Module Code: H9PAI
Long Title Programming for Artificial Intelligence
Title Programming for AI
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 10
Module Coordinator: Arghir Moldovan
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. Experience lecturing in the field. May have industry experience also.

Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Analyse, compare, contrast and critically evaluate the characteristics of programming languages and environments commonly utilised for AI solutions implementation.
LO2 Critically assess the challenges associated with implementing AI solutions for various problems.
LO3 Critically assess methods and practices for software development to design and implement AI solutions requirements.
LO4 Evaluate, design and implement AI solutions by using key algorithms, data structures, and relevant programming languages.
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

Internal to the programme


Module Content & Assessment

Indicative Content
Introduction to Programming for AI
Module Introduction; History and evolution of programming languages used for AI; Programming types and paradigms (imperative, declarative, functional, logic, agent oriented programming, probabilistic programming, etc.); Overview of programming languages used for implementing AI solutions: general-purpose languages (e.g., Python), classical AI languages (e.g., Lisp), new generic AI programming languages (e.g., MIT Gen), deep probabilistic programming languages (e.g., Edward, Pyro).
AI Computation Challenges
Challenges associated with big data requirements of statistical AI (e.g., deep learning); Computation challenges (e.g., search space, time and space complexity); Parallelism for computational processes; Use of specialised/dedicated hardware to speed up computations (e.g., GPUs, Google TPUs, wafer-scale AI chips such as Cerebras CS-1, etc.); Distributed computing platforms; Brief overview AI services and APIs offered by public cloud providers (e.g., Amazon AWS, Microsoft Azure, Google Cloud Platform).
Overview of the programming language
Syntax and semantics, expressions and statements; Basic data types, conversion and coercion; Built in data structures (arrays, matrices, lists, etc.), indexing data structures; Program flow control and iteration.
Input/Output and Functions
Input/output data from structured/semi-structured file formats (csv, xml, json); Input data from the Internet (e.g., web scraping, web APIs); Defining functions; Lambdas for functional programming; Algorithm design.
Data Operations and Data Streaming
Dealing with missing values; Catching exceptions; Use of support libraries (e.g., Pandas, Numpy, dfply); Stream input sources, live data stream, window-based transformations, combination of batch and stream computations.
Database Programming – Relational Databases
Database system technologies; Programmatically connecting to databases; Create/Read/Update/Delete (CRUD) Operations; SQL Optimization, Indexing and Normalization.
Database Programming – NoSQL Databases, Data Lakes
NoSQL Overview and Data Models: Document Model, Key-Value Model, Column Family, Aggregates, Graph Model, Triple Stores; NoSQL Data Modelling Concepts; Query Languages for Data in NoSQL; NoSQL systems.
ETL, Data Pipelines and Data Wrangling
Data wrangling techniques; Developing programs for data processing activities (e.g., data extraction, cleaning, merging, aggregation, validation, analysis, reporting).
Ontology Engineering
Ontology definitions: domain ontology, concepts, instances and relationships. Overview of technologies for ontology engineering: Web Languages (e.g., HTML, XML and RDF), Metadata standards (e.g., Dublin Core), Ontology Language (e.g., OWL), Ontology Editor (e.g., Protégé), Reasoning language (e.g., SPARQL), reasoners (e.g., HermiT); Overview of Python packages for ontology-oriented programming (e.g., Owlready2, RDFlib, pyspaql, pronto, AllegroGraph).
Deep Learning
A brief introduction to deep learning concepts; Overview of deep learning frameworks (e.g., PyTorch, TensorFlow, Apache MXNet, Keras); Overview of public cloud AI services for deep learning (e.g., AWS Deep Learning AMIs, Google Cloud TPUs); Use of pre-trained models and cloud services for various example applications (e.g., regression, classification).
Natural Language Processing
Overview of NLP libraries and frameworks (e.g., NLTK); Overview of public cloud AI services for NLP, translation (e.g., Amazon Lex, Polly, etc.); Use of pre-trained Generalized Language Models for NLP applications (e.g., Google BERT, OpenAI GPT-2, etc.).
Image Processing
Overview of image processing libraries and frameworks (e.g., OpenCV); Overview of public cloud AI services for image and video recognition (e.g., Azure Face, AWS Rekognition, etc.); Use of pre-trained models for example applications (e.g., RetinaNET object detection).
Assessment Breakdown%


Full Time

Assessment Type: Formative 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: Continuous Assessment % of total: 30
Assessment Date: n/a Outcome addressed: 3,4
Non-Marked: No
Assessment Description:
This assessment will consist of practical tasks in the form of an in-class test or homework. This will assess learners’ knowledge and competences on core programming language concepts and operations covered so far.
Assessment Type: Project % of total: 70
Assessment Date: n/a Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
The terminal assessment will consist of a project that will evaluate all learning outcomes. Learners will have to develop a software application of their own choice utilising appropriate AI programming languages, algorithms, techniques, tools / frameworks / services. The final submission will consist of a written report and the implemented AI solution artefact.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.
Reassessment Description
The reassessment strategy for this module will consist of a project that will assess all learning outcomes. Students who fail the module will be afforded an opportunity to do the repeat project over the Summer months.

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 24 Per Semester 2.00
Tutorial Practical/Tutorial 24 Per Semester 2.00
Independent Learning Independent learning 202 Per Semester 16.83
Total Weekly Contact Hours 4.00
Workload: Blended
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom & Demonstrations 12 Per Semester 1.00
Tutorial Practical/Tutorial 12 Per Semester 1.00
Directed Learning Directed Learning 24 Per Semester 2.00
Independent Learning No Description 202 Per Semester 16.83
Total Weekly Contact Hours 4.00

Module Resources

Recommended Book Resources
  • Artasanchez, A. & Joshi, P. (2020). Artificial Intelligence with Python(2nd ed.). Packt Publishing. [ISBN: 978-1839219535]..
  • Rothman, D., Lamons, M., Kumar, R., Nagaraja, A., Amir Ziai, & Dixit, A. (2018). Python: Beginner's Guide to Artificial Intelligence. Packt Publishing. [ISBN: 978-1789957327]..
Supplementary Book Resources
  • McKinney, W. (2017). Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython(2nd ed.). O’Reilly Media. [ISBN: 978-1491957660]..
  • Jean-Baptiste, L. (2020) Ontologies with Python: Programming OWL 2.0 Ontologies with Python and Owlready2. Apress. [ISBN: 978-1484265529]..
  • Bonaccorso, G., Fandango, A., & Shanmugamani, R. (2018). Python: Advanced Guide to Artificial Intelligence. Packt Publishing. [ISBN: 978-1789957211]..
This module does not have any article/paper resources
Other Resources
Discussion Note: