GRA 8273 Machine Learning
GRA 8273 Machine Learning
Digitalization creates high speed, high volume digital activity traces – Big Data. Using these data and recent advances in Machine Learning (ML), organizations can automatically build predictive models and tools that can improve a wide variety of routines, ranging from targeted marketing to preventative maintenance to fraud detection. But adopting these tools requires: specifying viable problems, recognizing underlying data requirements and shortcomings, understanding models’ potential failure points, assessing model performance, translating model predictions to actionable business insights, and reflecting on the ethical and security implications of data collection/model building. To successfully deploy ML/AI, managers must become familiar and comfortable with foundational tasks. Managers do not need to become data scientists, but managers in digital organizations need to understand what ML can and cannot do, what ML requires, how to formulate viable ML questions, how to action data-driven predictions, and how models can break.
This course develops these foundational skills through hands-on experience with cutting edge analytics tools and techniques, and through case discussions designed to reflect on the technical foundations of ML/AI successes and failures.
Upon completion, the student will be able to:
- Analyze the interdependencies between data structures, modeling techniques, and problem specifications.
- Evaluate potential modeling and data errors within ML frameworks that lead to discrepancies between development and practical performance.
- Assess critical security issues and best practices associated with machine learning applications, including its use for enhancing security.
Upon completion, the student will be able to:
- Select and justify appropriate tools and components for initiating a machine learning project.
- Formulate and scope viable machine learning problems from complex real-world challenges.
- Collaborate effectively within data science teams to define project requirements and critically evaluate modeling outcomes.
Upon completion, the student will be able to:
- Examine and critically discuss the social and security implications inherent in datamodeling and machine learning applications.
Course outline (on an overall level)
- Machine learning’s location within the analytics landscape
- Data structures, and the correspondence between data and ML tasks
- Specifying viable and valuable ML problems
- Modeling processes and techniques
- Automated ML
- ML case studies, reflecting on the technical underpinnings
The course consists of lectures and ML labs using Python (on a local computer), and some case discussions. Lectures will reference Python code "only" for illustrative purposes and students are "not " expected to have any coding background or write any codes. Following introductory lectures and labs, we will use applications and use cases to reflect on how tools, codes, applications are used for which tools might be developed considering ethical/security considerations.
Attendance to all sessions in the three core topics of the course is compulsory. If you have to miss part(s) of any of the three core topics of the course, you must ask in advance for leave of absence. More than 25% absence in one of the core topics of the course will require retaking the entire topic. It's the student's own responsibility to obtain any information provided in class that is not included on the course homepage/ It's learning or other course materials.
Candidates may be called in for an oral hearing as a verification/control of written assignments.
All the three core topics of the course must be passed in order to obtain a grade for the course.
The course is a part of a full Executive MBA programme and examination in all courses must be passed in order to obtain a certificate.
In all BI Executive courses and programmes, there is a mutual requirement for the student and the course responsible regarding the involvement of the student's experience in the planning and implementation of courses, modules and programmes. This means that the student has the right and duty to get involved with their own knowledge and practice relevance, through the active sharing of their relevant experience and knowledge.
Granted admission to the EMBA programme. Please consult our student regulations.
Disclaimer
Changes in exam type can be made until the course starts. In addition, unforeseen events or external conditions may call for deviations in teaching and exams.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Exam/hand-in semester: First Semester Weight: 60 Grouping: Individual Duration: 4 Week(s) Exam code: GRA 82732 Grading scale: ECTS Resit: Examination when next scheduled course |
Exam category: Activity, Oral Form of assessment: Presentation Exam/hand-in semester: First Semester Weight: 40 Grouping: Group (2 - 8) Comment: Group project and presentation, counts 40% of the final grade. Candidates may be called in for an oral hearing as a verification/control of written assignments. Exam code: GRA 82733 Grading scale: ECTS Resit: Examination when next scheduled course |
All exams must be passed to get a grade in this course.
| Activity | Duration | Comment |
|---|---|---|
Teaching | 32 Hour(s) | |
Prepare for teaching | 25 Hour(s) | |
Student's own work with learning resources | 63 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 4 ECTS credit corresponds to a workload of at least 110 hours.
Candidates may be called in for an oral hearing as a verification/control of written assignments.