Curriculum - Master AI
This module delves into the essential mathematical concepts used in machine learning. Topics covered include matrix operations, eigenvalues, eigenvectors, PCA, SVD, multivariate calculus, statistics, convex optimization, and estimation theory, providing a solid mathematical base for further studies in machine learning.
Students learn about different methods and systems for representing and reasoning with knowledge. The curriculum includes semantic networks, automated reasoning techniques like forward and backward chaining, ontologies, and applications in the Semantic Web, aiming to develop capabilities in designing knowledge-based systems.
This module introduces the basics of relational databases, including SQL and Python programming for data manipulation and queries. Students learn to design databases using the Entity Relationship Model and to implement and manipulate these using SQL, providing a foundation for complex data systems.
Covering the fundamentals of IoT, this module focuses on the integration and function of sensors, actuators, microcontrollers, and cloud communications. Practical applications include configuring IoT devices and gateways and understanding IoT security and network protocols.
This module provides an introduction to the methodologies of academic research, including formulating research questions, designing studies, and ethical considerations. Students learn about different research paradigms, data collection techniques, and the essentials of writing and presenting research findings.
Aimed at enhancing language skills in French and English, this module covers grammar, vocabulary, and pronunciation, along with cultural aspects to improve communication skills in professional and academic settings.
Focusing on personal development, this module covers self-perception, conflict management, adaptability, and professional interaction skills, with the goal of preparing students for effective interpersonal communications and problem-solving in their careers.
This comprehensive course on machine learning techniques covers predictive modeling, machine learning algorithms, model evaluation, and optimization methods. Practical aspects include implementing algorithms in Python and using libraries like scikit-learn and PyTorch.
This module provides an integrated approach to learning about NLP and computer vision. Topics include text processing, machine learning applications to text and image data, and the use of Python libraries for implementing algorithms that handle language and visual data.
An advanced look at non-relational databases and web development, this module covers NoSQL databases, Flask framework for web applications, and techniques for integrating databases with web services.
This module focuses on methods for extracting meaningful information from large datasets and networks. Techniques covered include itemset mining, clustering, and network analysis algorithms, with practical applications using software like Neo4J.
This module covers the design and control of autonomous robotic systems, including sensors, actuators, and algorithms for navigation, perception, and decision-making, emphasizing practical applications and programming of robots.
This module continues to build on the language skills developed in the first semester. Students are expected to refine their grammatical accuracy, expand their vocabulary, and improve their ability to argue and persuade effectively in both languages.
Students explore how to start and manage AI-driven businesses, including understanding market needs, developing AI products, and strategic planning for launching and scaling tech startups.
Students learn about the core concepts of reinforcement learning, including policy and value function methods, Monte Carlo simulations, and temporal-difference learning, with applications in game playing, robotics, and optimization problems.
This module advances knowledge in NLP, covering neural language models, transformers, speech recognition, and sophisticated methods for text analysis and processing, emphasizing practical implementation using modern AI frameworks.
Expanding on basic computer vision concepts, this module explores advanced techniques in object detection, image segmentation, and generative models, with applications in areas such as autonomous driving and medical imaging.
Covering advanced topics in machine learning applications on graph-structured data, this module includes learning about graph neural networks, knowledge graphs, and their applications in technology and science.
In this module, students engage in a project-based application of AI, integrating knowledge from various AI subfields to develop solutions to real-world problems, focusing on innovation and cross-disciplinary collaboration.
This semester's focus shifts towards more specialized applications of language skills. This module aims to prepare students for high-level communication challenges they will face in multinational companies or global research collaborations.
This module explores Moroccan culture and arts, including historical and contemporary practices in visual and performing arts, aiming to foster an appreciation and understanding of cultural heritage.
Preparing students for the workforce, this module covers job search techniques, resume and cover letter writing, interview preparation, and understanding of the professional environment, with a focus on aligning personal qualifications with market needs.
This capstone project module is designed to integrate and apply the broad AI knowledge and skills acquired throughout the program. Students undertake a project in a research or development setting, often within a company or academic laboratory. The focus is on developing a scientific approach to addressing a complex problem in the field of AI, culminating in a written thesis and a presentation to an academic and professional jury. The experience is intended to foster critical and analytical thinking, potentially leading to a publication or forming the basis for further postgraduate research.
This module serves as a transition from academic learning to professional application, emphasizing project management, teamwork, and communication skills, essential for successful career advancement in the AI industry.