Course Code

ΜΔΑ-220

Semester

1st Semester

ECTS Credits

7,5

Type of Course

Mandatory

Machine Learning: Methods and Algorithms

Objective

The course aims at familiarizing the audience with fundamental machine learning techniques and algorithms that cover the spectrum of diverse machine learning applications (supervised / unsupervised learning). The expected learning outcome of the course includes knowledge of the basic machine learning methods and gaining of experience in implementing and using them effectively. It also includes the critical ability for the choice of an appropriate methodology for each distinct machine learning problem, along with the deep understanding of its advantages and weaknesses.

 

Course Contents

Introduction to Machine Learning.

Types of Machine Learning, Training Methods, Accuracy Metrics, Prediction, Classification.

Regression Methods.

Linear Regression, Logistic Regression, Ridge Regression, Static/Dynamic Autoregressive and Spectral Analysis.

R Language – Machine Learning Applications.

R Language Environment, Syntax, Libraries, Foundational Structures and Functions.

Neural Networks.

Models and Architectures of Neural Networks, Feedforward Neural Networks (Perceptron Algorithm, Multilayer Networks, Backpropagation Algorithm).

Support Vector Machines.

Linear Classification, Kernels, Multiclass Classification.

Clustering Methods.

Definitions, Clustering categories, Distance Measures, Similarity Functions, Hierarchical Methods, Partitioning, k-means algorithm.

Evolutionary Learning and Optimization.

Genetic Algorithms, Hypothesis Space Search, Local Optimization Methods.

Dimensionality Reduction.

Principal Component Analysis, Linear Discriminant Analysis, Low Dimensional Embedding.

Feature Selection and Data Fusion.

Filtering, Wrapper Approach, Embedded Feature Selection Methods.

R Language – Machine Learning Applications.

Machine Learning Programming in R, Experimentation, Visualization.

Recommended Readings

  • T. Mitchell. Machine Learning. McGraw-Hill (International Edition), 1997.
  • C. Bishop. Pattern Recognition and Machine Learning. Springer, 2007.
  • K. Murphy. Machine Learning: A Probabilistic Perspective. MIT Press, 2012.