LEARNING OUTCOMES
After completeing the course, students should be able to pre-process and visualize marine data (e.g., AIS data, ice data, vessel data), apply different classification, regression, and clustering algorithms for marine data anlysis, evaluate different data analysis techniques and select appropriate technique for chosen engineering problems, and program the different data analysis algorithms in Python.
Credits: 5
Schedule: 04.09.2024 - 04.12.2024
Teacher in charge (valid for whole curriculum period):
Teacher in charge (applies in this implementation): Mashrura Musharraf
Contact information for the course (applies in this implementation):
CEFR level (valid for whole curriculum period):
Language of instruction and studies (applies in this implementation):
Teaching language: English. Languages of study attainment: English
CONTENT, ASSESSMENT AND WORKLOAD
Content
valid for whole curriculum period:
Marine data pre-processing, Marine data visualization, Classification, Regression, & Clustering algorithms and application to maritime engineering, Model selection & Boosting
Assessment Methods and Criteria
valid for whole curriculum period:
Quizzes,
Assignments
Project Works
Workload
valid for whole curriculum period:
Lectures,
Assignments,
Project Works
DETAILS
Substitutes for Courses
valid for whole curriculum period:
Prerequisites
valid for whole curriculum period:
SDG: Sustainable Development Goals
4 Quality Education
9 Industry, Innovation and Infrastructure
12 Responsible Production and Consumption
FURTHER INFORMATION
Further Information
valid for whole curriculum period:
Teaching Language: English
Teaching Period: 2024-2025 Autumn I - II
2025-2026 Autumn I - IIRegistration:
Registration for the course will take place on Sisu (sisu.aalto.fi).