ITI43210 Machine Learning (Spring 2014)

The course is connected to the following study programs

Mandatory for the Master's Degree Programme in Applied Computer Science.

Lecture Semester

Second semester (spring).

The student's learning outcomes after completing the course

Knowledge

The candidate shall have

• an overview over the most important methods in machine learning, and have 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 Bayesian methods used in search engines like Google and in spam filters.

 

Skills

The candidate 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 candidate has improved his/her competence in

• research and development, for instance finding relevant literature and understand scientific articles about machine learning
• writing scientific texts
• 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.

Algorithms for local and gobal optimization, for example tabu search and simulated annealing and genetic algorithms.

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 (2 hours per week) and projects with mandatory meetings with the supervisor every week.

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.

There is also a three day individual take home exam that determines 35% of the final grade.

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

In the case of a Fail, new assignments and the home exam will be decided by the course instructor.

Course evaluation

This course is evaluated by a

• Mid-term evaluation (voluntary)
• End evaluation (compulsory)

The lecturer compiles a report based on the evaluation forms filled in by the students and his/her own experience with the course. The report is the discussed by the study quality committee of the faculty of Computer Sciences.

Literature

Machine Learning, Tom M. Mitchell, McGraw-Hill higher Education, ISBN 0070428077