The course is aimed at IT professionals in employment in the Republic of Ireland registered companies. To qualify for direct entry they must have a Level 8 Honours Degree (2.2) or higher in Computer Science, Computing, Computer Applications or a related discipline. Applicants without these entry requirements (e.g., Level 7 degree or lower than an Honours 2.2 in a Level 8 degree) may be considered if they can demonstrate previously obtained competence equivalent to the entry requirements.

It is important to understand and assess the suitability of machine learning techniques for use with your data.

The suitability of many techniques used in machine learning relies on several assumptions which are not always adhered to. The implications of understanding how these techniques estimate the weights in the various models is significant when concluding the results.

In this course, you will examine Maximum Likelihood Estimation (MLE). It is one of the most important parameter estimation techniques in statistical modelling. We outline why this is the case and we will show you how they can derive theoretical estimates from first principles.

In this course we will outline Maximum likelihood estimation is one of the most important parameter estimation techniques in statistical modelling.

Upon completion you will understand what a maximum likelihood estimator is and how to implement it to estimate a parameter for the mean and regression models.

You will also be able to outline the optimal characteristics of a maximum likelihood estimator.