Introduction to AI & Machine Learning

1. Artificial Intelligence (AI)

Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think like humans and mimic their actions. The term may also be applied to any machine that exhibits traits associated with a human mind such as learning and problem-solving.

2. Machine Learning (ML)

Machine Learning (ML) is a subset of AI that focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. Instead of explicit programming, ML algorithms identify patterns in data to make predictions.

3. Types of Machine Learning

• Supervised Learning: The machine is trained using well-labeled data.
  Examples: Regression, Classification.
• Unsupervised Learning: The machine deals with unlabeled data.
  Examples: Clustering, Association.
• Reinforcement Learning: The machine interacts with an environment and learns through a system of rewards and punishments.

4. Deep Learning

A subset of machine learning based on artificial neural networks with representation learning. Deep learning architectures such as deep neural networks, deep belief networks, deep reinforcement learning, recurrent neural networks and convolutional neural networks have been applied to fields including computer vision, speech recognition, natural language processing, machine translation, bioinformatics, drug design, medical image analysis, climate science, material inspection and board game programs, where they have produced results comparable to and in some cases surpassing human expert performance.