## Terminology and key issues with Machine Learning

November 7, 2015 Leave a comment

These are some of the terms which are used in machine learning algorithms.

**Training Example**: An example of the form [x, f(x)]. Statisticians call it ‘**Sample**’. It is also called ‘**Training Instance**’.**Target Function**: This is the true function ‘f’, that we are trying to learn.**Target Concept**: It is a boolean function where- f(x) = 1 are called positive instances
- f(x) = 0 are called negative instances

**Hypothesis**: In every algorithm we will try to come up with some hypothesis space which is close to target function ‘f’.**Hypothesis Space**: The space of all hypothesis that can be output by a program. Version Space is a subset of this space.**Classifier**: It’s a discrete valued function.- Classifier is what a learner outputs. A learning program is a program where output is also a program.
- Once we have the classifier we replace the learning algorithm with the classifier.
- Program vs Output and Learner vs Classifier are same

Some of the notations commonly used in Machine Learning related white papers

Some of the **key issues with machine learning** algos

- What is a good hypothesis space? Is past data good enough?
- What algorithms fit to what spaces? Different spaces need different algorithms
- How can we optimize the accuracy of future data points? (this is also called as ‘
**Problem of Overfitting**‘) - How to select the features from the training examples? (this is also called ‘
**Curse of Dimentionality**‘) - How can we have confidence in results? How much training data is required to find accurate hypothesis (it’s a statistics question)
- Are learning problems computationally intractable? (Is the solution scalable)
- Engineering problem? (how to formulate application problems into ML problems)

**Note: **Problem of Overfitting and Curse of Dimentionality will be there with most of the real time problems, we will look into each of these problems while studying individual algorithms.

**REFERENCES**

http://www.cs.waikato.ac.nz/Teaching/COMP317B/Week_1/AlgorithmDesignTechniques.pdf

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