A collection of some of the most important algorithms used in artificial intelligence and machine learning.
📑 Table of Contents
- A* Algorithm
- Backpropagation
- Candidate Elimination
- Expectation-Maximization (EM)
- K-Means Clustering
- ID3 Algorithm
- K-Nearest Neighbors (KNN)
- Locally Weighted Regression (LWR)
- Naive Bayes
⭐ A* Algorithm
🚀 A* is a pathfinding and graph traversal algorithm.
- Finds the shortest path between two points.
- Uses heuristics to improve search efficiency.
🔄 Backpropagation
🧠 Backpropagation is an essential part of neural networks.
- Used to minimize the error by adjusting weights through gradient descent.
- A key concept behind training deep learning models.
🔍 Candidate Elimination
📂 Candidate Elimination finds hypotheses consistent with the training data.
- Works by maintaining a specific boundary and general boundary.
- Ensures that all possible hypotheses are evaluated.
📊 Expectation-Maximization (EM)
📈 EM is used for finding parameters of statistical models with latent variables.
- Alternates between expectation (E-step) and maximization (M-step).
- Often used in clustering and density estimation.
📌 K-Means Clustering
🗂️ K-Means partitions data into K clusters based on similarity.
- Each point belongs to the nearest centroid.
- Ideal for unsupervised learning tasks.
🌲 ID3 Algorithm
🌳 ID3 (Iterative Dichotomiser 3) builds a decision tree using entropy.
- Splits data by choosing the feature with the highest information gain.
- Commonly used in classification problems.
👥 K-Nearest Neighbors (KNN)
🏃 KNN is a simple, instance-based learning algorithm.
- Classifies points based on the majority vote of nearest neighbors.
- Works well for classification and regression tasks.
📐 Locally Weighted Regression (LWR)
📏 LWR performs non-parametric regression by assigning weights locally.
- Fits a regression model to localized subsets of data.
- Useful for modeling non-linear data.
🤔 Naive Bayes
📊 Naive Bayes is a probabilistic classifier based on Bayes’ theorem.
- Assumes independence between features.
- Works well for text classification and spam filtering.
📚 Conclusion
These algorithms form the building blocks of artificial intelligence and machine learning, solving problems ranging from pathfinding to classification and clustering. Mastering them equips us with the tools needed for AI development.