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Study-Unit Description
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CODE ICS2207

 
TITLE Machine Learning 1

 
LEVEL 02 - Years 2, 3 in Modular Undergraduate Course

 
ECTS CREDITS 5

 
DEPARTMENT Artificial Intelligence

 
DESCRIPTION This study-unit will introduce techniques in Machine Learning and includes an introduction to Inductive, Deductive, Supervised and Unsupervised Learning, typical problems that need approximate solutions and can be constructed as search problems, Vector Quantisation and Clustering, Combinatorial Optimisation, Monte Carlo Methods, Genetic Algorithms, Simulated Annealing, Ant Colony Optimisation, and an introduction to Markov techniques.

Study-Unit Aims:

The study-unit aims to:
• Introduce students to the field of machine learning, search and optimization;
• Help students understand different algorithms that can be used to solve typical problems;
• Introduce the theoretical foundations of machine learning, and search and optimization problems;
• Enable the student to choose the right algorithms and methods to solve problems;
• Prepare students for more advanced machine learning topics, expert systems and fuzzy logic;
• Prepare students for other study-units, and dissertations that require knowledge of machine learning techniques.

Learning Outcomes:

1. Knowledge & Understanding:
By the end of the study-unit the student will be able to:

• Analyse and determine how and why stochastic and approximate methods are required to solve certain types of problems;
• Demonstrate an understanding of the difference between supervised and unsupervised learning models;
• Implement and understand basic machine learning techniques;
• Identify problems that can be tackled using these algorithms.

2. Skills:
By the end of the study-unit the student will be able to:

• Use Machine Learning techniques to solve real-world problems;
• Choose the right algorithms to solve problems which may be intractable using other methods or ones that are poorly defined.

Textbooks:

• Course notes and references given in class

 
RULES/CONDITIONS Before TAKING THIS STUDY-UNIT YOU ARE ADVISED TO TAKE ICS1018

 
STUDY-UNIT TYPE Lecture, Laboratory Session and Tutorial

 
METHOD OF ASSESSMENT
Assessment Component/s Resit Availability Weighting
Project Yes 30%
Examination (2 Hours) Yes 70%

 
LECTURER/S

 
The University makes every effort to ensure that the published Courses Plans, Programmes of Study and Study-Unit information are complete and up-to-date at the time of publication. The University reserves the right to make changes in case errors are detected after publication.
The availability of optional units may be subject to timetabling constraints.
Units not attracting a sufficient number of registrations may be withdrawn without notice.
It should be noted that all the information in the study-unit description above applies to the academic year 2017/8, if study-unit is available during this academic year, and may be subject to change in subsequent years.
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13th Edition of EY’s Annual Attractiveness Event

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The 13th Edition of EY’s Annual Attractiveness event will be held on 25th October 2017 at the InterContinental Hotel,

St. Julians. It is titled "Thinking without the box: disruption, technology and FDI".

 

The  students' invitation and more information can be found here

The conference programme can be found here

 

 
 

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