ML Flashcards
What is Machine Learning?!
Machine learns from data …
Machine Learning: Definition
- ML is about enabling a machine to act, without explicitly programming it!
- It is predicting unknown from uncertain information
- Self-configuring data structures that allow a computer to do things that would be called “intelligent” if a human did it ….
- “making computers behave like they do in the movies”
Data is recorded from some real-world phenomenon. What might we want to do with that data?
Prediction - what can we predict about this phenomenon?
Description - how can we describe/understand this phenomenon in a new way?
Learning from Data: What is it Good for?
Right: Write code to make the computer learn how to do the tasks
Wrong: Write code to explicitly do the above tasks
Many applications are immensely hard to program directly. These almost always turn out to be “pattern recognition” tasks.
Right: 1. Program the computer to be able to learn from examples. 2. Provide “training” data.
Wrong: 1. Program the computer to do the pattern recognition task directly.
Steps Involved in Software Engineering Methodology
• Analyze
– Interview experts, users, etc. to determine the actions the system must perform
• Design
– Apply Computer Science knowledge to design a solution
• Implement
• Test
Challenges for Software Engineering Methodology
• Standard SE methods fail when…
– System requirements are hard to collect
– The system must resolve difficult tradeoffs
Why System Requirements Can be Hard to Collect …
• There are no human experts – Cellular telephone fraud • Human experts are inarticulate – Handwriting recognition • The requirements are changing rapidly – Computer intrusion detection • Each user has different requirements – E-mail filtering
Machine Learning:
Replacing Guesswork with Data
• In all of these cases, the standard SE methodology requires engineers to make guesses
• Machine Learning provides a way of making these decisions based on data, instead of guesswork
Machine Learning vs. Artificial Intelligence
Humans can think, learn, see, understand language, reason, etc.
• Artificial Intelligence aims to reproduce these capabilities.
• Machine Learning is one part of Artificial Intelligence.
• Machine learning aims to reproduce learning capabilities
Using machine learning to detect spam emails.
Naïve Bayes
Rule mining
Using machine learning to recommend books.
Collaborative Filtering
Nearest Neighbour
Clustering
Using machine learning to identify faces and expressions.
Decision Trees
Adaboost
Using machine learning to identify vocal patterns
Feature Extraction
Probabilistic Classifiers
Support Vector Machines
ML for working with social network data:
detecting fraud, predicting click-thru patterns, targeted advertising, etc etc etc .
ALGORITHMS
Support Vector Machines
Collaborative filtering
Rule mining algorithms