Artificial Intelligence and Machine Learning

Course Description:

The Artificial Intelligence and Machine Learning Fundamentals course provides a comprehensive introduction to the exciting field of AI and ML. This course aims to equip students with a solid understanding of the core principles, techniques, and applications of AI and machine learning, enabling them to leverage these technologies effectively in various domains.

Course Objectives:

1. Introduction to Artificial Intelligence: Gain a clear understanding of the fundamental concepts, goals, and applications of artificial intelligence, exploring its history and current advancements.

2. Machine Learning Basics: Learn the foundations of machine learning, including supervised and unsupervised learning, feature extraction, model evaluation, and the use of training and test datasets.

3. Regression and Classification: Explore regression and classification algorithms, such as linear regression, logistic regression, decision trees, and support vector machines, and understand their applications in predictive modelling.

4. Clustering and Dimensionality Reduction: Study clustering algorithms, including K-means, hierarchical clustering, and density-based clustering, and grasp the concepts of dimensionality reduction techniques like principal component analysis (PCA).

5. Neural Networks and Deep Learning: Delve into neural networks, deep learning architectures, and their applications, including feedforward networks, convolutional neural networks (CNNs), and recurrent neural networks (RNNs).

6. Natural Language Processing: Understand the principles of natural language processing (NLP) and explore techniques such as text preprocessing, sentiment analysis, named entity recognition, and text generation.

7. Reinforcement Learning: Introduce the concepts of reinforcement learning, Markov decision processes, and explore algorithms such as Q-learning and deep Q-networks (DQNs) for training intelligent agents.

8. Model Evaluation and Performance Metrics: Learn how to evaluate the performance of machine learning models using metrics such as accuracy, precision, recall, F1-score, and understand techniques like cross-validation and overfitting.

9. Ethics and Bias in AI: Discuss the ethical considerations, fairness, and bias issues associated with AI and machine learning applications, emphasising the importance of responsible AI development and deployment.

10. Real-world Applications and Case Studies: Explore practical applications of AI and machine learning across diverse domains, including healthcare, finance, image recognition, recommendation systems, and autonomous vehicles, through case studies and real-world examples.

Course Format:

The course combines theoretical lectures, hands-on programming exercises, coding assignments, and projects to reinforce the concepts learned. Students will have the opportunity to apply their knowledge using popular machine learning libraries and frameworks. Additionally, guest lectures by industry experts and interactive discussions will provide insights into the latest trends and emerging applications in AI and machine learning.

Format: On-site and online

Language(s): English and Arabic 

Duration: One Week

Certificate of Completion: Upon successful completion of the programme, participants will receive an Al-Majd Pathways Centre (APC) Certificate of Completion.