Enrolment options

Please note! Course description is confirmed for two academic years, which means that in general, e.g. Learning outcomes, assessment methods and key content stays unchanged. However, via course syllabus, it is possible to specify or change the course execution in each realization of the course, such as how the contact sessions are organized, assessment methods weighted or materials used.

LEARNING OUTCOMES

In the age of big data and artificial intelligence, proficiency in database management is essential for leveraging the power of machine learning algorithms. This undergraduate course provides students with a comprehensive understanding of databases and their pivotal role in supporting machine learning applications. Through a blend of theoretical concepts, practical exercises, and real-world case studies, students will explore the principles, methodologies, and best practices for integrating databases with machine learning workflows.

Credits: 5

Schedule: 15.04.2025 - 30.05.2025

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Alex Jung

Contact information for the course (applies in this implementation):

Associate Professor Alexander Jung (alex.jung@aalto.fi)

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:

    * Introduction to Databases and Machine Learning:

    * Relational Databases and SQL for Machine Learning:

    * NoSQL Databases and Big Data Technologies

    * Data Preprocessing and Feature Engineering:

    * Database Integration with Machine Learning Frameworks:

Assessment Methods and Criteria
  • valid for whole curriculum period:

    Assignemtns and Quizzes. 

Workload
  • valid for whole curriculum period:

    Lectures 69h, independent work 59h.

DETAILS

Study Material
  • valid for whole curriculum period:

    A. Jung, "Machine Learning: The Basics," Springer, 2022. 

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information
  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Spring IV - V
    2025-2026 Spring IV - V

  • applies in this implementation

    This course will use a more hands-on approach, similar in spirit to ISM-C2002 - MySQL for Data Analytics. For a more theoretic perspective on databases we refer to the course CS-A1150.

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