Om kursen
Aim
This course offers an introduction to information theory and its application to digital communication, statistics, and machine learning.
One important feature of the information-theory approach is its ability to provide fundamental results, i.e., results that demonstrate the optimality of certain procedures.
Obtaining results of this flavor is useful for many reasons: for example, we can assess whether achieving a target error probability in the transmission of information is feasible; we can determine how many data samples need to be collected to distinguish between two or more statistical hypotheses, or how many examples are needed to train a machine learning algorithm.
Additional information
The course is also part of the master program in information and communication technologies (MPICT). Additional information is available in the student portal
Application code: 13111
Application information
Contact Giuseppe Durisi
This course offers an introduction to information theory and its application to digital communication, statistics, and machine learning.
One important feature of the information-theory approach is its ability to provide fundamental results, i.e., results that demonstrate the optimality of certain procedures.
Obtaining results of this flavor is useful for many reasons: for example, we can assess whether achieving a target error probability in the transmission of information is feasible; we can determine how many data samples need to be collected to distinguish between two or more statistical hypotheses, or how many examples are needed to train a machine learning algorithm.
Additional information
The course is also part of the master program in information and communication technologies (MPICT). Additional information is available in the student portal
Application code: 13111
Application information
Contact Giuseppe Durisi
Mer information
Telephone: 031 772 18 21
Email: durisi@chalmers.se
Föreläsare
Giuseppe Durisi
