• Foundations in Data Analytics and AI

    These courses have been developed by Dr. Amelia Taylor, a senior lecturer in Computer Science at the Malawi University of Business and Applied Sciences. Some courses are designed to support her teaching modules but can be custom made to address specific training needs at your organisation. 

    If you want to know more about these courses please contact me at ataylor[at]mubas.ac.mw



Available courses

Artificial Intelligence

This course is an introduction to Artificial intelligence. 

Module Aim


The aim of this module is to provide the learner with a solid foundation of the
key concepts in artificial intelligence and knowledge-based systems.

Intended Learning Outcomes


On completion of this module the student should be able to:

  1. Have knowledge and understanding of key concepts of artificial
    intelligence such as intelligent agents, knowledge representation,
    reasoning under uncertainty and methods for machine learning.
  2. Analyse AI problems to determine appropriate methods of design,
    testing and evaluation.
    Formulate a research project involving AI concepts.
  3. Find and use AI tools to solve problems.

Indicative Content


a) Introduction to AI

i. history of AI,
ii. recent developments in AI,
iii. types of AI

b) Intelligent Agents

i. Agent Architecture and Hierarchical control,
ii. Uninformed and Informed Search,
iii. Local Search and Optimisation problems Game strategies

c) Introduction to Machine Learning

i. Learning problems,
ii. Decision Tree Learning,
iii. Instance-Based Learning,
iv. Bayesian Learning,
v. Artificial Neural Networks,
vi. Deep Learning and Reinforcement Learning,
vii. Support Vector Machines

d) Knowledge representation and planning

i. Propositional and First Order Logic,
ii. Natural Language Processing,
iii. Expert System

e) AI for Business Planning and Decision Making

i. Applications of AI Technologies: e.g., in Computer Vision, Machine
Translation, Education, Supply Chain, Medicine, Retail, Security,
etc.,
ii. Data and Data Sources
iii. Legal and ethical Issues in Artificial Intelligence.

Machine Learning

An introductory course in ML covering the following topics:

  1. Introduction to different paradigms of machine learning 
  2. Linear prediction, Regression 
  3. Linear Classification, Logistic Regression, Naïve Bayes 
  4. Support Vector Machines
  5. Unsupervised Learning, Clustering, k-means 
  6. Kernel methods
  7. Neural Networks, Backpropagation 
  8. Convolutional Neural Networks 
  9. Dimensionality Reduction, PCA 
  10. Basics of optimisation

Big Data

This course is an introduction to large-scale data analytics. It covers cluster computing software tools (e.g. Hadoop MapReduce, Apache Spark), programming techniques used by data scientists and mathematical and statistical models used in learning from big data. 

Computational Intelligence

This course covers four paradigms of computational intelligence: machine learning, genetic programming, swarm intelligence and fuzzy logic. 

Research Methodology and Scientific Writting

This course is addressed to students, mainly at MSc and PhD level, and those interested in conducting and publishing research. The course covers key aspects of the practice of research and was tailored to be taken by MSc students that study AI, Informatics, Data Analytics and Information Systems. 

The course is designed to cover the following topics:

a) The nature of research in AI, data science and information systems.
b) Theoretical foundations of different research methodologies and designs.
c) Qualitative and quantitative research strategies and methods.
d) Sampling techniques.
e) Research findings interpretation.
f) Literature review.
g) Referencing and citation.
h) Research proposal, dissertation and journal paper writing.

On completion of this module the student should be able to:
a) Be able to design and conduct their own research.
b) Be able to identify and utilise advantages and disadvantages of different
methods of data collection and different research designs.
c) Be able to effectively conduct a systematic literature review.
d) Be able to understand and interpret statistical and qualitative data analyses.
e) Be able to present, analyse and interpret research findings.
f) Identify the practical/ethical issues involved in conducting AI/DS/ IS research.
g) Practice publishing research papers in local, regional and international journals.

Forecasting and Data Analytics in Excel

Module Aim
This module aims to provide students with a thorough overall grasp of forecasting and modern data analytics skills and their applications to various domains. The module's examples are based on Excel.

Intended Learning Outcomes
On completion of this module the student should be able to: 

a) Explore the role of data and data analysis in modern applications
b) Explore the links between quantitative analysis and qualitative insights 
c) Leverage Excel formulas, charting and analytics packages to develop dashboards for data analysis and visualisation.

Data Structures and Algorithms

A course on data structures and algorithms covering foundational algorithms for searching, sorting and data structures such as queues, stacks, hash tables, graphs and trees.