Artificial Intelligence — definition?
Simulation of human intelligence by machines.
Course objectives — overview?
Introduces AI, its structure, and development history.
Basic AI principles — key?
Reasoning, knowledge representation, planning, learning, NLP, perception, robotics.
Deep learning — techniques?
Neural networks, especially deep and convolutional types.
AI applications — examples?
Healthcare, autonomous vehicles, NLP, daily tech.
Ethics in AI — main issues?
Bias, transparency, accountability, privacy.
Supervised learning — data type?
Labeled data for predicting outputs.
Unsupervised learning — focus?
Finding patterns in unlabeled data.
Reinforcement learning — mechanism?
Learning via rewards in an environment.
Neural networks — inspiration?
Inspired by human brain's neuron connections.
Convolutional neural networks — use?
Image processing and spatial feature recognition.
Backpropagation — purpose?
Training neural networks by error correction.
Testez vos connaissances avec un QCM de 6 questions sur Fundamentals of Artificial Intelligence.
1. How does machine learning relate to the broader field of artificial intelligence introduced in the course?
2. Which of the following best illustrates a cause-and-effect relationship in the basic principles of AI?
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