Corpus ID: 51925400

Machine Learning: Basic Principles

@article{Jung2018MachineLB,
  title={Machine Learning: Basic Principles},
  author={A. Jung},
  journal={arXiv: Learning},
  year={2018}
}
  • A. Jung
  • Published 2018
  • Mathematics, Computer Science
  • arXiv: Learning
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… Expand
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References

SHOWING 1-10 OF 99 REFERENCES
An Introduction to Statistical Learning: with Applications in R
  • 2,181
  • PDF
Semi-Supervised Learning
  • 2,446
  • PDF
Probabilistic Graphical Models: Principles and Techniques - Adaptive Computation and Machine Learning
  • 524
  • PDF
Deep Learning
  • 15,705
  • Highly Influential
  • PDF
A Fixed-Point of View on Gradient Methods for Big Data
  • A. Jung
  • Mathematics, Computer Science
  • Front. Appl. Math. Stat.
  • 2017
  • 16
  • PDF
Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers
  • 12,292
  • PDF
The Elements of Statistical Learning
  • E. Ziegel
  • Computer Science, Mathematics
  • Technometrics
  • 2003
  • 8,271
  • Highly Influential
Introductory Lectures on Convex Optimization - A Basic Course
  • 4,230
Proximal Algorithms
  • 2,539
  • PDF
Semi-Supervised Learning in Gigantic Image Collections
  • 266
  • PDF
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