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.
- Supervised learning
- Unsupervised learning
- Reinforcement learning
- Outlier detection
- Machine learning for high-dimensional data
- Machine learning in non-stationary environments
- Machine learning under label scarcity
Basic knowledge of mathematics, algorithms & data structures and programming (Python).
The current plan is a hybrid lecture, for which some students attend in the classroom and others online via livestreaming (Webex). Regarding the in-person teaching part, we remain flexible to the pandemic, student and teaching staff needs and we will make adjustments and changes accordingly.
- Lectures (#2 lectures per week):
- Tutorials (#1 tutorial per week, in 2 groups):
Discord invitation link:
For in-person attendance, please follow the official rules.
1st Lecture date: Wednesday 27/10/2021
1st Tutorial date: Tuesday 02/11/2021