Syllabus

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:

  Substitutes for Courses

Prerequisites

• valid for whole curriculum period:

  Prerequisites

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 - II

  Registration:

  Registration for the course will take place on Sisu (sisu.aalto.fi).

Description