The course focuses on methods for learning the underlying structures from data and to train models that can make predictions when presented with new data. Such predictions can typically involve the discrimination between different categories of data, or pattern classification, which will be the main focus of this course.
Dette er emnebeskrivelsen for studieåret 2018-2019. Merk at det kan komme endringer.
The course starts with an introduction to the fundamental theory, Bayes decision theory. This statistical based theory let us define optimal decision thresholds to distinguish between data elements, represented by so called feature vectors. These decision thresholds are optimal with respect to minimising the expected error rate. The introductory theory assumes that the statistical functions describing the data are known. This will not be true in practice where we will have to estimate these functions using parametric and non-parametric methods. Alternatively to estimating the statistical functions directly, we can estimate the coefficients in the polynomials describing the decision borders directly. This is introduced with linear discriminant functions where we seek to find the polynomial coefficients the rate of error expressed by a criteria function. To do this, we use iterative gradient descent techniques. Further to this neural networks are presented as a method to use when linear discriminant functions falls short. As part of this, deep neural networks will also be discussed, which is the foundation for deep learning. In the techniques presented this far, the class to which each data element belongs is assumed known. In the application of clustering techniques we no longer make this assumption and seek to find natural clusters in the data material. Finally methods for evaluating classifier performance are presented. Another important aspect of classification is how to characterise the data material as feature vectors. During the course illustrative examples from ongoing research projects within biomedical data analysis are presented.
Learning outcome
At the end of this course, the student should be able to recognize problems that can be handled by machine learning methods. Furthermore, the student shall be able to use the subject terminology acquired throughout the course to state the problem in a precise manner. To be able to solve the problem, the student must be able to implement a classifier by training it using a representative data material and make sure that it is capable of handling new data. The student should be able to handle different type of classifiers and know the theory for these so that specially designed solutions can be made.
Forkunnskapskrav
Ingen
Anbefalte forkunnskaper
Introduction to programming (BID230), Mathematical methods 1 (ÅMA100), Introduction to Probability and Statistics (ÅMA110), Mathematical Methods 2 (ÅMA260)
Exam
Form of assessment
Weight
Duration
Marks
Aid
Exam system
Withdrawal deadline
Exam date
Written exam
1/1
4 Hours
Letter grades
No printed or written materials are allowed. Approved basic calculator allowed
Inspera assessment
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Vilkår for å gå opp til eksamen/vurdering
Exercises
It is important to work with theoretical and laboratory assignments to attain experiene in the application of the methods presented in the lectures.
8 hrs per week with typically 4 hrs lectures and 2 hrs computer exercises and 2 hrs theoretical exercises per week. There might be variations to this.
This is necessary to be able to apply the methods to real world problems. the Mandatory that 85% of each type of exercises passed. Mandatory work demands (such as theoretical hand in assignments, lab- assignments, projects, etc) must be approved by subject teacher within the specified deadlines.
Åpent for
Master studies at the Faculty of Science and Technology.
Emneevaluering
Form and/or discussion
Litteratur
S. Theodoridis: Machine Learning - A Bayesian and Optimization Perspective.
In addition, online articles and notes published on Canvas at the start of semester
The course description is retrieved from FS (Felles studentsystem). Version 1