S10

Artificial Intelligence 10. Machine Learning Overview Course V231 Department of Computing Imperial College © Simon Colton

Inductive Reasoning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Machine Learning Tasks ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Potential for Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Performing Machine Learning ,[object Object],[object Object],[object Object],[object Object],[object Object]

Constituents of Learning Problems ,[object Object],[object Object],[object Object],[object Object]

Problem constituents: 1. The Example Set ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Example: Positives and Negatives ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Problem Constituents: 2. Background Concepts ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Problem Constituents: 3. Background Axioms ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Problem Constituents: 4. Errors in the Data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Example (Toy) Problem Michalski Train Spotting ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Another Example: Handwriting Recognition ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Constituents of Methods ,[object Object],[object Object],[object Object]

Method Constituents 1. Representation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Examples of Representations The name is in the title… ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Method Constituents 2. Search ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Method constituents 3. Choosing a hypothesis ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Example method: FIND-S ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Generalisation Method in detail ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Worked Example: Predictive Toxicology ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Positives (toxic) Negatives (non-toxic) ? ? ?

Worked Example: First Round ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Worked Example Possible Solutions ,[object Object],4/7=57% N1,N3 P1,P2,P3 <?,?,o> 9 4/7=57% N1,N2,N3 P1,P2,P3,P4 <c,?,?> 8 4/7=57% N1,N2,N3 P1,P2,P3,P4 <?,c,?> 7 3/7=43% N1,N2,N3 P1,P2,P3 <h,?,?> 6 4/7=57% N1,N2 P1,P3,P4 <?,c,n> 5 6/7=86% P1,P2,P3 <c,?,o> 4 4/7=57% N1,N2 P1,P2,P3 <h,c,?> 3 4/7=57% P1 <c,n,o> 2 3/7=43% N2 P1 <h,c,n> 1 Accuracy Negatives true for Positives true for Solution Hypothesis

Worked Example A good solution ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],C ? O

Assessing Hypotheses ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Predictive Accuracy over the Examples Supplied ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Illustrative Example ,[object Object],[object Object],[object Object],[object Object]

Answers in terms of Predictive Accuracy over Examples ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

The Real Test: ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Training and Test sets ,[object Object],[object Object],[object Object],[object Object],[object Object]

Methodologies for splitting data ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Cross Validation Method ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

Overfitting ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]

S10

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