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Python Fundamentals for Machine Learning

The central goals of the course:

  • Programming basics in Python.
  • Fundamental tools and techniques in Python to discover, visualise, manipulate and clean data (mainly with the Pandas and Matplotlib libraries).
  • Holistic overview of the ML landscape.
  • Basic challenges with data for ML (missing values, noise, over- and undersampling).
  • Introduction to supervised learning vs unsupervised learning and reinforcement learning.
  • Introduction to bias-variance tradeoff and techniques to avoid over- and underfitting.
  • Introduction to selected ML algorithms such as linear regression, decision trees, random forests, ensemble learning, etc.
  • Classification and regression examples.
  • Implementing basic ML models using Python (including feature scaling and test/train split development).
  • Evaluating the performance of ML models using performance metrics.
  • Hands-on practice of the acquired knowledge.

Exam info and full course description

Exam info and full course description can be found in the course catalogue.

Admission Requirements

Course specific:

A bachelor’s degree in in engineering, business or science.

General:

Exchange students: nomination from your home university

Freemovers: documentation for English Language proficiency

You can read more about admission here.

Lecturer

Tharsika Pakeerathan Srirajan

PhD Student Department of Business Development and Technology