Artificial Intelligence Foundations

Upon completion of this course, students will be able to:

• Define the field of Artificial Intelligence (AI) and its core principles.
• Explain the basics of neural networks and their historical development.
• Differentiate between traditional machine learning and deep learning approaches.
• Understand the architecture and components of neural networks.
• Explore optimization techniques, including gradient descent, regularization, and hyperparameter tuning.
• Implement basic neural network models for supervised learning tasks.
• Comprehend and apply Convolutional Neural Networks (CNNs) for image data analysis.
• Implement Recurrent Neural Networks (RNNs) for sequential data processing.
• Develop autoencoders for unsupervised learning tasks.
• Differentiate between various types of reinforcement learning algorithms.
• Implement reinforcement learning techniques for solving control and decision-making problems.
• Evaluate the strengths and weaknesses of reinforcement learning approaches.
• Implement AI algorithms for real-world applications, such as image classification, speech recognition, and Natural Language Processing (NLP).
• Explore the use of AI in recommender systems, autonomous vehicles, and weather forecasting.
• Analyze the ethical and societal implications of AI applications.
• Investigate emerging AI techniques, including advanced Transformer models.
• Explore AI generative algorithms, such as Generative Adversarial Networks (GANs) and ChatGPT.
• Assess the potential impact of AI advancements on various industries and domains.