Welcome to the Machine Learning for Data Science lecture

The course provides an overview of machine learning methods and algorithms for different learning tasks, namely supervised, unsupervised and reinforcement learning.

In the first part of the course, for each task the main algorithms and techniques will be covered including experimentation and evaluation aspects.

In the second part of the course, we will focus on specific learning challenges including high-dimensionality, non-stationarity, label-scarcity and class-imbalance.

By the end of the course, you will have learned how to build machine learning models for different problems, how to properly evaluate their performance and how to tackle specific learning challenges.

Syllabus

Part 1

• Supervised learning
• Unsupervised learning
• Reinforcement learning
• Outlier detection

Part 2

• Machine learning for high-dimensional data
• Machine learning in non-stationary environments
• Machine learning under label scarcity

Requirements:

Basic knowledge of mathematics, algorithms & data structures and programming (Python).