Module Code: H7AI
Long Title Artificial Intelligence
Title Artificial Intelligence
Module Level: LEVEL 7
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
Module Coordinator: KEITH MAYCOCK
Module Author: Arghir Moldovan
Departments: School of Computing
Specifications of the qualifications and experience required of staff


MSc and/or PhD degree in computer science or cognate discipline. May have industry experience also.

 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Describe the theory and concepts underpinning Artificial Intelligence (AI) and outline the historical evolution of AI.
LO2 Apply the technical and practical skills for constructing algorithms used in various real world applications such as natural language processing.
LO3 Demonstrate the use of structures for knowledge representation and logical reasoning systems while solving practical AI problems.
LO4 Evaluate the architecture of intelligent agents to solve real world problems.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

Learners should have attained the knowledge, skills and competence gained from stage 2 of the BSc (Hons) in Data Science
 

Module Content & Assessment

Indicative Content
Introduction to Artificial Intelligence
Foundations of AI: philosophy, maths, psychology, computing, linguistics, logic, probability theory. Historical evolution of the field. Weak vs Strong AI. Ethical implications of AI
Agents
Precepts, actions, goals, environment. Rational agents. Environments. Agent functions and programs. Simple reflex agents. Reflex agents with state. Goal based agents. Utility based agents
Search-Based Problem Solving
Utility based agents. Performance. State space search. Uninformed Search strategies: Uniform Cost, Dept-First, Depth-Limited, Iterative Deepening
Search-Based Problem Solving
Informed Search strategies: Greedy Best First Search, A* Search; Heuristic functions; Iterative Improvement algorithms • Hill climbing & Simulated Annealing
Reasoning
Propositional Logic. First Order Logic. Inference in First Order Logic
Knowledge representation
Ontological Engineering. Categories, objects and events. Semantic networks
Bayesian Networks
Quantifying Uncertainty. Bayes’ Rules and its use
Bayesian Networks
Probabilistic Reasoning; The semantics of Bayesian Networks; Conditional distributions and efficient representation of them; Inference in Bayesian Networks; Dynamic Bayesian Network
Natural Language Processing
Language models. Retrieval, extraction and classification for natural language processing
Natural Language Processing
Natural language for communication (phrase structures, syntactic analysis, augmented, grammars and semantic interpretation, machine translation, speech recognition) Practical problems, for example using systematic functional linguistics for the identification of latent dehumanisation
Recommender Systems
Recommender systems introduction and examples . Basic models of recommender systems
Recommender Systems
AI techniques for recommender systems; Recommender systems challenges: scalability, sparsity, cold-start, etc
Assessment Breakdown%
Coursework40.00%
End of Module Assessment60.00%

Assessments

Full Time

Coursework
Assessment Type: Continuous Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
Assessment Type: Project (0050) % of total: 40
Assessment Date: n/a Outcome addressed: 3,4
Non-Marked: No
Assessment Description:
Project where the students need to implement AI into an application of their choice (e.g., chess game, chat bot, etc.). This project would built on students’ previous skills in programming (e.g. Python), and machine learning.
Assessment Type: Easter Examination % of total: 60
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
Written examination held during final terminal exams examining all learning outcomes
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
Reassessment Description
The repeat strategy for this module is an examination. Students will be afforded an opportunity to repeat the examination at specified times throughout the year and all learning outcomes will be assessed in the repeat exam.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom & Demonstrations (hours) 24 Per Semester 2.00
Tutorial Other hours (Practical/Tutorial) 24 Per Semester 2.00
Independent Learning Independent learning (hours) 202 Per Semester 16.83
Total Weekly Contact Hours 4.00
 

Module Resources

Recommended Book Resources
  • Russell, S., and Norvig, P.. (2010), Artificial Intelligence: A Modern Approach (3rd ed), Pearson.
  • Koski, T. & Noble, J.. (2009), Bayesian Networks: An Introduction, Wiley.
  • Bird, S., Klein, E. & Loper, E.. (2009), Natural Language Processing with Python: Analyzing Text with the Natural Language Toolkit, O’Reilly.
  • Jannach, D., Zanker, M., Felfernig, A. & Friedrich, G.. (2010), Recommender Systems: An Introduction, Cambridge University Press.
Supplementary Book Resources
  • Funge, J. D.. (2004), Artificial Intelligence for Computer Games: An Introduction, CRC Press.
Supplementary Article/Paper Resources
This module does not have any other resources
Discussion Note: