Introduction to
Artificial Intelligence
The Introduction to Artificial Intelligence course at our university is a four-stage journey designed to familiarize students with the inner workings of artificial intelligence. Starting with the basics, the course progresses towards more advanced concepts. It culminates in engaging practical projects, allowing students to apply their newfound skills in real-world scenarios.
Application requirements
This course is open exclusively to Saudi university students who are currently pursuing a degree in fields related to science, technology, or engineering. Ideal candidates are those with a strong sense of curiosity and an eagerness to learn.
In addition to being currently enrolled in a relevant degree, students must also meet the following criteria to qualify for the course.
-
- Candidates are required to pay a subscription fee. This fee is mandatory to cover course costs and resources. Details regarding the fee amount and payment process will be provided upon application.
-
- Candidates must complete designated prerequisite courses available on Coursera. These courses are designed to ensure that all participants have the foundational knowledge necessary for the course.
-
- A qualification exam will be administered to assess the candidates’ understanding of key concepts. This exam must be passed to proceed further in the course.
- Participants will undergo assessments at each stage of the course. Only top-qualified students will be invited to advance to the next stage. This ensures a competitive and merit-based progression, aligning with our commitment to academic excellence.
Signing up for the course
Registration for the course will remain open until November 30, 2024. Interested candidates are encouraged to complete their registration before this deadline.
Eligible candidates will be invited to participate in the qualification exam for the first cohort, scheduled for the week of November 2 – 6, 2024. This is a key step in the selection process for the course.
Additionally, eligible candidates will have the opportunity to be invited for the second cohort qualification exam, which is set for the week of December 14 – 17, 2024.
To qualify for the exam, candidates must be actively enrolled in the KAUST Academy Coursera program and must have completed the prerequisite online courses. This ensures that all participants have the necessary foundational knowledge for the course.
Candidates who do not pass the first qualification exam will have the opportunity to attend the second qualification exam. This provides applicants with a second chance to demonstrate their eligibility for the course.
Prerequisite online courses
To qualify for the Introduction to Artificial Intelligence course, candidates must successfully complete two comprehensive prerequisite specializations on Coursera.
-
- Python 3 Programming Specialization
- Mathematics to Machine Learning and Data Specialization
These specializations are composed of a series of separate online courses, each meticulously designed to ensure a comprehensive grasp of their respective subject areas. Such a structured approach ensures a solid foundation for advanced study in artificial intelligence.
These courses must be completed before November 1, 2024, for those aiming to attend the qualification exam for the first cohort. For those targeting the second cohort, the completion deadline is December 12, 2024.
Upon successful completion of these prerequisites, candidates become eligible to take the qualification exam. Those who excel in this exam will be rewarded with exclusive invitations to enroll in the Introduction to Artificial Intelligence course.
Your enrollment in our course also grants access to over 200 online courses, each culminating in a certificate upon successful completion. This extensive learning opportunity is available at a significantly discounted fee of 400 SAR, a special offer exclusively for KAUST Academy students.
Topics covered
by the course
- Basics of Python programming, as well as the ways in which it can be used when it comes to artificial intelligence and machine learning.
- Mathematics for machine learning, including linear algebra, calculus, and optimization techniques.
- Machine learning tools and techniques, including linear regression, logistic regression, decision trees, and support vector machines.
- Introduction to neural networks, deep networks, and deep learning libraries.
- Convolutional neural networks and their applications in computer vision, including image classification, segmentation, and object detection.
- Major branches of artificial intelligence, including natural language processing, reinforcement learning, deep unsupervised learning, and graph neural networks.
- Practical applications of artificial intelligence in various fields, including physical sciences and beyond.
Skills you’ll gain
- Programming in Python
- Linear Algebra
- Machine Learning
- Mathematics for Machine Learning
- Neural Networks
- Deep Learning Libraries
- Large-Scale Deep Learning
- Natural Language Processing
- Convolutional Neural Networks
- Computer Vision
- Project Management
- AI in Sciences
- Innovative Thinking
- Leadership
- Collaboration
What is the course like?
Course stages
Stage 1: Prerequisite Massive Open Online Courses
Duration: 2 to 4 weeks Completion Date: Before November 1, 2024 Method: Self-paced, Online
This initial stage requires students to complete two specialized series of courses focusing on Python programming and foundational mathematics for machine learning. These include essential topics like linear algebra, calculus, and Python programming.
Note that enrollment in the prerequisite Coursera courses does not automatically guarantee participation in the later stages of the course. Progress through the course depends on high performance in assessments. They are strategically integrated into the course and occur between its key stages. Only top-performing students will be invited to advance to the next stage.
Stage 2: Introduction to Artificial Intelligence
Duration: 5 days Start Date: November 17, 2024 Method: Instructor-led, In-person
In this stage, participants delve into machine learning tools such as linear regression, logistic regression, decision trees, and support vector machines. Additionally, an introduction to neural networks, deep networks, and deep learning libraries is provided. Progress to the next stage is contingent upon meeting qualification criteria and assessment performance.
Stage 3: Advanced AI
Duration: 5 days Start Date: To be decided! Method: Instructor-led, In-person
This stage focuses on convolutional neural networks and their application in computer vision tasks like image classification, segmentation, and object detection. Continuation to the next stage is based on student qualifications and assessment performance.
Stage 4: AI Summer School
Duration: 8 weeks Start Date: 26 June, 2025 Method: Instructor-led, In-person
Qualified students will be invited to participate in the prestigious KAUST Academy Summer School, hosted either at KAUST or at top international universities. This stage offers in-depth learning in major branches of artificial intelligence such as natural language processing, reinforcement learning, deep unsupervised learning, and graph neural networks. On-campus accommodation and services are provided.
Prerequisite course agenda
This section provides a detailed agenda, outlining the specific segments of the Coursera specializations to complete and their recommended timelines. The aim is to ensure participants finish these courses in time for the qualification exams. For the first cohort, the target completion date is before November 1, 2024. For the second cohort, the target completion date is before December 12, 2024.
This is a focused course that teaches how to use linear algebra in data contexts. You’ll learn to represent data with vectors and matrices, understand their properties, and perform key operations like dot products and determinants. The course also covers how these concepts apply to machine learning, including the use of eigenvalues and eigenvectors.
A course tailored for understanding and applying calculus in machine learning contexts. It focuses on optimizing various functions frequently used in machine learning, both analytically and approximately, utilizing derivatives and gradients. Additionally, the course emphasizes visual interpretation of function differentiation, enhancing your practical understanding of these mathematical concepts in the realm of machine learning.
This course delves into the uncertainty in predictions of machine learning models, teaching how to describe and quantify this uncertainty. You’ll gain an intuitive understanding of common probability distributions used in machine learning, and apply statistical methods like MLE and MAP to solve problems. The course also covers assessing machine learning model performance using interval estimates and margin of errors.
In this course, you’ll start with the fundamentals of Python 3, learning conditional statements, loops, and data structures such as strings and lists. It’s designed to develop practical programming skills, including creating drawings and enhancing debugging abilities.
This course focuses on Python’s dictionary data structure and user-defined functions. Key concepts like local and global variables, parameter-passing, named functions, and lambda expressions are covered. You’ll also learn to apply Python’s sorting functions and control order with custom functions, culminating in a project involving social media data analysis.
Learn to effectively fetch and process data from Internet services, master list comprehensions for data extraction and processing, and utilize the Python requests module for REST API interaction.
Explore the essentials of classes, instances, and inheritance in Python. This course guides you through class design, automated test writing, method overriding, and creating inherited classes for efficient data representation.
During this project-based course, you’ll learn how to work with APIs and third-party libraries in Python 3. You’ll learn to use the Python imaging library (pillow) for image manipulation, apply py-tesseract for optical character recognition, and use opencv for face detection in images.
Click on the apply now button to sign up!
Professor’s Take
on the Course
“Artificial intelligence isn’t just a buzzword; it’s the cornerstone of our future. The potential for AI to revolutionize industries, drive economic growth, and enhance the quality of life is unparalleled. At the AI Initiative, we are committed to bringing the best research, facilities, and products to the Kingdom. Our collaborations with global leaders and institutions ensure that we remain at the forefront of AI research and development.
Our mission isn’t just about technology; it’s about nurturing local talent to sustain our progress. We are partnering with KAUST Academy in providing programs that are meticulously crafted to educate and empower Saudi students with the skills needed to not only keep up with the rapid advancements in AI but to drive innovation themselves. By fostering a vibrant AI ecosystem, we’re ensuring that the Kingdom remains a frontrunner in the AI race”
Professor Bernard Ghanem, Deputy Director of AI Initiative at KAUST
Frequently Asked Questions
Yes, the course is specifically designed with a focus on practical programming. It covers foundational concepts that are directly applicable to real-world scenarios.
The course is primarily aimed at university students, providing them with tailored content that bridges academic learning and practical application.
The course structure varies across stages. The initial MOOC (Massive Open Online Course) stage is self-paced, allowing flexibility. Stages two to four are instructor-paced and conducted in person, requiring a more structured time commitment.
Yes, participants receive certificates of completion at each stage of the course. This certifies their proficiency in artificial intelligence knowledge and skills.
The enrollment fee gives you the opportunity to qualify for KAUST Academy’s Introduction to Artificial Intelligence course. Additionally, for an exclusive discounted fee of 400 SAR, students can gain access to over 200 online courses offered through Coursera.
Fees can be conveniently paid through Shopify, ensuring secure processing of both credit and debit card transactions. For debit card users, please ensure that online transactions are enabled on your card before proceeding.
No, completion of 200 courses is not required. Candidates need to complete two specific specializations, each consisting of a series of separate courses.
Three significant assessments are strategically integrated into the course, occurring between its key stages. These assessments are designed to evaluate the depth of your understanding and the application of the concepts learned. Only top-performing students will be invited to advance to the next stage. This competitive system ensures that only the most dedicated and capable students move forward in the course.
Do you have any questions?