Statistical Machine Learning
Sapienza University of Rome ยท M.Sc. in Data Science
Overview
This course introduces the theory and methodology of statistical machine learning, focusing on error bounds, performance guarantees, and the design of successful learning algorithms.
It bridges theoretical foundations, methodology, and practical implementation, exploring a wide range of learning models and their applications.
๐ Complete course information
Main objectives
- Understand the statistical properties of machine learning algorithms
- Learn to identify when models work and when they fail
- Explore error bounds and performance guarantees
- Combine theory with practical aspects in R, Keras, and TensorFlow
Learning outcomes
- Knowledge of main ML methodologies and paradigms, with their strengths and weaknesses
- Ability to design and select models for applied problems
- Skills to assess both empirical and theoretical performance
- Development of a critical mindset for evaluating learning paradigms
- Communication of methods and results through reports and presentations
My work in this course
- Hackathon project
- Smart Urban Sustainability (SUS) Hackathon
- ๐ Achieved 1st place (LinkedIn announcement)
- Smart Urban Sustainability (SUS) Hackathon
- Final project