ML 1.7 Some canonical learning problems

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 v  Some canonical learning problems: -

Ø In machine learning, canonical learning problems are standard tasks that serve for developing and evaluating algorithms.

Ø These canonical problems provide a framework for understanding and applying machine learning techniques across various domains.

Ø These problems help in understanding the strengths and limitations of various approaches. Some of the most common canonical learning problems include:

 

1.     Classification: Assigning inputs to predefined categories. For example, determining whether an email is spam or not.

 

Binary Classification: trying to predict a simple yes/no response. For instance, predict whether a user review of the newest Apple product is positive or negative about the product.

 

Multiclass Classification: trying to put an example into one of a number of classes. For instance, predict whether a news story is about entertainment, sports, politics, religion, etc. Or predict whether a CS course is Systems, Theory, AI or Other.

 

Ranking: trying to put a set of objects in order of relevance. For instance, predicting what order to put web pages in, in response to a user query.

 

2.     Regression: trying to predict a real value. For instance, predict the value of a stock tomorrow given its past performance. Predicting continuous numerical values based on input data. An example is forecasting housing prices based on features like location and size.

 

3.     Clustering: Grouping similar data points together without predefined labels. This is often used in customer segmentation to identify distinct user groups based on purchasing behaviour.

 

4.     Dimensionality Reduction: Reducing the number of variables under consideration to simplify models while retaining essential information. Techniques like Principal Component Analysis (PCA) are commonly used for this purpose.

 

5.     Anomaly Detection: Identifying data points that deviate significantly from the norm. This is crucial in applications like fraud detection, where unusual patterns may indicate fraudulent activity.

 

6.     Reinforcement Learning: Training agents to make sequences of decisions by rewarding desired behaviours. This approach is used in game-playing AI and robotics.

 

7.     Recommendation Systems: Suggesting items to users based on their preferences and behaviours. Examples include recommending movies on streaming platforms or products in online stores.

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