**Professional Master in Visual Computing**

Visual computing is an emerging field that combines computer graphics, computer vision and virtual reality to advance cutting-edge methodologies for the acquisition, processing, manipulation and rendering of visual content. This multidisciplinary program is designed to provide students with the knowledge and technological skills to understand and develop sophisticated problem solutions in innovative-driven industries such as entertainment, medicine, robotics, criminology and security, Computer Aided Design (CAD) or machine vision.

**Admission requirements**

Admission to the professional master's program in Visual Computing (MxVC) is a competitive process. The application must give evidence that the candidate possesses a potential for strong academic performance. We select the best applicants based on the overall undergraduate GPA and transcript, general GRE (quantitative) score, and the recommendation or reference letters.

The minimum requirements for applicants to MxVC are:

A four-year bachelor's or masters' degree (or equivalent) in Mathematics, Statistics, Computer Science or any related area in Science and Engineering

Minimum Grade-Point Average (GPA): 2.5 on a scale of 4.00 (or equivalent)

Grades of at least B (or equivalent) in most Mathematics and Statistics courses

Minimum GRE score in quantitative section: 156

IELTS score of 6+ or TOEFL of 70+ (waived for KFUPM graduates)

Two recommendation or reference letters

Required preparatory courses include undergraduate courses in calculus, linear algebra, probability and statistics, differential equations, numerical methods, and programming. (See details below for each program)

The admission process goes beyond meeting the minimum requirements.

The list of courses, offered at KFUPM, which are equivalent to the required preparatory undergraduate instruction in calculus, linear algebra, probability and statistics, differential equations, numerical methods, and programming are given below:

- Math 101, Math 102, Math 201, Math 202, Math 225, Math 333, Math 371, ICS 104, and any one of Stat 201, 212, 214, 319

**Degree Plan**

**Two Years Degree Plan of MX-Visual Computing**

Course # | | Title | LT | LB | CR |

Fall Semester | | | | | |

MATH528 | | Mathematics for Visual Computing | 3 | 0 | 3 |

ICS502 | | Machine Learning | 3 | 0 | 3 |

| | 6 | 0 | 6 | |

Spring Semester | | | | | |

ICS504 | | Deep Learning | 3 | 0 | 3 |

ICS505 | | Computer Vision | 3 | 0 | 3 |

| | 6 | 0 | 6 | |

Fall Semester | | | | | |

MATH583 | | Computer Graphics: Modeling and Processing | 3 | 0 | 3 |

ARC580 | | Computer Graphics and Imaging | 3 | 0 | 3 |

MATH 619 | | Project | 0 | 0 | IP |

| | 6 | 0 | 6 | |

Spring Semester | | | | | |

MATH584 | | Computer Graphics: Animation and Simulation | 3 | 0 | 3 |

ICS544 | | Interactive Computer Graphics | 3 | 0 | 3 |

MATH 619 | | Project | 0 | 0 | 6 |

6 | 0 | 12 | |||

Total Credit Hours | | | 30 |

**Course Descriptions**

**ARC 580 Computer Graphics and Imaging (3-0-3) **

Fundamental concepts of light and colors, ray tracing technology for synthetic computer generated images, texture mapping and bump mapping, anti-aliasing, basic lighting and shading models, realistic rendering.

*Prerequisite***: Graduate Standing**

**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.

**Note: Not to be taken for credit with ICS 485**

*Prerequisite:*** 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.

*Prerequisite***: ICS 502 or Consent of Instructor**

**Note: Not to 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.

**Note: Not to be taken for credit with ICS 483**

*Prerequisite:*** MATH 503 or Consent of Instructor**

*Corequisite:*** ICS 504 or Consent of Instructor**

**ICS 544 Interactive Computer Graphics (3-0-3) **

Virtuality, virtual objects, images, worlds, and environments, presence and telepresence, immersive vs non-immersive VR, marker based and marker-less AR, 3D interface design considering cognitive boundaries and limitations, HMD, standalone and mobile integrated, HADs and special displays, AR interfaces, hangable, collaborative, hybrid and multimodal, MR surface approximation, applications of VR and AR in Education, Medicine, Military, Engineering and Accenture, XR application design and development in Unity.

*Prerequisite***: Graduate Standing**

**MATH 528 Mathematics for Visual Computing (3-0-3) **

Discrete and continuous differential geometry of curves and surfaces, geometry processing on meshes, scattered data interpolation and approximation.

*Prerequisites***: Graduate Standing**

**MATH 583 Computer Graphics: Modeling and Processing (3-0-3) **

Central concepts of geometric modeling, basic shape representations (parametric and implicit curves and surfaces, meshes, point clouds), freeform curve and surface design in spline representation, subdivision surfaces, surface quality assessment.

*Prerequisite:*** Graduate Standing**

**MATH 584 Computer Graphics : Animation and Simulation (3-0-3) **

Physically-based simulation methods for modeling shape and motion (rigid bodies, deformable objects, fluids), interactive dynamic animations (representation, dynamics, collisions detection), data driven animation methods.

*Prerequisite***: Graduate Standing**

**MATH 619 Project (0-0-6) **

A graduate student will arrange with a faculty member to conduct an industrial research project related to the visual computing field. 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.

*Prerequisite: ***Graduate Standing**