GRA 4136 Machine Learning for Business
GRA 4136 Machine Learning for Business
This course teaches students how to use machine learning techniques and tools for predictive analytics. The course will also teach students to: specify problems accessible with these methods, evaluate analytics models, use new tools in the rapidly evolving automated machine learning field, and deploy machine learning models. The course assumes prior experience with: basic probability theory, traditional statistics for categorical and continuous independent variables, introductory Python programming, and MS Excel.
Students will learn about machine learning algorithms, translating business problems into problems machines can solve, and evaluating machine learning models. Students will also learn about recent advances in automated machine learning, and the implications for generalized deployment of the technology. Students will extend their programming expertise to machine learning applications, using predictive analytics to address business problems and to evaluate strategic options. Students will become familiar with automated machine learning tools. Because these automated ML tools are very new and changing very rapidly, final decisions on tools will depend on the “state of the art” when the course starts.
Students completing the course will be able to: specify business problems such that predictive analytics can be applied; evaluate and specify project data requirements; execute predictive analytics on a broad range of problems; evaluate predictive analytics model results; and discuss results/requirements both with data scientists and with practitioners.
Positioning data vis-à-vis methods, as in predictive analytics, is a natural opportunity to inculcate a perspective that organizations are problem-solving, data-driven entities. Predictive analytics offers a framework in which students can reflect on the value of data for a particular problem, or the value of a method given particular data. The course will encourage students to reflect on capturing and creating data-driven value.
This course focuses on predictive analytics using machine learning: supervised learning (classification and regression). The course will also introduce unsupervised learning, including clustering and dimensionality reduction. Students will implement and tune individual machine learning algorithms (e.g., via Python’s scikit-learn library), and work with automated machine learning tools. In addition to lectures, the course will include cases and in-class workshops.
The course will use a variety of software, including: Continuum's Anaconda software suite, DataRobot, and MS Excel. This field is changing very rapidly and new tools are released monthly. To ensure that the course stays current, the instrutor may introduce new software. Such changes will not require any prerequisite changes or fundamentally alter the nature of the course.
During class, BI will provide the students with a limited-time-period academic license for DataRobot, which is to be used for academic purposes only.
Teaching will consist of a 2/3rd synchronous instruction, and 1/3rd asynchronous instruction. The asynchronous portion may involve pre-recorded digital lectures, reading or other individual or group learning activities.
Please note that while attendance is not compulsory in all courses, it is the student’s own responsibility to obtain any information provided in class.
Please note that students are responsible to ensure they have access to a computer, utilized software and to update and maintain their Python libraries and dependencies as required for the course. Access to an additional monitor for digital sessions and recorded video lessons is highly recommended, as the course involves executing programming commands during instruction.
This is a course with continuous assessment (several exam components) and one final exam code. Each exam component is graded by using points on a scale from 0-100. The components will be weighted together according to the information in the course description in order to calculate the final letter grade for the examination code (course). Students who fail to participate in one/some/all exam elements will get a lower grade or may fail the course. You will find detailed information about the point system and the cut off points with reference to the letter grades when the course start.
At re-sit all exam components must, as a main rule, be retaken during next scheduled course.
All courses in the Masters programme will assume that students have fulfilled the admission requirements for the programme. In addition, courses in second, third and/or fourth semester can have specific prerequisites and will assume that students have followed normal study progression. For double degree and exchange students, please note that equivalent courses are accepted.
Disclaimer
Deviations in teaching and exams may occur if external conditions or unforeseen events call for this.
The course assumes prior experience with: basic probability theory, traditional methods for analysis of continuous and categorical variables, and introductory Python programming, in particular with the Pandas and NumPy libraries and plotting basics.
| Assessments |
|---|
Exam category: Submission Form of assessment: Written submission Weight: 60 Grouping: Individual Duration: 72 Hour(s) Comment: Machine learning take-home exam. Exam code: GRA 41363 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
Exam category: Submission Form of assessment: Written submission Weight: 40 Grouping: Group (2 - 4) Duration: 3 Week(s) Comment: The students will be assigned to groups, when pedagogical reasons warrant it. Exam code: GRA 41363 Grading scale: Point scale leading to ECTS letter grade Resit: All components must, as a main rule, be retaken during next scheduled course |
| Activity | Duration | Comment |
|---|---|---|
Teaching | 36 Hour(s) | |
Examination | 24 Hour(s) | |
Prepare for teaching | 36 Hour(s) | |
Group work / Assignments | 32 Hour(s) | |
Student's own work with learning resources | 32 Hour(s) |
A course of 1 ECTS credit corresponds to a workload of 26-30 hours. Therefore a course of 6 ECTS credits corresponds to a workload of at least 160 hours.