number announced every week) Mr. Vivek Rangoju. Office hours Wednesday 10:00am-11:00am on zoom (see canvas) and Thursday 11:00am-12:00pm Snell library (room number announced every week)
Course policies and administration: Syllabus, Canvas. Please use Piazza (as opposed to email) for all communication. Academic integrity policy is strictly enforced.
Required texts: (JW) An Introduction to Statistical Learning. G. James, D. Witten, T. Hastie, R. Tibshirani, Springer 2013. (HT) Elements of Statistical Learning. T. Hastie, R. Tibshirani and J. Friedman, Springer, 2009. (CB) Pattern Recognition and Machine Learning. C. M. Bishop, Springer 2006.
Optional texts: (KM) Probabilistic Machine Learning: An Introduction K. Murphy, MIT Press 2022.