NCHNAP785 Applied Machine Intelligence Course Descriptor

Course Summary

This course covers the theoretical foundations and practical application of artificial intelligence (AI) and machine learning. Learners will explore how to use AI and machine learning methodologies such as data mining, supervised/unsupervised machine learning, deep learning, natural language processing and machine vision to meet business objectives. Performance and accuracy metrics for model validation, optimisation techniques, sources of error and bias will be examined. Learners will gain hands-on experience of AI and machine learning using Python, R and modern machine libraries such as TensorFlow. Emerging technologies and trends such as multi-agent reinforcement learning will be introduced and evaluated. 

Course Aims

• Train learners in the practical application of AI and machine learning methodologies.
• Train learners in model validation, error, bias, optimisation techniques and metrics.
• Allow learners to explore new and emerging AI and machine learning methodologies.

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 (12 days x 7 hours) = 84 hours
• On-the-job learning (24 days x 7 hours) = 168 hours (e.g. 2 days per week for 12 weeks)
• Private study (4 hours per week) = 48 hours

Total = 300 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

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

Müller, A. and Guido, S. (2017). Introduction to Machine Learning with Python: A Guide for Data Scientists. Sebastopol, California : O’Reilly

Russell, S. and Norvig, P. (2003). Artificial Intelligence: A Modern Approach. Upper Saddle River, New Jersey : Prentice Hall/Pearson

Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow. O’Reilly

Journals

Learners are encouraged to read material from relevant journals on AI and machine learning as directed by their Course Leader.

Electronic Resources

Learners are encouraged to consult relevant websites on AI and machine learning.

Indicative Topics

Learners will study the following topics: 

• AI and machine learning
• Data mining
• Simulation