Module Description:

The aims and objectives of the module is to introduce comprehensively the concepts, techniques, as well as the applications of Artificial Intelligence (AI). The development of AI is tackled in the course along with its important aspects such as machine learning, search algorithms, natural language processing, and expert systems. AI is analysed in terms of its relevance in solving practical problems. The course will also cover the design and evaluation aspects of AI systems and will pay special attention to the ethical issues involved with their use. Students will be equipped with both theoretical and practical knowledge which will enable them pursue further education or employment in the AI industry. The course is not solely intended for students with a technical background; everyone is welcome. Students from both technical and non-technical schools will be able to appreciate the value of AI as it permeates in the modern industry (125 Hours).

Learning Outcomes:

Competences:

At the end of the module/unit the learner will have acquired the responsibility and autonomy to:

  • Balance all considerations with AI's possibilities and constraints.
  • Create satisfactory preliminary solutions that are AI-driven.
  • Engage in collaborative or independent work on assigned AI projects.
  • Focus on the progress of artificial intelligence or other emerging novel technologies thoroughly.
  • Determine the settings and methodologies most appropriate in relation to the application of AI technologies.
  • Contemplate the ethical issues which AI technologies pose in the societal domain.
  • Use AI technologies in tourism and hospitality industries: an interdisciplinary approach.

Knowledge:

At the end of the module/unit the learner will have been exposed to the following:

  • Advanced Topics of AI: Refers to the intelligent agents, search algorithms, machine learning, and Natural Language Processing (NLP).
  • AI Algorithms: Covers the search algorithms, decision trees, neural networks, and clustering within the scope of machine learning.
  • In machine learning methodologies, the three model types — supervised, unsupervised, and reinforcement learning — are incorporated.
  • Among other pertinent tools and libraries, AI programming and libraries include Scikit-learn, TensorFlow, Keras, and Python, among other suitable programming and library resources.
  • Bias, equity, privacy, and the socioeconomics of the AI workplace are a few of the societal and legal issues.

Skills:

At the end of the module/unit the learner will have acquired the following skills:

  • Programming Skills: Demonstrated ability to design algorithms, models, and applications using Python and applicable frameworks for Artificial Intelligence.
  • Model Development: Proficient in implementing and assessing numerous machine learning models of classification and regression and even clustering.
  • Data Analysis: Adept in the processes of analysing data to build a good AI model through data cleaning, feature extraction, and model evaluation.
  • Communication & Presentation: Able to disseminate and defend AI concepts, results, and solutions through professional reports and presentations.
  • Teamwork: Able to work in groups in the field of AI systems in the delivery of ideas, task allocation, and group work performance.

Core Topics and Subtopics :

Foundations of Artificial Intelligence

  • Objectives and Definition of AI
  • Categories of AI: Narrow, General, Super Intellect
  • AI: A Historico-Evolutionary Perspective
  • “Society 5.0”: AI Applications and Its Societal Impacts

Intelligent Agents

  • Agents Together with Their Environments
  • Rationality Alongside Autonomy
  • Classification of Agents As Reflexive, Goal-Driven, and Utility-Driven
  • PEAS Framework

Problem Solving and Search Techniques

  • Formulating the Problem
  • Uninformed Search Methods: BFS, DFS, and Uniform Cost
  • Informed Search Methods: Greedy and A*
  • Evaluation Functions and Heuristics

Adversarial Search and Game Playing

  • The Minimax Algorithm and Game Trees
  • Pruning According To Alpha and Beta
  • The Role Artificial Intelligence Plays In The Development Of Games
  • Restriction Of Search Space With Adversarial Technique

Knowledge Representation and Reasoning

  • Propositional and First-Order Logic
  • Methods of Inference
  • Semantic Networks and Ontologies
  • Expert Systems and Other Rule-Based Systems

Planning

  • Planning in the Classical Sense
  • Search-Based Planning
  • Partial Order Planning
  • Representation Using STRIPS

Introduction to Machine Learning

  • Supervised Learning: Classification and Regression
  • Unsupervised Learning: Clustering and Dimensionality Reduction
  • Fundamentals of Reinforcement Learning
  • Overfitting and Assessment of the Model

Natural Language Processing (NLP)

  • Preprocessing Textual Data and Its Tokenization
  • Language Processing Models and Syntax Analysis
  • Analysis of Sentiments and NER
  • Constructing Basic Conversational Agents

Robotics and Perception (Optional / Introductory)

  • Mapping and Perception
  • Control and Localization
  • AI in Vision Systems and Lidar Sensors
  • Basic Self-Directed Systems

Ethics, Safety, and Future of AI

  • Bias in AI and Issues of Equity
  • XAI or Explained AI
  • Concerns on Privacy and Surveillance
  • AGI and Risks to Existence
  • Development of AI in a Responsible Fashion

Weekly Breakdown / Session Plan

Week 1: Introduction to AI, History and Evolution

  • Objectives: Learn what artificial intelligence is, its objectives, its categories (Narrow, General), and its history.
  • Activities: Intro lecture, timeline creation, group discussion
  • Key Readings: Russell & Norvig, AI: A Modern Approach, Ch. 1
  • Activities:  Intro lecture, timeline creation, group discussion

Week 2: Intelligent Agents

  • Objectives: Learn about perception and decision making, as well as taking action in relation to agents and environments.
  • Activities: Design your own intelligent agent (scenario-based)
  • Key Readings: Russell & Norvig, Ch. 2
  • Activities: Design your own intelligent agent (scenario-based)

Week 3: Search in AI (Uninformed & Informed)

  • Objectives: Implement BFS, DFS, explain A*, and create heuristics.
  • Activities: Hands-on coding session and quizzes
  • Key Readings: Russell & Norvig, Ch. 3 & 4
  • Activities: Hands-on coding session and quizzes

Week 4: Game Playing and Adversarial Search

  • Objectives: Learn about game trees, and Minimax with Alpha-Beta pruning.
  • Activities: Build a Tic-Tac-Toe AI; compare strategies
  • Key Readings: Russell & Norvig, Ch. 5
  • Activities: Build a Tic-Tac-To AI; compare strategies

Week 5: Knowledge Representation and Reasoning

  • Objectives: Utilise Propositional and First Order Logic to knowledge representation.
  • Activities: Perform tasks involving logical reasoning.
  • Key Readings: Russell & Norvig, Ch. 7 & 8
  • Activities: Perform tasks involving logical reasoning.

Week 6: Planning

  • Objectives: Use Propositional and First Order logic to represent knowledge in a logical form.
  • Activities: AI plan generation (e.g., robot navigation)
  • Key Readings: Russell & Norvig, Ch. 10
  • Activities: AI plan generation(e.g., robot navigation)

Week 7: Machine Learning I: Supervised Learning

  • Objectives: Elaborate on regression, classification and overfitting.
  • Activities: Train a classifier using Scikit-learn
  • Key Readings: Mitchell, ML, Ch. 1–3; Russell & Norvig, Ch. 18
  • Activities: Train a classifier using Scikit-learn

Week 8: Machine Learning II: Unsupervised & Reinforcement Learning

  • Objectives: K-means, clustering, and Q-learning fundamentals.
  • Activities: Clustering activity and Q-learning maze
  • Key Readings: Russell & Norvig, Ch. 20; Sutton & Barto, RL: Intro, Ch. 1–2
  • Activities: Clustering activity and Q-learning maze

Week 9: Natural Language Processing (NLP)

  • Objectives: Gain insights in parsing, language models, and technologies of NLP.
  • Activities: Build a simple rule-based chatbot
  • Key Readings: Jurafsky & Martin, Speech & Language Processing, Ch. 1 & 2
  • Activities:  Build a simple rule-based chatbot

Week 10: Ethics & Future of AI

  • Objectives: Examine the issues of bias in AI and fairness, their influence on employment, and the safety of AGI.
  • Activities: Ethics debate, group presentations
  • Key Readings: Bostrom, Superintelligence, Intro; Russell & Norvig, Ch. 26
  • Activities: Ethics debate, group presentations

Reading Materials

Textbook

  • Title & Source: Artificial Intelligence: A Modern Approach
  • Author/Publisher: Stuart Russell and Peter Norvig, Pearson
  • Purpose: The main text for this course that teaches the basics of AI theories, algorithms, and core concepts.

  • Title & Source: Machine Learning
  • Author/Publisher: Tom Mitchell, McGraw-Hill
  • Purpose: Reviews important definitional concepts in both supervised and unsupervised learning.

  • Title & Source: Reinforcement Learning: An Introduction
  • Author/Publisher: Richard Sutton & Andrew Barto, MIT Press
  • Purpose: Provides the primary materials for understanding different reinforcement-learning methods.

  • Title & Source: Speech and Language Processing
  • Author/Publisher: Dan Jurafsky & James H. Martin, Pearson
  • Purpose: Introduces fundamental ideas for natural-language processing (NLP) and text processing, forming the basis for AI language systems.

 Book

  • Title & Source: Superintelligence: Paths, Dangers, Strategies
  • Author/Publisher: Nick Bostrom, Oxford University Press
  • Purpose: Research work focusing on the ethical and social impacts of artificial intelligence and new advanced AI technologies.

  • Title & Source: Pattern Recognition and Machine Learning
  • Author/Publisher: Christopher Bishop, Springer
  • Purpose: Supplementary reading for students wishing to study the mathematical and statistical techniques used in machine learning in greater depth.

Article

  • Title & Source: "Artificial Intelligence and the Risk of Bias"
  • Author/Publisher: Various Authors, Harvard Business Review
  • Purpose: Explores bias, fairness, and evaluation in machine-learning techniques.

  • Title & Source: “Charting Hospitality Industry Trends for 2025”
  • Author/Publisher: Bianca Lüthy; EHL Hospitality Insights
  • Purpose: Describes how personalization, technology, changing demographics, wellbeing, and “workation” trends redefine service and innovation in modern industries.

Research Paper

  • Title & Source: The Impact of AI on Employment
  • Author/Publisher: Brynjolfsson & McAfee, MIT
  • Purpose: Investigates how artificial intelligence will influence labor markets and future job opportunities.

Lecture Notes

  • Title & Source: Course-Specific Lecture Slides (Weekly Topics)
  • Author/Publisher: Instructor (TBA)
  • Purpose: Weekly slides outline key algorithms and examples for each session, supporting theory with practical illustrations.

Online Course

  • Title & Source: Introduction to AI (Coursera)
  • Author/Publisher: Stanford University, Andrew Ng
  • Purpose: A self-paced, supplementary course offering practical and conceptual reinforcement for AI fundamentals.