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 Artificial Intelligence Professional Master

Master of Artificial Intelligence

The Saudi Data and Artificial Intelligence Authority was established by Royal decree in August 2019, to become the main government entity to facilitate and enable the adoption of AI in the Kingdom, particularly in relation to achieving the Vision 2030 goals related to building a future based on AI and innovation.

About the Program

In this Master of Artificial Intelligence and Machine Learning degree program, students learn to apply creative thinking, algorithmic design, and coding skills to build modern AI systems. The program provides breadth coverage of the different paradigms within the AI area.  It is noteworthy though that among all AI paradigms, advancements made in ML paradigm and related disciplines will soon touch every piece of technology. Accordingly, the proposed program provides depth coverage of ML techniques, models, and applications. The program combines rigorous AI/ML curriculum with real-world market niches and experiences.

Program Objectives

The program Educational Objectives are to produce graduates who:
  • PEO1. Are skillful enough professionals necessary to enhance the drive toward innovation and digital transformation via the application of different paradigms of artificial intelligence in Saudi Arabia.
  • PEO2. Have the ability to develop more efficient solutions to real world problems via the application of Artificial Intelligence.
  • PEO3. Identify new opportunities utilizing innovative Artificial Intelligence in general and Machine Learning in specific in improving the quality of life of the citizens.
  • PEO4. Appreciate and enforce the ethics and impact on society when applying Artificial Intelligence.
  • PEO5. Maintain currency in aspects and applications of Artificial Intelligence through self-learning or other professional development.

Why Should You Apply?

  • This program establishes the theoretical and practical foundations necessary to be at the forefront of progress in the next technological revolution, already manifested in Industry 4.0.
  • The program is designed in line with the best practices of prominent universities offering similar programs. 
  • The program is unique in the sense that it provides a descent eye-opener coverage of the various AI paradigms and focused in-depth coverage of ML.
  • The program aims at equipping its graduates with critical practical and technological skills along with solid mathematical and analytics skills to support design and development of AI/ML applications in various domains.

Admission Requirements

The formal requirement for admission is a Bachelor's degree granted by a recognized university in the field of Computer sciences or related disciplines. Have taken statistics or linear algebra and have prior programming experience. The applicants should have a GPA of at least 2.5 out of 4. IELTS 6 and submit 2 recommendation letters.

Degree Plan

Course  Title LT LB CR
First Semester ​ ​    
ICS 501
Foundations of Artificial Intelligence 3 0 3
ICS 502 Machine Learning 3 0 3
MATH 503
Mathematics for Data Science 3 0 3
MATH 506
Fundamentals of Data Science 3 0 3
   12 0 12
Second Semester ​ ​    
ICS 503
Evolutionary Computation and Global Optimization 3 0 3
ICS 504
Deep Learning 3 0 3
ICS 505
Computer Vision 3 0 3
ICS 619
Project 0 0 IP
  9 0 9
Summer ​ ​    
ICS 506
Natural Language Processing 3 0 3
ICS 619
Project 0 0 6
   3 0 9
  Total Credit Hours    30


Courses Flow Chart

Courses description

ICS 501: Foundations of Artificial Intelligence (3-0-3)
Fundamental concepts and techniques of intelligent systems.  Principles and methods for heuristic search, knowledge representation, problem solving, planning and reasoning with uncertainty, game and adversarial search and their application to building intelligent systems in a variety of domains. Basics of machine learning, visual perception and natural language processing.  Introduction to AI programming.
Pre-requisites:  Graduate Standing.
Note:  Cannot be taken for credit with ICS-381.

ICS 502: Machine Learning  (3-0-3)
Introduction to machine learning; supervised learning (linear regression, logistic regression, classification, support vector machines, kernel methods, decision tree, Bayesian methods, ensemble learning, neural networks); unsupervised learning (clustering, EM, mixture models, kernel methods, dimensionality reduction); learning theory (bias/variance tradeoffs); and reinforcement learning and adaptive control. 
Pre-requisites:  Graduate Standing.
Note:  Cannot be taken for credit with ICS-485.

ICS 503: Evolutionary Computation and Global Optimization (3-0-3)
An introduction to a wide variety of robust optimization algorithms based on the theme of nature inspired optimization techniques. Computational implementation single-state methods such as Simulated Annealing  and Tabu Search ; and population-based methods such as Genetic Algorithms, Particle Swarm, and Ant Colony. Theory including representations, landscapes, epistasis, code bloat, diversity, and problem structure is discussed.  Applications to optimization, machine learning, software development, and others. 
Pre-requisites:  Graduate Standing.
ICS 504: Deep Learning (3-0-3)
Deep Learning models and their applications in real world.  Foundations of deep learning networks training and optimization. Deep learning models for spatial and temporal data processing. Analysis of prominent deep learning models such as Convolutional Neural Networks (CNNs), Recurrent and Recursive Networks, Long-Short Term Memory (LSTM), Residuals Networks, and Generative Adversarial Networks (GANs). One-Shot Learning and Deep Reinforcement Learning. 
Pre-requisites:  ICS-502 or Consent of Instructor.
Note:  Cannot be taken for credit with ICS-471.

ICS 505: Computer Vision (3-0-3)
Taxonomy of computer vision tasks, Applications of computer vision, Image representation in the spatial and frequency domains,  Image formation, Image filtering, Feature detection and matching,  Image segmentation,  Image classification,  Object detection,  Image alignment and stitching, Motion estimation and tracking, Depth estimation,  Deep learning for computer vision.
Pre-requisites:  MATH-503 or Consent of Instructor.
Co-requisites: ICS-504 or Consent of Instructor.
Note:  Cannot be taken for credit with ICS-483.

ICS 506: Natural Language Processing (3-0-3)
Natural language processing (NLP) fundamentals, Language modeling, Vector space semantics and Embeddings, Sequence labelling, Syntactic parsing, semantic analysis, Information Extraction, Machine translation, Discourse Coherence, Question Answering, Dialogue Systems and Chatbots, and Natural language summarization.. 
Pre-requisites:  ICS-504 or Consent of Instructor.
Note:  Cannot be taken for credit with ICS-472.
ICS 619: Project (6-0-6)
A graduate student will arrange with a faculty member to conduct an industrial research project related to the Artificial Intelligence and Machine Learning field of the study. Subsequently the students shall acquire skills and gain experiences in developing and running actual industry-based project. This project culminates in the writing of a technical report, and an oral technical presentation in front of a board of professors and industry experts.
Pre-requisites:  Completion of 12 credit hours.

MATH 503: Mathematics for Data Science (3-0-3)
Selected topics from linear algebra, multivariate calculus, and optimization for Data Science with an emphasis on the implementation using numerical and symbolic software, toolboxes, and libraries for data science like NumPy, SciPy, Pandas, SymPy. Topics include data transformation using linear algebra, vector spaces, linear transformations, matrix representations, matrix decompositions (eigenvectors, LU, QR, SVD, Cholesky); multivariate calculus for continuous, convex, and non-convex optimization methods; time series construction and visualization, Fourier transformations for time series conversion.
Pre-requisites:  Graduate Standing.

MATH 506: Fundamentals of Data Science (3-0-3)
All aspects of the data science pipeline using the software, toolboxes, and libraries like NumPy, SciPy, Pandas, Matplotlib, Seaborn: data acquisition, cleaning, handling missing data, EDA, visualization, feature engineering, modeling, model evaluation, bias-variance tradeoff, sampling, training, testing, experimenting with a classical model; ethics in data science.
Pre-requisites:  Graduate Standing.