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Machine Learning - From CourseraTeam

Terms in this set (232)

Well-posed Learning Problem
A computer program is said to learn from (E)xperience E with respect to some (T)ask T and some (P)erformance measure P, if its performance on T, as measured by P, improves with experience E.
Machine Learning
Field of study that gives computers the ability to learn without being explicitly programmed.
Machine Learning broad definition
Field of study that gives computers the ability to learn without being explicitly programmed.
Abstract Essence of ML
Representation + Evaluation + Optimisation
Machine Learning
Learning from experience. It's also called supervised learning, were experience E is the supervision.
Pattern Recognition
Finding patterns without experience. It's also called unsupervised learning.
Unsupervised Learing
- We only have xi values, but no explicit target labels.
- You want to do 'something' with them.
Unsupervised Learning Tasks
- Outlier detection: Is this a 'normal' xi ?
- Data visualization: What does the high-dimensional X look like?
- Association rules: Which xij occur together?
- Latent-factors: What 'parts' are the xi made from?
- Ranking: Which are the most important xi ?
- Clustering: What types of xi are there?
Classification
ML task where T has a discrete set of outcomes. Often classification is binary.
Examples:
• face detection
• smile detection
• spam classification
• hot/cold
Regression
ML task where T has a real-valued outcome on some continuous sub-space
Examples:
• Age estimation
• Stock value prediction
• Temperature prediction
• Energy consumption prediction
Labels
Values that h aims to predict
Example:
• Facial expressions of pain
• Impact of diet on astronauts in space
• Predictions of house prices
Training algorithm
Given a model h with Solution Space S and a training set {X,Y}, a learning algorithm finds the solution that minimizes the cost function J(S)
Features/Attributes
Measurable values of variables that correlate with the label y
Examples:
• Sender domain in spam detection
• Mouth corner location in smile detection
• Temperature in forest fire prediction
• Pixel value in face detection
Cost Function
Squared error cost function. J(S)
Local minima
The smallest value of the function. But it might not be the only one.
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