Karteikarten: Introduction to AI and Machine Learning — 18 Karten

Artificial Intelligence — definition?

Simulation of human intelligence processes by machines.

History of AI — start?

Mid-20th century with initial optimism and setbacks.

Narrow AI — role?

Designed for specific tasks like voice recognition.

General AI — hypothetical?

Yes, capable of human-like understanding across tasks.

Turing Test — purpose?

Evaluate if a machine's behavior is indistinguishable from human.

Machine Learning — definition?

Systems learning from data to improve performance.

AI vs ML — difference?

AI is broader; ML uses data-driven algorithms within AI.

Supervised Learning — data type?

Labeled data with known outputs.

Unsupervised Learning — data?

Unlabeled data to find patterns.

Deep Learning — key feature?

Uses multi-layer neural networks for complex patterns.

Neural Network — basic unit?

Neuron that processes inputs and passes outputs.

Natural Language Processing — goal?

Enable computers to understand and generate human language.

Computer Vision — task?

Interpret and analyze visual information from images or videos.

AI applications — sectors?

Healthcare, finance, autonomous vehicles, and more.

Reinforcement Learning — mechanism?

Learns via rewards and penalties through trial and error.

Overfitting — problem?

Model learns noise, performs poorly on new data.

Clustering — purpose?

Group data based on similarity without labels.

Activation Function — role?

Introduce non-linearity into neural networks.