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Summary

Summary Machine Learning
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  • Course
  • Introduction Of Artificial Intelligence (822184B6)
  • Institution
  • Tilburg University (UVT)

Summary of the topic 'Machine Learning' covered in this course, extensively explained including all important terminology. Recommand to buy the bundel to get a full overview of all topics from the course. Passed the course with an 8,5 using the bundel with summaries!

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Document information

  • December 14, 2020
  • 7
  • 2019/2020
  • Summary

Subjects

  • machine learning
  • prediciting
  • fitting

Written for

  • Tilburg University (UVT)
  • Cognitive Science And Artificial Intelligence
  • Introduction Of Artificial Intelligence (822184B6)
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Machine Learning

How can we get machines to learn from observations?
We want a machine to recognize write digits, also with low pixels

Different kinds of tasks:
Classification:
Assigning data to a class;
- Linear
- Non-linear
- Complex
- Multiple categories
Signal classification:
 Forecasting = learning from observations; getting some rules that can be
used in the future to predict the next outcomes (for example in finance).
Predicting the next outcomes of a time series
 Function approximation = determining the value of a point
- Identify species (such as birds or insects)
- Identify abnormalities (such as irregular heart rate)
Unsupervised clustering:
Find unknown clusters in data
- Species assemblages
- Protein structure
- Clustering your customers

Learning from observations:

Example 1 – predicting the water
- Regression problem  output is real/continuous (salary/weight)
- Learning to estimate a numeric output  predicting the tempearture
- Problem of overfitting  applies to every machine learning problem

Observation = series of k observations (examples/instances/cases), and observation
describes a set of inputs x (x1,..,xn) and an output y. Each xi is called a
feature/attribute/input variable. Y is typically called the output variable.
There is a related input and output, the input is a single value.
Feature is the day of the year we are talking about
How accurately can we predict the temperature?  accurate prediction is hard

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