Foundations of Artificial Intelligence: Principles and Techniques

Preference Dates Timing Location Registration Fees
Instructor-Led Training

(In-Person and Live Webinars)
10, 13, 17, 20, 24, 27 July 2023 Mondays & Thursdays: 7:00PM - 9:30PM Dubai Knowledge Park 1575 USD

Course Description

Foundations of Artificial Intelligence (AI): Principles and Techniques is a comprehensive course that aims to provide a robust foundation in artificial intelligence (AI), machine learning (ML), and deep learning principles. Designed to empower students with the essential skills and knowledge necessary to thrive in the rapidly progressing field of AI, this course delves into a diverse array of topics such as search algorithms, knowledge representation, logic, probability, optimization, ML, and natural language processing.

Throughout the course, delegates will gain practical experience in creating AI solutions using Python, enhancing their understanding of AI concepts and ability to address real-world problems. By the end, they will have a thorough grasp of AI principles and techniques, preparing them for success in the ever-evolving AI landscape.

Learning Objectives


  1. Understand the fundamental concepts of artificial intelligence, machine learning, and deep learning.
  2. Acquire proficiency in Python programming language and its application in AI development.
  3. Develop problem-solving skills using search algorithms, including uninformed search, informed search, and constraint satisfaction problems.
  4. Gain knowledge of knowledge representation techniques, such as graphs, trees, and networks.
  5. Master logical reasoning concepts, including propositional logic, first-order logic, and inference in AI systems.
  6. Learn the principles of probability theory and its application in AI, including Bayesian networks and hidden Markov models.
  7. Apply optimization and decision-making techniques, such as linear programming, decision trees, and game theory.
  8. Understand the fundamentals of machine learning, including supervised learning, unsupervised learning, and reinforcement learning.
  9. Explore neural networks and deep learning techniques, such as artificial neural networks, convolutional neural networks, and recurrent neural networks.
  10. Gain knowledge in natural language processing, including text preprocessing, sentiment analysis, and machine translation.
  11. Develop practical AI solutions by implementing various AI algorithms and techniques using Python.
  12. Understand the real-world applications, ethical considerations, and future trends in artificial intelligence research.

Throughout every module of this comprehensive course, participants will engage in hands-on Python assignments, acquiring essential practical experience in AI. By successfully completing the course and addressing various real-world projects using Python, delegates will showcase their proficiency and expertise in Artificial Intelligence. Consequently, they will be granted an esteemed Certificate in Artificial Intelligence, accredited by the Dubai Government, acknowledging their achievements and equipping them for a thriving career in the ever-advancing domain of AI.

Unit 1 -Introduction to Artificial Intelligence and Python

  • Overview of artificial intelligence and its history
  • Review of Python programming language
  • Python libraries and tools used in AI development

Module 2: Search Algorithms and Knowledge Representation

  • Depth-first search, breadth-first search and uniform cost search
  • Informed search: A* search and its applications
  • Representing knowledge using graphs, trees, and networks
  • Constraint satisfaction problems

Module 3: Logic and Reasoning

  • Propositional logic and its applications in AI
  • First-order logic and resolution
  • Inference and deduction in AI systems

Module 4: Probability and Uncertainty

  • Introduction to probability theory
  • Bayesian networks and reasoning under uncertainty
  • Hidden Markov models and their applications

Module 5: Optimization and Decision Making

  • Linear programming and optimization techniques
  • Decision-making algorithms and decision trees
  • Game theory and adversarial search

Module 6: Machine Learning Fundamentals

  • Supervised learning: regression and classification techniques
  • Unsupervised learning: clustering and dimensionality reduction
  • Reinforcement learning and its applications

Module 7: Neural Networks and Deep Learning

  • Introduction to artificial neural networks
  • Convolutional neural networks and their applications in computer vision
  • Recurrent neural networks and their applications in natural language processing

Module 8: Natural Language Processing

  • Text preprocessing and tokenization
  • Sentiment analysis and text classification
  • Machine translation and sequence-to-sequence models

Module 9: Applications and Future of AI

  • Real-world applications of AI in various industries
  • Ethical considerations and AI’s impact on society
  • Future trends and challenges in AI research

Upon successful completion of this course, students will be equipped with the necessary skills and knowledge to:

  1. Pursue advanced studies: Graduates will have a solid foundation in artificial intelligence and intelligent agents, which will enable them to pursue higher-level courses or degrees in AI, machine learning, robotics, or related fields.

  2. Develop AI-driven solutions: With a strong understanding of AI techniques, graduates will be able to design, implement, and optimize AI-driven solutions for a wide range of industries, including healthcare, finance, retail, manufacturing, and transportation.

  3. Collaborate effectively in multidisciplinary teams: Equipped with AI expertise, graduates will be able to work effectively in teams consisting of data scientists, engineers, and domain experts, helping to bridge the gap between AI theory and real-world applications.

  4. Stay current with AI advancements: Graduates will have the necessary foundation to keep up with the rapidly evolving field of AI, adapting to new techniques and technologies as they emerge.

  5. Pursue AI-related careers: Graduates will be well-prepared for various AI-related roles, such as AI engineer, machine learning engineer, data scientist, AI researcher, or AI product manager, in both established companies and innovative startups.

  6. Contribute to AI research: With a strong understanding of AI principles and techniques, graduates may choose to contribute to the ongoing research in artificial intelligence, working on cutting-edge projects and pushing the boundaries of what is possible with AI.

  7. Leverage AI for social good: Graduates will be in a position to apply their AI expertise to address pressing global challenges, such as climate change, poverty, and healthcare, by developing AI-driven solutions that can have a positive impact on society.

  • Familiarity with the Python programming language and programming concepts such as variables, data types, conditionals, loops, functions, and objects.
  • Knowledge of basic mathematics, such as algebra, calculus, and probability.
  • Understanding of fundamental computer science concepts, such as algorithms and data structures.

After completing Fundamentals of Artificial Intelligence course, participants will have gained:

  • An understanding of the fundamentals of artificial intelligence and how it relates to computer science and programming.
  • The ability to use Python programming language and AI libraries, such as NumPy, Pandas, Matplotlib, and Scikit-learn, to implement various AI algorithms and techniques.
  • Knowledge of different AI domains, such as search algorithms, logic and reasoning, probability and uncertainty, optimization and decision making, neural networks and deep learning, and natural language processing.
  • The ability to apply AI techniques and algorithms to solve real-world problems in different fields, such as finance, healthcare, gaming, and social media.
  • An understanding of ethical considerations and the societal impact of AI.
  • A solid foundation for further study and research in artificial intelligence and related fields.