Slide 37
Slide 37 text
Summary
Type of Learning Description Example Algorithms Applications
Supervised Learning
Trained on labeled data,
learns a mapping from
inputs to outputs.
Linear Regression, Logistic
Regression, Decision Trees
Classification (spam
detection), Regression
(predicting house prices)
Unsupervised Learning
Trained on unlabeled data,
aims to find hidden
patterns in the data.
k-Means, PCA, Hierarchical
Clustering, Autoencoders
Clustering (customer
segmentation), Anomaly
Detection (fraud)
Reinforcement Learning
Agent learns by interacting
with an environment,
receiving
rewards/penalties.
Q-Learning, DQN, Policy
Gradient Methods
Game playing (AlphaGo),
Robotics (robot
navigation), Autonomous
vehicles