Module MATH4352: Internship Project IV

Department: Mathematical Sciences

MATH4352: Internship Project IV

Prerequisites

• As relevant to the project topic

Corequisites

• None

Excluded Combination of Modules

• Level 4 project modules in any other Department

Aims

• • To allow a student to conduct a substantial piece of independent statistics and machine learning work, in an applied context and in collaboration with a non-expert third party, and to write up and present this work in an appropriate fashion. • This will further the student’s analytical, collaborative, and transferable skills, and their knowledge of the practice of statistics and machine learning, as well as their abilities in oral or written communication.

Content

• • Study and investigation of a sufficiently advanced statistics and machine learning topic arising from a real non-expert third-party problem, chosen by the project supervisor in agreement with the non-expert third party. • Application of the expertise acquired to the study and attempted solution of the real non-expert third-party problem, under the guidance of the supervisor and performed in collaboration with the third party.

Learning Outcomes

• • In-depth knowledge of the specific advanced statistics and machine learning techniques used in the project. • Knowledge of the practice of statistics and machine learning as an applied rather than academic discipline. • Understanding of the non-expert context within which the project is carried out and its relation to academic statistics and machine learning.

• Ability to: • Communicate with non-expert third parties in order to understand the problem that needs solving and throughout the process of its solution; • Express a real-world problem in a rigorous statistics and machine learning framework; • Handle real data sets and prepare them for analysis; • Devise suitable models and methods for the solution of the problem; • Evaluate and update these models and methods according to results obtained; • Communicate the academic content and the results of a study, both orally and in writing, in a manner appropriate for both an academic and a third-party audience.

• Ability to: • Work independently, using own initiative; • Collaborate effectively with others, including joint goal setting, discussion, and compromise; • Plan and manage time and tasks in the implementation of a substantial and time-limited project, taking into account own and others’ constraints.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

• • Guided self-study on the statistics and machine learning topic of the project. • Weekly meeting with supervisor to guide and support this self-study. • Guided work on the solution of the non-expert third-party problem giving rise to the project. • Regular meetings with supervisor and non-expert third party to quide and support this work. • Oral presentation of the statistics and machine learning content and the study and attempted solution of the non-expert third-party problem, demonstrating comprehension of the material, understanding of the problem, its attempted solution, and its context, and ability to communicate orally the academic content and the results of a study in a manner appropriate for both an academic and a third-party audience. • Written report on the statistics and machine learning content and the study and attempted solution of the non-expert third-party problem, demonstrating comprehension of the material, understanding of the problem, its attempted solution, and its context, and ability to communicate in writing the academic content and the results of a study in a manner appropriate for both an academic and a third-party audience.

Teaching Methods and Learning Hours

Summative Assessment

Formative Assessment:

Feedback on ongoing work shown to supervisor and third-party at meetings.

■ Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University