Computer Science CS50 Lecture Notes - Andrew Ng, Labeled Data, Unsupervised Learning
25 Feb 2023
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Document Summary
Machine learning is a way to build intelligent systems by automatically learning from data, without being explicitly programmed. Supervised learning is a type of machine learning where the algorithm is trained on labeled data, where the inputs (features) are paired with their corresponding outputs (labels). Unsupervised learning is a type of machine learning where the algorithm is trained on unlabeled data, and the goal is to learn patterns and structure in the data. Reinforcement learning is a type of machine learning where the algorithm learns by interacting with an environment and receiving feedback in the form of rewards or penalties. Overfitting occurs when a model is trained too well on the training data, to the point where it becomes too specific to that data and does not generalize well to new data. Bias is the difference between the expected value of a model"s predictions and the true values.
