Simulated Annealing (SA) is a probabilistic technique used for finding an approximate solution to an optimization problem. To find the optimal solution when the search space is large and we search through an enormous number of possible solutions the task can be incredibly difficult, often impossible. Even with today’s modern computing power, there are still often too many possible solutions to consider. Therefore, we often settle for something that’s close enough to optimal solution when we may be not able to find the optimal solution within a certain time.
Machine learning is a subfield of computer science where computers have the ability to learn without being explicitly programmed. List of the machine learning task:
- Supervised: Approximation where the all data is labeled and the algorithms learn to predict the output from the input data (training, cross validation and testing sets). We have two types of supervised problems:
- Regression: When the output variable is a real value, such as “dollars” or “age”.
- Classification: When the output variable is a category, such as “cat” or “dog” or “Tumor benign” and “Tumor malignant”.
Regression vs Classification
- Unsupervised: Description where the all data is unlabeled and the algorithms learn to inherent structure from the input data.
- Clustering: A clustering problem is where you want to discover the inherent groupings in the data, such as grouping customers by purchasing behavior.
- Association: An association rule learning problem is where you want to discover rules that describe large portions of your data, such as people that buy X also tend to buy Y.