30412 - MACHINE LEARNING
Department of Decision Sciences
Course taught in English
(6 credits - II sem. - OB | INF/01)
The goal of this course is to provide the students with the conceptual, mathematical and algorithmic tools that are needed for a rigorous and modern understanding of machine learning. A central topic is the connection with Bayesian inference and, more in general, the critical discussion on how and to which extent machine learning methods become essential in large scale data analysis and in artificial intelligence.
The students implement data analysis algorithms and learn to use the modern scientific libraries; the course includes an original algorithmic development project.
Intended Learning Outcomes
Course Content Summary
- Linear regression.
- Logistic regression.
- Neural networks: representation.
- Neural networks: learning.
- Learning and Bayesian inference.
- Support vector machines.
- Unsupervised learning.
- Dimensionality reduction.
- Large Scale Machine Learning.
- Application example: Photo OCR.
Detailed Description of Assessment Methods
The final mark is the sum of partial marks, obtained through
Each partial exam assigns 16 points; the general exam assigns 32 points.
30 cum laude is obtained with 31 points or more.
The exam takeS place in the informatics classroom.
- D. MAC KAY, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003.
- I. GOODFELLOW, Y. BENGIO, A. COURVILLE,Deep Learning, MIT Press, 2016.
Intermediate level programming, familiarity with the Python numerical and scientific library stack, fundamentals of analysis, fundamentals of linear algebra, fundamental of statistics, fundamentals of optimization theory.
Last change 27/04/2017 14:13