Doctor of Philosophy in Artificial Intelligence
Program Introduction
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Duration of Study
The normal duration of this program is 3 years,and the maximum duration is 6 years.
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Teaching Approach
Face-to-face Teaching
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Teaching Language
Chinese/English
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Academic Field
Artificial Intelligence
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Course Introduction
The purpose of this program is to cultivate high-level talents with a broad and solid theoretical foundation, systematic and in-depth professional knowledge, rigorous work style and excellent professional ethics, and the ability to independently engage in scientific research work, to be competent for scientific research, teaching and technical management in this discipline and related fields.
Study Plan
Admission Requirements
Master's degree holders in Computer Science and Technology, Software Engineering, Control Science and Engineering, Information and Communications Technology, Electronic Science and Technology, Statistics, Mathematics, Medicine or related fields.
(Applicants must submit proof of English proficiency)
Research Area
Computer Vision, Multimedia Retrieval, Intelligent Medical Diagnosis, Intelligent Analysis in Chinese Medicines
Course Structure
Table 1: Core Courses (10 Credits)
Course Code | Course Title | Credit |
DIAZ11 | Literature Survey and Thesis Planning | 2 |
DIAZ12 | Academic Activities | 2 |
DIAZ01 | Artificial Intelligence Principles | 3 |
DIAZ02 | Machine Learning | 3 |
Table 2: Elective Courses (University has the ultimate right to cancel the course with insufficient number of students, 6 Credits)
Course Code | Course Title | Credit |
DIAE01 | Deep Learning | 3 |
DIAE02 | Computer Vision | 3 |
DIAE03 | Natural Language Processing | 3 |
DIAE04 | Algorithm and Computational Complexity | 3 |
DIAE05 | Digital Image Processing | 3 |
DIAE06 | Special Topics in Artificial Intelligence | 3 |
Table 3: Dissertation (18 Credits)
Course Title | Type | Credit | |
DIAZ13 | Dissertation | Compulsory | 18 |
Table 4: Recommend Top International Conferences List
Full name of international conference | Short Name |
Neural Information Processing Systems Conference | NIPS |
International Conference on Machine Learning | ICML |
IEEE Conference on Computer Vision and Pattern Recognition | CVPR |
IEEE International Conference on Computer Vision | ICCV |
International Conference on Learning Representation | ICLR |
International Conference on Computer Graphics and Interactive Techniques | ACM SIGGRAPH |
International ACM SIGIR Conference on Research and Development in Information Retrieval | ACM SIGIR |
AAAI Conference on Artificial Intelligence | AAAI |
ACM International Conference on Multimedia | ACM MM |
International Joint Conference on Artificial Intelligence | IJCAI |
Course Description
Compulsory Courses
Literature Review and Thesis Proposal (2 credits)
This course aims to let students learn the latest research development in Artificial intelligence, to help students to know the unsolved problems and possible solutions in this field through literature survey, to lead students to choose suitable research directions so as to complete the selection of PhD research topics.
Academic activities (2 credits)
This course aims to extend the horizon of students, enable students to learn the latest developments in AI field, through academic seminar/exchange activities and other face-to-face form with academicians.
Artificial Intelligence Principle (3 credits)
Artificial intelligence is the study of how to realize human intelligence by using computer software and hardware to perceive and affect the external environment. Based on the systematical discourse about the subject of artificial intelligence and its research system, this course aims to introduce the artificial intelligence principle that is divided into four parts: solution, planning, learning and reasoning as follows.
1) Solution of search problem, optimization problem, game problem and constraint problem, etc.
2) Planning of space-time and decision theory.
3) Machine learning from the perspectives of task, paradigm and model.
4) Knowledge representation and reasoning mechanism.
Machine Learning (3 credits)
This course provides an extensive introduction to machine learning, data mining, and statistical pattern recognition. The topics include:
1) Supervised learning (parametric / non-parametric algorithm, support vector machine, kernel function, and neural network).
2) Unsupervised learning (clustering, dimensionality reduction, recommendation system, deep learning recommendation).
3) Best practices in machine learning (deviation / variance theory, innovation process in machine learning and artificial intelligence)
Elective Courses
Deep Learning (3 credits)
This course systematically introduces the basic principles of deep learning, including startup function, back propagation, convolution, pooling, dropout, and describes representative deep network models, such as convolutional neural network, recurrent neural network, and generative adversarial network, etc. This course also introduces some representative application examples of deep learning.
Computer vision (3 credits)
This course systematically describes the theoretical basis and framework in computer vision, manual feature extraction and feature learning methods, object recognition and classification, action detection and localization, and content understanding and prediction, etc.
Natural language processing (3 credits)
The main contents of this course includes: 1) Mathematical foundation of natural language processing, n-element grammar model, and Hilkov model, etc. 2) Linguistic basis of natural language processing, word collocation, semantic disambiguation, and probability syntactic analysis, etc. 3) Applications of natural language processing, such as text classification and clustering, information retrieval, machine translation, information mining, etc.
Algorithm and computational complexity (3 credits)
By introducing several algorithms, this course aims to discuss the basic requirements of algorithm design, and to analyze the computational complexity. The content includes: combinatorial-progressive relation and generating function, dynamic programming, priority strategy, divide-and-conquer strategy, search technique, parallel algorithm, ranking and searching, and NP theory, etc.
Digital Image Processing (3 credits)
This course aims to introduce the fundamental principle, methods and applications in digital image processing and pater recognition. The main content includes the pre-process, characteristics extraction and analysis, statistics mode recognition, structural mode recognition and their applications in different fields. Students are supposed to read related papers and write reports.
Selected topics in Artificial Intelligence (3 credits)
The course aims to extend the horizon of students, enable students to learn the latest developments, applications and developing trend of related industry in AI field.
Degree Requirements
1.During the first two semesters, students are required to complete two courses " Artificial Intelligence Principles " and " Machine Learning " in Table 1 for a total of 6 credits. 2.During the first two semesters, students are required to take 2 elective courses in Table 2, for a total of 6 credits, according to their research interests and professional background. 3.During the first two years, students are required to participate in at least 30 hours of “Academic Activities” in Table 1, for a total of 2 credits. 4.During the second year, students are required to complete the “Literature Review and Thesis Proposal” in Table 1, for a total of 2 credits. 5.After completing required courses, students can start writing the thesis proposal. Students can continue their dissertation research and writing upon completion of thesis proposal defense. 6.Generally, to be qualified as a doctorate candidate, a student is required to publish at least two academic papers (2 SCI, or 1 SCI with 1 top international conference (Table 4) paper) by the first author(Macau University of Science and Technology as the first unit), before applying for your doctoral dissertation defense. 7.The dissertation should pass the assessment and be defended successfully.
Learning Time
1.In general. The duration for dissertation composition shall be within 24 months, and the writing time shall not be less than 12 months. 2.The classes will be generally arranged in the evening from Monday to Friday, or Saturday.
Qualifications of Graduation
Upon approval from the Senate of the University, the Doctor of Philosophy in Artificial Intelligence Degree will be conferred on a student when he or she has: 1.Completed and met the requirements prescribed in the study plan of his or her program within the specific study period, and achieved a cumulative GPA of 2.50 or above (excluding dissertation). 2.Abided by the regulations of the University. 3.Cleared all fees and charges and returned all University’s property and equipment borrowed. Note: All curriculums and study plans are based on the newest announcement of the Boletim Oficial da Região Administrativa Especial de Macau. ※ In case of any discrepancy, the Chinese version shall prevail.
