Game Design and Production

Professor in charge: Perttu Hämäläinen
Extent: Long or compact major (40-65 credits). Students taking a compact major take also a minor (20-25 cr). Students taking a long major may include an optional minor in their elective studies.
Code: SCI3046
School: School of Science

Objectives

The objective of the major is to educate programmer-designers* that understand both technology and the player’s point of view, and can thus 1) participate in overall game design and 2) take responsibility of the myriad design decisions that are not necessarily communicated in a design document and only arise during implementation.

The students will learn about game design, production, and technology using a project-oriented, hands-on with minds-on approach. The project courses emphasize interdisciplinary and collaborative work. The teacher network includes both game industry professionals and game scholars.

* You may also substitute “engineer” or “computer scientist” for “programmer”

Learning Outcomes

  • Deepening of technological expertise already built during Bachelor level studies (compulsory technical courses on computer graphics, machine learning, and artificial intelligence)
  • Building a wide set of cross-disciplinary design, production, and teamworking skills (compulsory Deparment of Media courses, especially DOM-E5095 game project, during which multiple games are developed). 
  • Deeper understanding of each student's specific areas of interest (large selection of elective courses that can be included in the personal study plan).

Structure and content

The Game Design and Production major is organized in collaboration with Media Lab Helsinki of Aalto ARTS, which has an M.A. in New Media “sibling major” with the same name. Computer and video games is a multidisciplinary field, and the M.Sc. and M.A. majors share a large portion of the courses. The obligatory courses differ, however, and the CCIS students should expect to work in a more technical role, e.g., when creating a joint thesis game with ARTS students. Multidisciplinarity is also emphasized by the high flexibility of elective studies, where one can include, e.g., 3D animation, interactive storytelling and interaction design in addition to computer science.

Students take the Major compulsory courses. In addition, they take Major optional courses. Listing of optional courses is not exhaustive. Additionally, students may choose courses from all Aalto schools according to the personal study plan. It is strongly suggested that students venture outside their comfort zone and do not, for example, take a course in web software development if they already possess the equivalent skills and knowledge.

Major compulsory courses

CODE

NAME

CREDITS

PERIOD/YEAR

CS-C3100

Computer Graphics

5

I-II

CS-E3210

Machine Learning: Basic Principles

5

I-II

CS-E4800 Artificial Intelligence

5

III-IV

DOM-E0000

Understanding Media, Art and Design

4

I/1st year

DOM-E5080

Game Design

5

I

DOM-E5083

Game Analysis

5

III-V

DOM-E5095

Game Project

5-15

I-V/1st year

DOM-E5093

Game Design Exam

1

III,V

DOM-E5001

Personal study plan

1

I/1st year

Recommended optional courses (students may also suggest others as game design is a multidisciplinary field).

CODE

NAME

CREDITS

PERIOD/YEAR

DOM-E5094

Advanced Topics in Game Design

3-5

I

DOM-E5082

Playability Evaluation

3

III

DOM-E5087  

Action Games

3-5

V

CS-E5100

Introduction to IT Business and Venturing

2

I-II

DOM-E5038

Generative and Interactive Narratives

3

III-V

DOM-E5066

Introduction to Sound Design and Music

1-5

I

DOM-E5029  

Introduction to 3D Animation

4

I

DOM-E5058  

Information Visualization and Design

3-6

III

CS-E4840

Information Visualization

5

IV

ELEC-E7851

Computational User Interface Design

 

II

CS-E4200

Emergent User Interfaces

5

III-IV

CS-C3120

Human-Computer Interaction

5

I-II

CS-E5520

Advanced Computer Graphics

5

III-V

CS-C3170

Web Software Development

5

II-III/1st year

CS-C3130

Information Security

5

I/1st year

CS-E3190

Principles of Algorithmic Techniques

5

I-II/1st year

CS-E4580

Programming Parallel Computers

5

V

CS-E4830

Kernel Methods in Machine Learning

5

I-II

CS-E4890

Deep Learning

5

II

CS-E4820

Machine Learning: Advanced Probabilistic Methods

5

III-IV

CS-E4850

Computer Vision

5

III-IV

CS-E4100

Mobile Cloud Computing

5

I-II

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