Artificial Intelligence
⸺ AI is transforming how organizations leverage data for strategic and operational decision-making, creating a growing demand for skilled AI engineers who can apply advanced techniques like deep learning, neural networks, and generative AI to develop cutting-edge solutions. This program combines high-quality online content from world-leading universities (Coursera) with on-campus practical discussions, group projects, and hands-on labs, covering key topics such as machine learning, deep neural networks, transformers, large language models (LLMs), reinforcement learning, and AI agent development with frameworks like PyTorch, TensorFlow, and LangChain. By the end of the program, participants will be equipped to build and fine-tune LLMs, design AI-driven applications, and implement AI-powered decision-making systems, positioning themselves for roles in AI engineering, research, or applied AI development. A solid foundation in programming, preferably in Python, along with a strong grasp of statistics and linear algebra, is recommended to maximize learning outcomes.
Dates :
Starts March, September 6-month program.
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Part 1: Foundations of Machine Learning and Deep Learning
- Mathematical Foundations of Machine Learning.
- Supervised and Unsupervised Learning
- Machine Learning with Python
- Introduction to Deep Learning & Neural Networks
- Building Deep Learning Models with Keras & TensorFlow
- Computer Vision and Image Processing
- Introduction to Neural Networks with PyTorch
- Deep Learning with PyTorch
- AI Capstone Project with Deep Learning
Part 2: Advanced Generative AI and Large Language Models
- Generative AI and LLMs: Architecture and Data Preparation
- Foundational Models for NLP & Language Understanding
- Generative AI Language Modeling with Transformers
- Engineering and Fine-Tuning Generative AI Models
- Advanced Fine-Tuning for LLMs
- AI Agents and Autonomous Systems with RAG & LangChain
- Project: Generative AI Applications with RAG & LangChain
Coursera Instructor(s) :
SAEED AGHABOZORGI
Ph.D., Sr. Data Scientist
Romeo Kienzler
Chief Data Scientist, Course Lead
IBM Watson IoT
Alex Aklson
Ph.D., Data Scientist
Samaya Madhavan
Advisory Software Engineer
Joseph Santarcangelo
Ph.D., Data Scientist at IBM
IBM Developer Skills Network
Aije Egwaikhide
Senior Data Scientist
IBM
JEREMY NILMEIER
Data Scientist and Developer Advocate
IBM Cloud and Cognitive Software
Our Instructor(s) :
Dr Mohamed Ali Sedrine
Dr Mohamed Ali Sedrine holds a PhD in Electronics, Information & Communication Technologies with First Class Honors from Tunisia Polytechnic School, Carthage University in 2022, and a Master’s in Mobile Robotics from Blaise Pascal University, France, in 2014. He held internships in INRA (Institut National de la Recherche Agronomique), France and CEA (Commissariat à l’Energie Atomique), France, before joining the Aeronautical Application Center of the Tunisian Air Force as Head of the IT and Embedded Systems Workshops Department in 2014. He later joined the Tunisian Air Force Academy as Associate professors in 2017. Dr Sedrine’s expertise is in mobile robotics, computer vision and image processing, UAVs, and artificial intelligence. He has several publications in renowned conferences and journals.
Tarek Gasmi
Tarek Gasmi is an Assistant Professor in Computer Science. Tarek’s academic research and industrial expertise encompass Edge and Cloud AI, Computer Vision, MLOps, LLMOps and data analytics. Tarek is recognized as an Nvidia Faculty Ambassador and a Microsoft Certified Trainer. Additionally, Tarek contributes as a Co-Founder and CEO of DataDoIt startup, focusing on the powerful combination of Computer Vision and Generative AI, particularly in the domain of intelligent video analytics.
Pr. Fakhri Karray
Pr. Fakhri Karray is the inaugural co-director of the University of Waterloo Artificial Intelligence Institute and served as the Loblaws Research Chair in Artificial Intelligence in the department of electrical and computer engineering at the University of Waterloo, Canada. He is also Professor of Machine Learning and held the position of Provost at the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), a research-oriented artificial intelligence (AI) graduate institution in Abu Dhabi, UAE. Fakhri’s research focuses on operational and generative AI, cognitive machines, natural human-machine interaction, and autonomous and intelligent systems, with applications to virtual care systems, cognitive and self-aware devices, and predictive analytics in supply chain management and intelligent transportation systems. He holds editorial roles in major publications related to intelligent systems and information fusion. Fakhri’s latest textbook, “Elements of Dimensionality Reduction and Manifold Learning,” was published by Springer Nature in early 2023. In 2021, he was honored by the IEEE Vehicular Technology Society (VTS) with the IEEE VTS Best Land Transportation Paper Award for his pioneering research on enhancing traffic flow prediction using deep learning and AI. Furthermore, his research on federated learning in communication systems earned him and his co-authors the 2022 IEEE Communication Society’s MeditCom Conference Best Paper Award. He holds fellowship status in the IEEE, the Canadian Academy of Engineering, and the Engineering Institute of Canada. Additionally, he has served as a Distinguished Lecturer for the IEEE and is a Fellow of the Kavli Frontiers of Science. Fakhri earned his Ph.D. from the University of Illinois Urbana-Champaign, USA.
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