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Module Code - Title:

CS5014 - DATA SCIENCE

Year Last Offered:

2021/2

Hours Per Week:

Lecture

2

Lab

0

Tutorial

1

Other

3

Private

4

Credits

6

Grading Type:

N

Prerequisite Modules:

Rationale and Purpose of the Module:

This module is a core module for the MSc in Artificial Intelligence.

Syllabus:

1. Introduction to Data Analytics/Science programming language e.g. Julia 2. Principles of problem modelling for optimization 3. Resource allocation problems as optimization problems 4. Modelling alternatives: Constraint Satisfaction, Boolean Satisfiability, Linear and Integer Linear Programming, Quadratic / Non-Linear Programming 5. Modelling languages / environments 6. Theoretical aspects of machine leanring algorithms: convergence, termination

Learning Outcomes:

Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)

1. Demonstrate an ability to formulate machine learning and other data exploration algorithms as optimization algorithms; 2. Demonstrate an ability to formulate resource allocation problems in one, or more, optimization modelling environments

Affective (Attitudes and Values)

1. Instil awareness of societal implications of decision making algorithms 2. Demonstrate a professional commitment to ethics in the practice of data manipulation and analysis

Psychomotor (Physical Skills)

N/A

How the Module will be Taught and what will be the Learning Experiences of the Students:

The module will be delivered using a blended learning approach using on-line lectures, labs and tutorials.

Research Findings Incorporated in to the Syllabus (If Relevant):

Prime Texts:

Boyd, Vandenberghe (2004) Convex Optimization , Cambridge University Press

Other Relevant Texts:

Nocedal, Wright (2006) Numerical Optimization , Springer

Programme(s) in which this Module is Offered:

Semester - Year to be First Offered:

Module Leader:

Enrique.naredo@ul.ie