### Introduction to Machine Learning (ILV)

#### Course lecturer:

DI Dr.in techn.

Olivia Pfeiler

FH-Prof. DI (FH) Dr. techn.

Markus Prossegger Course number M2.08760.11.051 Course code IML Curriculum 2021 Semester of degree program Semester 1 Mode of delivery Presence- and Telecourse SPPW 3,5 ECTS credits 5,0 Language of instruction English

The students know the statistical and mathematical foundations of ML methods, the approaches to ML and the basic concepts and techniques.
Additionally, the students know the importance and the power of feature engineering and the crucial step of model evaluation.
Further, they are able to apply the ML cycle, incl. handling of real data, data preparation and the application of the learned ML techniques with the scripting languages R and Python.

none

The module covers the following topics/contents:
Foundations of ML:

• Mathematics for ML, incl. matrix decompositions, calculus, gradients
• Overview on discrete and continuous probability distributions
• Basics of statistical learning, incl. loss function, decision analysis, Bayes decision, graphical models and model selection
• Basics of multivariate statisticsOverview on optimization methods (Gradient Descent, Stochastic gradient descent, Constrained and Convex Optimization)
ML basics:
• Introduction to primary approaches to machine learning
• Machine learning concepts and techniques
• Anatomy of a learning algorithm & overview of fundamental algorithms (Regression, Support Vector Machine, Clustering, ...)
• Filtering techniques
• Feature engineering
• Model inference and prediction
• Model evaluation (Confusion matrix, Accuracy, F1-score, Precision, Recall, Cross validation, ...)
Application of ML methods to data:
• Introduction to script languages (R/Python)
• The machine learning cycle (data analysis pipeline)
• Data preparation techniques (outlier detection, missing values, data structures, error and noise...)

Lecture script as provided in the course (required)
Introduction to Machine Learning with Python: A Guide for Data Scientists. Sarah Guido. O'Reilly UK Ltd, 2016
Understanding Machine Learning: From Theory to Algorithms. Shai Shalev-Shwartz und Shai Ben-David. Cambridge University Press, 2014
Machine Learning for Hackers. Drew Conway & John Myles White. O'Reilly UK Ltd, 2012
The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Trevor Hastie, Robert Tibshirani, and Jerome Friedman. Springer 2009

Integrated course - teaching & discussion, guest lectures by specialists, demonstration, exercises and practical examples in the lab, home work

Immanent examination character:presentation, assignment reports, written/oral exam