Introduction to Artificial Intelligence

Introduction

• Why you need to know about artificial intelligence

1. What Is Artificial Intelligence?

• Define general intelligence
• The general problem-solver
• Strong vs. weak AI

2. The Rise of Machine Learning

• Machine learning
• Artificial neural networks

3. Common AI Systems

• Searching for patterns in data
• Robotics
• Natural language processing
• The Internet of Things

4. Learn from Data

• Labeled and unlabeled data
• Massive datasets

5. Identify Patterns

• Classify data
• Cluster data
• Reinforcement learning

6. Machine Learning Algorithms

• Common algorithms
• K-nearest neighbor
• K-means clustering
• Regression
• Naive Bayes

7. Fit the Algorithm

• Select the best algorithm
• Follow the data
• Overfitting and underfitting

8. Artificial Neural Networks

• Build a neural network
• Weighing the connections
• The activation bias

9. Improve Accuracy

• Learning from mistakes
• Step through the network

10. Where to Go from Here

• Using AI systems
• Applying AI to solve problems