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Introduction to machine learning and its applications

FDA178, 2005VT

Status Archive
School Computer and Information Science (CIS)
Division ADIT
Owner Nahid Shahmehri

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Course plan

No of lectures

Probably as follows:

Part 1: 6-7 3-hour sessions (18-21 hours in total).

Part 2: ~3 3-hour sessions (9 hours in total, but will depend on the number of participants).

Recommended for

PhD students in computer science.

The course was last given

The course is loosely based on a study circle at ADIT given in 1998. Other related graduate courses previously taught at IDA include FDA140, FDA133, and FDA093.


There are two main goals:

1. To provide an overview of fundamental machine learning techniques, and

2. To provide opportunities for in-depth study of how these techniques can be (or have been) applied to practical learning problems.



The course is organised as a combination of lectures and study circle,
and it consists of two parts.

Part one will start with a brief overview lecture introducing the topic of machine learning. Afterwards, participants will in turn study and present fundamental machine learning techniques (see content description below).

In part two, each participant will choose (or suggest) a topic for in-depth study. This in-depth study will investigate how machine learning techniques can be applied to a certain type of learning problem (e.g. for anomaly detection, user profiling, etc). This study could be organised as a literature survey or as an implementation project (or a combination). These studies will end with a written report and presentation.


Fundamental machine learning topics include concept learning, decision
tree learning, artificial neural networks, Bayesian learning, instance-based learning, genetic algorithms, and reinforcement learning.

Based on the interest of the participants, there may also be possibilities to include more recent developments (e.g. ensemble learning, support vector machines) to this list of fundamental topics.


The following book by Tom Mitchell will be used to cover the fundamental topics studied in part one.

Tom Mitchell, 'Machine Learning', McGraw-Hill, 1997.

In addition, other material may be used to cover more recent advances
in machine learning.

Part two will be based mainly on research papers, and will depend on the type of project suggested by the participant.


Johan Åberg, and possibly guest lecturers.


Nahid Shahmehri and Johan Åberg


Part one: active participation and presentations.

Part two: project report and presentation.


3+3 credits.


Will likely be given in the later part of VT2005.

Page responsible: Director of Graduate Studies
Last updated: 2012-05-03