Machine Learning: Basic Principles
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by
Alexander Jung
2020
Abstract
This tutorial introduces some main concepts of machine learning (ML). From an
engineering point of view, the field of ML revolves around developing software
that implements the scientific principle: (i) formulate a hypothesis (choose a
model) about some phenomenon, (ii) collect data to test the hypothesis
(validate the model) and (iii) refine the hypothesis (iterate). One important
class of algorithms based on this principle are gradient descent methods which
aim at iteratively refining a model which is parametrized by some ("weight")
vector. A plethora of ML methods is obtained by combining different choices for
the hypothesis space (model), the quality measure (loss) and the computational
implementation of the model refinement (optimization method). %Many of the
current systems, which are considered as (artificially) intelligent, are based
on %combinations of few basic machine learning methods. After formalizing the
main building blocks of an ML problem, some popular algorithmic design patterns
for ML methods are discussed. This tutorial grew out of the lecture notes
developed for the courses "Machine Learning: Basic Principles" and "Artificial
Intelligence", which I have co-taught since 2015 at Aalto University.
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