Subject description MB120

This course introduces students to machine learning algorithms and techniques to deliver value for organizations. Students will gain advanced knowledge of key theories, frameworks, and concepts of data analytics and machine learning. They will acquire specialized analytical skills to develop data analytics projects to support decision-making and solve real-world business problems. On completion of this course, the students should understand the fundamental challenges of machine learning such as model assessment, selection, complexity, etc. and be able to implement machine learning using open source machine learning libraries.

Knowledge

The student...

• can understand the theoretical and practical relationship between big data analytics, machine learning and decision-making.
• is able to characterize the strengths and weaknesses of various machine learning approaches and algorithms.
• can understand how to formulate appropriate models of data analysis.

Skills

The student...

• can identify the characteristics of datasets and compare the trivial data and big data for various applications.
• is able to acquire knowledge and hands-on skills in data acquisition and preparation techniques.
• can plan, develop, employ and evaluate predictive techniques for various business problems.
• can carry out and manage business data analytics and data science projects, create and capitalize on data assets, and evaluate results.
• can use appropriate models of data analysis to develop solutions to business-related challenges.

General competence

The student...

• can design, implement, analyse and apply data mining, machine learning techniques and deep learning techniques on large datasets for real-world applications.
• can use appropriate models of analysis, assess the quality of input, derive insights from results, and investigate potential issues.
• can critically assess the ethical and legal issues in applying machine learning algorithms.

Master of Science in Information Systems - Business Analytics

The classes are taught in an interactive manner, with theoretical parts, intermingled with practice.

The course provides knowledge of various concepts, techniques and methods related to data mining, machine learning and deep learning approaches.

Participation in lectures and exercises - 48 hours

Self study - 80 hours

Independent preparation for presentation / discussion in class - 12 hours

Independent practice / lab work / practical work individually or in groups - 48 hours

Execution of and preparation for the exam - 12 hours

Recommended use of time in total - 200 hours

Data acquisition and scrapping tools, data preparation and processing tools, data analytics tools, relevant programming languages ​​like R and Python.

No compulsory activity in this course.

Exam type: Written home examination in groups (2-5 students)

Duration: 3 weeks

Grading scale: The Norwegian grading system using the graded scale A - F where A is the best grade, E is the lowest pass grade and F is fail

Weighting: 100 % of the overall grade

Support materials: All support materials are allowed

Grading scale: AF with A as the best grade and E as the lowest pass grade. F means fail.

Re-sit exam: Individual written home exam with a similar adapted assignment and 3 week duration

Written home examination in groups (2-3 students) Re-sit exam: Individual written home exam with a similar adapted assignment and 3-week duration. We reduced max size of the group to 3, mainly for pedagogical and assessment reasons. Reset exam duration made like main exam to have equal expectations.