Courses / Module

• Build and understanding of machine learning and artificial intelligence and their increasing role in modern business decision making;
• Understand and apply predictive analytics techniques such as regression, classification, nearest neighbour models, and deep learning on real-world datasets;
• Demonstrate knowledge of issues such as bias and over-fitting, as well as evaluation methods of predictive analytics approaches;
• Critically choose suitable predictive analytics technique for given business problems.

• This module is delivered through four days of module content. The majority of the learning occurs in preparation for the taught element, the lab work, and project work that flows from the taught element. This involves individual independent study, reading and research, coupled with group work. Lectures are important signposts to learning, but are only part of the educational process at third level. This module is structured around a series of lectures, lab work, readings, papers, case studies and video materials. All students will be encouraged to engage in class discussion in order to facilitate the formation of their critical judgments. Teaching methods will be constructivist, i.e. they will be delivered in the form of scaffolding for the learner who will be encouraged to construct their learning under guidance and with intensive participation. Please note that the number of hours for planned learning activities and independent student activities will vary from student to student, depending on their capability to absorb and apply concepts and tools, and write up work. A guidance for students is that one credit is a total of 25 to 30 hours of student effort. Thus a 5 credit module can vary in amount of student work between 125 hours and 150 hours. Our minimum expectation is as outlined below.

• Module Graded (numeric value) or Ungraded (Pass/Not Passed): Graded

• Pass standard: 40%
• Could the result of this module be capped? No
• Penalties: Students are expected to attend all lectures, seminars, and workshops. Where attendance falls below 75%, a student is deemed to have failed a module. Standard School of Business penalties apply for late submission of assignments; 10% is deducted for each 24 hours (or part thereof) that an assignment is submitted after the official submission deadline. Personal exceptional circumstances policy of the School of Business and University may apply.

• Are supplemental/resit registrations permitted? Yes
• Students are permitted to repeat an element of the module should they fall below the Pass standard in their first attempt.
• Is Supplemental Continuous Assessment registration permitted? Yes
• Is Supplemental Continuous Assessment capped? Yes

For a full timetable search, go to NUIM Timetable. Venue locations can be found here.