Course syllabus for Digitalization and AI for future shipping: Fundamentals and applications

Course syllabus adopted 2024-02-15 by Head of Programme (or corresponding).

Overview

  • Swedish nameDigitalisering och AI för framtida sjöfart: grunder och tillämpningar
  • CodeMMS285
  • Credits7.5 Credits
  • OwnerTSILO
  • Education cycleFirst-cycle
  • Main field of studyShipping and Marine Technology
  • DepartmentMECHANICS AND MARITIME SCIENCES
  • GradingTH - Pass with distinction (5), Pass with credit (4), Pass (3), Fail

Course round 1

  • Teaching language English
  • Application code 81135
  • Maximum participants50 (at least 10% of the seats are reserved for exchange students)
  • Open for exchange studentsYes

Credit distribution

0124 Project, part A 4.5 c
Grading: TH		0 c0 c0 c4.5 c0 c0 c
0224 Laboratory, part B 3 c
Grading: UG		0 c0 c0 c3 c0 c0 c

In programmes

Examiner

Eligibility

General entry requirements for bachelor's level (first cycle)
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Specific entry requirements

The same as for the programme that owns the course.
Applicants enrolled in a programme at Chalmers where the course is included in the study programme are exempted from fulfilling the requirements above.

Course specific prerequisites

Knowledge and skills corresponding to learning outcomes of the following courses (or similar): MMS265 Introduction to international logistics

Aim

The course aims to enhance students' knowledge in digitalization of the maritime sector with an emphasis on how digitalization can be used in daily operations and for decision-making transport. For this, the course aims to enhance students' understanding and skills in big data and AI/machine learning tools used in shipping digitization, as well as in visualizing-, and analyzing data within environmentally sustainable maritime transport and management. Through actual industry practice in environmentally sustainable shipping, the course also aims to provide students with an in-depth understanding of challenges and opportunities related to digitalization and AI in the maritime transport industry. During the course, participants' skills in PYTHON programming will be further developed.

Learning outcomes (after completion of the course the student should be able to)

  • Explain challenges and opportunities in digitalization in maritime transport sector
  • Explain use of big data/AI at stakeholders within shipping sector
  • Apply PYTHON in big data/AI analysis
  • Explain application of programming scrips in big data/AI analysis
  • Show understanding in the use of big data tools in digitalization of the maritime transport sector
  • Develop machine learning models for decision making
  • Explain and reflect on the impact of digitalization/on students own learning 

Content

BLOCK 1 Digitalization in the maritime transport sector
Introduction
  • Shipping Digitalization status and trend
  • Ship/port digitalization sensors and systems
  • Traffic information in shipping chain
  • Big data concerning maritime environment in shipping
  • Data collection and sharing scheme among shipping stakeholders
Challenges and opportunities of big data from shipping digitalization
  • Data collection/storage/security
  • Data and sensor sensitivities (AIS data, ship monitoring data, environment data, logistic data, management plan data, …)
  • Data visualization
  • Data analysis and data mining techniques
BLOCK 2 Big data and AI tools in digitalization in the maritime transport sector
Basis tools to handle shipping digitalization information
  • Overview of python and libraries for data analysis
  • Quality assessment of different shipping data sources
  • Types of data information from shipping digitalization
  • Examples of data handling for decision making
Basic AI tools for big data analytics in shipping digitalization
  • Clarification of different terminologies within field of AI and ML
  • Overview of different machine learning categories
  • Basic mathematics and statistics for application of ML
Big data and AI tools in modern maritime transport decision making process
  • Maritime economics and logistic planning
  • Port management and ship route planning
  • Ship performance monitoring
  • Ship post-voyage analysis and maintenance planning
BLOCK 3: Machine learning and programming
Machine learning methods
  • Basic regression
  • Decision trees and ensemble algorithm
  • Neural network and Deep Learning
PYTHON programming
  • Understanding python scripts
  • Python programming
Examples of digitalization and AI tools in shipping
  • AI/ML techniques for modelling shipping efficiency and safety
  • AI/ML techniques for modelling shipping economics/logistics
  • AI/ML techniques for modelling maritime environment impact

Organisation

The course is organized around,
  • Lectures and guest lectures
  • Computer laboratory exercises,
  • Project assignments
  • Seminars

Literature

The following literature will be used in the course:
  • Gruner, J. (2021). Digital Transformation in Shipping: The Hapag-Lloyd Story. In: Seebacher, U.G. (eds) B2B Marketing. Management for Professionals. Springer, Cham.
  • Martelli, A, Ravencroft, Holden, S. McGuire, P. (2023). Python in a Nutshell (4th Edn), O’Reilly.
  • Computer laboratory manual and instructions are provided during the course.

Examination including compulsory elements

The examination of the course consists of the following elements,
  • Course element 1: Project assignments presented and reported during a final seminar (4,5 credits)
  • Course element 2: Laboratory exercises, PYTHON programming (3 credits)

The course examiner may assess individual students in other ways than what is stated above if there are special reasons for doing so, for example if a student has a decision from Chalmers on educational support due to disability.