NCHNAP784 Advanced Statistical and Mathematical Methods Course Descriptor

Course Summary

This course provides the statistical and mathematical principles and properties behind artificial intelligence and machine learning problems. The course focuses on aspects of advanced mathematics and statistics relevant to artificial intelligence (AI) and machine learning that are used routinely in organisational settings. Learners will cover hypothesis testing, time series analysis, linear models and logistic regression, Monte Carlo, bootstrap, causal inference false discovery rates, permutation tests, regression-based local fitting and modelling methods. Learners will understand the relationship between mathematical principles and core techniques in AI and data science. Performance and accuracy metrics will be evaluated for model validation. The course will be illustrated with practical examples in Python and R.

Course Aims

• Train learners in the statistical and mathematical principles and practice behind AI and machine learning.
• Give learners the mathematical and statistical tools to develop scientifically valid and robust AI and machine learning predictive models.
• Train learners in aspects of statistics and mathematics aligned to AI and machine learning problems in the context of business.

Learning Outcomes

On successful completion of the course, learners will be able to:

Knowledge and Understanding

Subject Specific Skills

Transferable and Professional Skills

Teaching and Learning

This is an e-learning course, taught throughout the year. 

This course can be offered as a standalone short course.

Teaching and learning strategies for this course will include: 

• Online learning
• Online discussion groups
• Online assessment

Course information and supplementary materials will be available on the University’s Virtual Learning Environment (VLE).

Learners are required to attend and participate in all the formal and timetabled sessions for this course. Learners are also expected to manage their self-directed learning and independent study in support of the course.

The course learning and teaching hours will be structured as follows:

• Off-the-job learning and teaching (6 days x 7 hours) = 42 hours
• On-the-job learning (12 days x 7 hours) = 84 hours (e.g. 2 days per week for 6 weeks)
• Private study (4 hours per week) = 24 hours

Total = 150 hours

Workplace assignments (see below) will be completed as part of on-the-job learning.

Assessment

Formative

Learners will be formatively assessed during the course by means of set assignments. These will not count towards the final degree but will provide learners with developmental feedback. 

Summative

*AE1 uses linear marking

Feedback

Learners will receive formal feedback in a variety of ways: written (via email or VLE correspondence) and indirectly through online discussion groups. Regular tri-partite reviews between the learner (apprentice), their apprenticeship advisor (provider) and workplace line manager (employer) formally monitor and evaluate the learner’s progress. 

Indicative Reading

Note: Comprehensive and current reading lists for courses are produced annually in the Course Syllabus or other documentation provided to learners; the indicative reading list provided below is used as part of the approval/modification process only.

Books

Unpingco, J. (2019). Python for Probability, Statistics, and Machine Learning. Cham : Springer

Montgomery, D., Peck, E.A., and Vining, G.G. (2013). Introduction to Linear Regression Analysis. Chichester : Wiley

Montgomery, D., Jennings, C.L. and Kulahci, M. (2015). Introduction to Time Series Analysis and Forecasting. Hoboken, New Jersey : Wiley

Journals

Learners are encouraged to read material from relevant journals on mathematical and statistical principles of AI and machine learning as directed by their course leader.

Electronic Resources

Learners are encouraged to consult relevant websites on mathematical and statistical principles of AI and machine learning.

Indicative Topics

Learners will study the following topics: 

• Linear and logistical regression
• Time Series Analysis
• Monte Carlo