ITI43210 Machine Learning (Spring 2019)

The course is connected to the following study programs

Elective course in the master programme in applied computer science full time and part time.

Absolute requirements

None.

Recommended requirements

None.

Lecture Semester

Second semester (spring) in the full time programme.

Second or fourth semster (spring) in the part time programme.

The student's learning outcomes after completing the course

Knowledge

The student has

• an overview over the most important methods in machine learning, and a deeper knowledge of three of them, viz. Decision Trees, Neural Networks, and Evolutionary Computation
• knowledge about the practical challenges in connection with data mining, e.g. overfitting, missing values, and classifications with different costs.
• gained knowledge in basic topics such as numerical optimisation and statistical methods, for example Bayesian methods used in search engines like Google and in spam filters.

Skills

The student is able to

• arrange or code data to fit data mining and machine learning algorithms
• choose correct tools for a given type of data
• decide on how good the results are based on simpel statistical analysis of, for instance, classification exactness
• use machine learning i practical applications and be able to transfer machine learning models to programming language code

General competence

The student has improved his/her competence in

• research and development, for instance finding relevant literature and understand scientific articles about machine learning
• writing scientific texts in English
• treat and analyse data of arbitrary type, even if this is done without inductive learning
  

Content

Machine learning is about computers learning through training and experience instead of being explicitely programmed for a given task.The students will get acquainted with several methods and algorithms for machine learning. Based on this, the students should be able to select the methods best suited for the problem in question.

The course should give the students knowledge about the basic properties common to all machine learning methods. Examples include ability to generalise and heuristic search.

The course contains three projects, one about decision trees, rules and regression analysis, one about neural networks, and one about evolutionary computation.

Induction of decision trees and some applications such as medical diagnosis and credit evaluation.

Artificial neural nets and optimization algorithms such as steepest descent and trust region Newton methods. Applications of neural nets to sound and image analysis.

Basic theory for machine learning, for example Bayes' formula, maximum likelihood and the minimum description length principle.

Instance based learning such as nearest neighbour, locally weighted regression, and radial basis functions.

Evolutionary computation, especially genetic algorithms and genetic programming. General principles for evolution. Selection methods and genetic operators such as mutation and crossover. The Baldwin effect.

Some of the topics above require basic knowledge of statistics and information theory which will be taught as needed.

Forms of teaching and learning

Lectures and supervision.

Workload

2 hours lectures per week and projects with mandatory meetings with the supervisor every week. Approx 400 hours.

Practical training/internship

None.

Coursework requirements - conditions for taking the exam

None.

Examination

Portfolio and take home exam

The exam consists of both a portfolio and a take home exam.

The portfolio (determines 65 % of the final grade) consists of:

• one project on decision trees
• one project about neural nets
• one project in automatic programming

The projects can be carried out individually or in groups of two students. The students will get an individual grade.

The three day home exam determines 35 % of the final grade and focus on theory. The home exam can be carried out individually or in groups of two students. The students will get an individual grade.

The student will get an individual joint grade for the entire course. Grades: A - F.

Examiners

The exam is assessed by the course instructor and an internal or external examiner.

Conditions for resit/rescheduled exams

Upon re-examination, both parts of the examination must be retaken. Upon re-examination, new assignments and the take home exam will be decided by the course instructor.

Course evaluation

This course is evaluated by a

• Mid-term evaluation (compulsory)

The responisble for the course compiles a report based on the feedback from the students and his/her own experience with the course. The report is discussed by the study quality committee of the faculty of Computer Sciences.

Literature

Last updated 15. October 2015. The readinglist may be subject to change before the semester starts.

Kuhn, Max og Johnson, Kjell, "Applied Predictive Modeling" (2013), 1st Ed., Springer-Verlag New York, ISBN 978-1-4614-6848-6