Sofia University Launches Doctor of Machine Learning
Sofia University’s Doctor of Machine Learning is a 72-quarter-hour online and hybrid doctorate that can be completed in 2.5 to three years.
By Academic Writing Club Newsroom

Sofia University has launched a 72-quarter-hour Doctor of Machine Learning program for professionals with master’s degrees in technical fields. The professional doctorate is available 100% online and in hybrid formats, with completion estimated at approximately three years for students taking two courses each quarter. Students taking three courses per quarter may finish in approximately 2.5 years. The program adds doctoral-level depth to the university’s computer science portfolio.
Coursework And Applied Study
The program covers machine learning mathematics, algorithms, artificial intelligence, data science, engineering, research, and emerging technologies. Its curriculum also examines the ethical, legal, and societal implications of machine learning.
Students will complete a final capstone project and gain experience through a Work Integrated Learning program. Independent study opportunities are also available.
Who The Program Serves
The Doctor of Machine Learning is designed for candidates with a master’s degree in computer science, computer engineering, data science, machine learning, artificial intelligence, information technology, applied mathematics, or relevant engineering disciplines.
Prospective applicants can request information about the Doctor of Machine Learning.
Expanding Computer Science Study
The doctorate builds on Sofia’s Master of Science in Computer Science and graduate certificates, including its Graduate Certificate in AI and Machine Learning. The university states that AI and Machine Learning is its primary and most popular MSCS concentration.
Sofia University is accredited by the WASC Senior College and University Commission. The new degree is intended to prepare practitioners, researchers, educators, engineers, and technology leaders for work applying machine learning across science, technology, business, and engineering.



