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TDT4173: Machine Learning and Case-Based Reasoning

# Introduction ## Well Posed Learning Problems __Definition__: A computer program is said to __learn__ from experience _E_ with respect to some class of tasks _T_ and performance measure _P_ if its performance at tasks in _T_, as measured by _P_, improves with experience _E_. An example of a task, inspired by [MarI/O](https://www.youtube.com/watch?v=qv6UVOQ0F44): - Task _T_: Playing the first level of Super Mario Bros. - Performance measure _P_: How far the Mario character is able to advance. - Training experience _E_: Playing the level over and over. ## Learning "Any process by which a system improves performance" -H Simon Basic definition: "Methods and techniques that enable computers to improve their performance through their own experience" ## Why machine learning? * Today, lots of data is collected everywhere (Big Data) * There are huge data centers

## Concept learning and the General-to-Specific Ordering

## Inductive bias An inductive bias is the set of assumptions needed to take some new input data, which the system has _not_ encountered before, and predict the output. E.g: a self-driving car may have been trained to avoid cats in the road, but suddenly it encounters a dog. What should it do? If the system does not have an inductive bias, then it has not learned anything other than reacting to the specific examples that it has been trained on. In that case, it is basically just a [key-value store](https://en.wikipedia.org/wiki/Key-value_database) for its training examples. An inductive bias is essential to machine learning. ## Candidate elimination algorithm

This is a ~~very important~~ n important part of the curriculum. Learn it.

# Decision ~~trees ## Decision tree learning with~~Tree Learning ## ID3 algorithm

## Overfitting

## Cross validation # Evaluating Hypotheses # Bayesian Learning # Computational Learning Theory # Instance-Based Learning # Learning Sets of Rules # Analytical Learning # Case–Based Reasoning # Deep Learning # Ensemble Methods # Support Vector Machines