Machine Learning, MS

Program Introduction

The Master of Science in Machine Learning program at Carnegie Mellon University (CMU) offers students the opportunity to develop in-depth understanding and expertise in machine learning theory, algorithms, and applications. Operated by the Machine Learning Department, the program comprehensively covers various core areas of machine learning including artificial intelligence, data mining, statistical learning, deep learning, and reinforcement learning. Under the guidance of world-renowned faculty, students learn cutting-edge theories and techniques, developing problem-solving abilities through practical experiences with real data. This program is designated as a STEM (Science, Technology, Engineering, and Mathematics) field, allowing international students to benefit from up to 36 months of Optional Practical Training (OPT) after graduation. Carnegie Mellon's Machine Learning Master's program typically spans 3-4 semesters, with students growing into machine learning specialists through a systematic curriculum from foundational theories to cutting-edge research. The program is designed for students to build strong mathematical foundations in machine learning (probability theory, statistics, optimization) while simultaneously developing practical application skills. Students have opportunities to participate in research projects through collaboration with various research groups in the Machine Learning Department, and can gain extensive knowledge through interdisciplinary collaborations with adjacent fields (computer vision, natural language processing, robotics, computational biology, etc.). Graduates are prepared to work as data scientists, machine learning engineers, research scientists, and more in various environments including technology companies, research institutions, academia, and startups.