30E03500 - Capstone: Data Science for Business II D, Lecture, 22.10.2024-27.11.2024

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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

The course introduces modern methods in predictive modelling (e.g. quantile/convex regression and deep learning) and provides an introduction to Explanable Artificial Intelligence (xAI) and Interpretable Machine Learning (IML). Upon completion of the course, students will be able to apply machine learning techniques to support business decision making.

Credits: 6

Schedule: 22.10.2024 - 27.11.2024

Teacher in charge (valid for whole curriculum period):

Teacher in charge (applies in this implementation): Pekka Malo

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:

    The content may vary from year to year depending on the lecturers. A more detailed description of the actual content for the implementation will be provided in the course syllabus on MyCourses before registration for the implementation opens.

    The examples of topics are:
    - Advanced regression techniques (quantile/convex regression)
    - Image recognition using convolutional neural networks (CNN)
    - Text analysis and NLP using recurrent neural networks (RNNs) and transformers
    - Introduction to Explainable Artificial Intelligence (xAI) and Interpretable Machine Learning (IML)

    The course consists of pre-recorded lecture videos and tutorials in Python for the topics. Python assignments and theory tests will be used to assess student knowledge.

    The course is intended for BIZ students. Therefore, in addition to the theoretical foundations, the applications (not the development) of the methods will be presented. Lectures are designed to provide the in-depth material, while the assignments are designed to try out the methods.
    Knowledge of Python is assumed, but the tutorials are detailed and the assignments are designed considering that coding is a secondary skill.

Assessment Methods and Criteria

  • valid for whole curriculum period:

    Assignments, quizzes

Workload

  • valid for whole curriculum period:

    Lectures, tutorials, assignments

DETAILS

Study Material

  • valid for whole curriculum period:

    Lectures (recordings, slides), tutorials (recordings, notebooks), other material defined in syllabus.

Substitutes for Courses
Prerequisites

FURTHER INFORMATION

Further Information

  • valid for whole curriculum period:

    Teaching Language: English

    Teaching Period: 2024-2025 Autumn II
    2025-2026 Autumn II

    Registration:

    The selection is made by Sisu automatically based on the priority groups. The priority for the student selection is as follows 1. Aalto ISM MSc students, Aalto Business Analytics MSc student. 2. Students in Master’s Programme in ICT Innovation (EIT digital). 3. Bachelor’s students in Business with 150 credits complete. 4. Other Aalto students.

    Due to the logistics of the course, late registrations will not be accepted.

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