Master Award in
Introduction to Artificial Intelligence
Master Award could transfer 20 credits and 50% tuition fees to Master’s programs of UKeU and/or Partner University.
Master Award in Introduction to Artificial Intelligence
The aim of this award is offers a comprehensive introduction to Artificial Intelligence, covering classical and modern approaches, including knowledge representation, reasoning, machine learning, neural networks, and search algorithms. It also addresses AI’s ethical implications and future challenges, providing foundational knowledge for advanced AI studies.
Could transfer 20 credits and 50% tuition fee to the Master of Artificial Intelligence of UKeU.
Learning Outcomes:
1. Understand the fundamental concepts and approaches in AI.
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1.1 Describe the key classical and modern approaches to AI.
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1.2 Explain the significance of modern benchmarks for AI beyond the Turing test.
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1.3 Explain the limitations of the Church Turing thesis in modern AI development.
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1.4 Analyse the philosophical debates surrounding AI, including the Turing test and Searle’s Chinese Room argument.
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1.5 Evaluate the principal achievements and shortcomings of AI.
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1.6 Assess the future challenges and ethical considerations of AI development.
2. Be able to apply search algorithms in AI problem-solving.
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2.1 Describe different types of search algorithms used in AI.
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2.2 Explain the differences between finding satisfactory paths and optimal paths.
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2.3 Critically analyse the effectiveness of heuristic search methods in problem-solving.
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2.4 Evaluate the application of search algorithms in real-world AI problems.
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2.5 Develop a simple AI program utilizing search algorithms to solve a given problem.
3. Understand the principles of knowledge representation and reasoning in AI
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3.1 Describe various methods of knowledge representation used in AI.
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3.2 Explain the concepts of monotonic and non-monotonic reasoning.
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3.3 Analyse the role of data-driven and goal driven reasoning in AI.
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3.4 Evaluate the challenges of reasoning under uncertainty in AI.
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3.5 Develop a reasoning system using knowledge representation techniques
4. Be able to apply machine learning techniques in AI
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4.1 Describe and compare machine learning techniques, including Logistic Regression and Kernel Methods.
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4.2 Explain the process of inductive and deductive learning in AI
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4.3 Analyse the role of classification and regression trees in machine learning.
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4.4 Critically evaluate the effectiveness of Perceptrons and introduce Support Vector Machines (SVMs).
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4.5 Develop a machine learning model to solve a specific problem.
5. Understand the ethical and societal implications of AI
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5.1 Describe the key ethical concerns associated with AI development and deployment.
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5.2 Explain the importance of responsible AI development and governance.
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5.3 Critically analyse the potential societal impacts of widespread AI adoption.
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5.4 Evaluate the role of international collaboration in addressing global AI challenges.
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5.5 Develop recommendations for ensuring ethical AI practices in a given context.
Topics:
Introduction to Artificial Intelligence
Course Coverage:
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Definition and Scope of AI
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Historical Development of AI
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Key Milestones in AI Evolution
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Overview of Modern Data-Driven Algorithms
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Introduction to data-driven approaches in AI, focusing on how they leverage large datasets to train models for various tasks.
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Applications of data-driven algorithms in areas like computer vision, natural language processing, and robotics.
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Approaches to AI
Course Coverage:
- Strong and Weak AI
- Definitions and Differences
- Examples of Strong AI vs. Weak AI
- Symbolic and Sub-Symbolic AI
- Characteristics of Symbolic AI (Rule-Based Systems)
- Characteristics of Sub-Symbolic AI (Neural Networks and Deep Learning Models)
- Knowledge-Based and Data-Driven AI
- Overview of Data-Driven Algorithms: Machine Learning and Deep Learning
- Basics of Deep Learning: Introduction to neural networks, the concept of layers (input, hidden, output), and the use of non-linear activation functions.
- Comparison and Integration of Knowledge-Based Systems and Data-Driven Approaches.
Computational Theories in AI
Course Coverage:
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The Computational Metaphor
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Understanding Computation in AI
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The Role of Algorithms
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Church-Turing Thesis
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Debating Its Relevance in Modern AI Development
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The Turing Test
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History and Significance
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Modern Interpretations and Critiques as Benchmark for AI
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Philosophical Foundations of AI
Course Coverage:
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The Nature of Intelligence
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Defining Intelligence in Machines
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Comparing Human and Machine Intelligence
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Searle’s Chinese Room Argument and Its Implications for AI
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Overview and Critique
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Implications for AI Consciousness
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Achievements and Limitations of AI
Course Coverage:
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Principal Achievements
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Success Stories: Chess, Go, Autonomous Vehicles
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AI in Everyday Applications: Speech Recognition, Recommendation Systems
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Shortcomings and Challenges
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General AI vs. Narrow AI
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The Challenge of AI Robustness and Reliability
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Future Challenges and Ethical Considerations
Course Coverage:
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Ethical AI
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Bias, Fairness, and Accountability in AI Systems
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Transparency and Explainability
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Societal Impact of AI
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The Future of Work and AI Automation
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AI in Public Safety, Healthcare, and Education
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Global AI Challenges
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Ensuring AI Safety and Security
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International Collaboration and Regulation
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Introduction to Search Algorithms
Course Coverage:
- Overview of Search in AI
- Defining Search Problems
- Importance of Search in AI Applications
Types of Search Algorithms
Course Coverage:
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Uninformed Search
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Depth-First Search (DFS)
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Breadth-First Search (BFS)
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Iterative Deepening Search
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Informed (Heuristic) Search
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Heuristics: Definition and Role
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A* Algorithm and Its Variants
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Greedy Best-First Search
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Pathfinding in AI
Course Coverage:
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Satisfactory vs. Optimal Paths
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Defining Satisfactory Paths: Practical Applications
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Finding Optimal Paths: A* and Branch and Bound
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Dynamic Programming in AI
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Overview and Applications
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Bellman Equations and Shortest Path Problems
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Heuristic Search in Problem-Solving
Course Coverage:
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Effectiveness of Heuristics
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Designing Effective Heuristics
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Trade-offs: Speed vs. Accuracy
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Real-World Applications
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Robotics: Path Planning
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Game AI: Strategy and Decision-Making
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Practical Implementation
Course Coverage:
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Developing an AI Program
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Step-by-Step Implementation of a Search Algorithm
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Testing and Debugging the AI Program
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Case Study: Solving a Maze Using Search Algorithms
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Introduction to Knowledge Representation
Course Coverage:
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What is Knowledge Representation
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Importance in AI
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Types of Knowledge: Declarative, Procedural, etc.
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Methods of Knowledge Representation
Course Coverage:
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Production Rules
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Structure and Use Cases
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Example: IF-THEN Rules in Expert Systems
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Semantic Networks
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Nodes, Edges, and Relationships
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Applications in AI: Concept Mapping
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Frames and Scripts
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Frame-Based Knowledge Representation
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Understanding Scripts: Sequential Events
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Description Logics
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Concepts, Roles, and Individuals
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Use in Ontologies and AI Systems
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Monotonic and Non-Monotonic Reasoning
Course Coverage:
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Monotonic Reasoning
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Principles and Characteristics
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Applications in AI: Deductive Reasoning
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Non-Monotonic Reasoning
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Principles and Characteristics
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Handling Uncertainty: Default Logic, Circumscription
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Reasoning Techniques in AI
Course Coverage:
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Data-Driven Reasoning
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Forward Chaining: Process and Applications
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Case Study: Medical Diagnosis Systems
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Goal-Driven Reasoning
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Backward Chaining: Process and Applications
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Case Study: Automated Planning Systems
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Reasoning under Uncertainty
Course Coverage:
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Introduction to Uncertainty in AI
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Probabilistic Reasoning: Concepts and Challenges
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Conditional Independence and Causality
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Incorporating Probability Theory in AI Understanding
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Bayesian Networks
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Structure and Functionality
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Belief Propagation: Inference Techniques
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Linear Algebra and Its Role in AI
Introduction to Machine Learning
Course Coverage:
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What is Machine Learning?
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Definitions and Importance in AI
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Categories: Supervised, Unsupervised, Reinforcement Learning
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Types of Machine Learning
Course Coverage:
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Supervised Learning
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Overview: Labeled Data and Training
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Algorithms: Linear Regression, Decision Trees, SVMs
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Unsupervised Learning
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Overview: Unlabeled Data and Clustering
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Algorithms: K-Means, PCA, DBSCAN
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Reinforcement Learning
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Overview: Learning from Interaction
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Algorithms: Q-Learning, SARSA, Deep Q Networks
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Learning Techniques in AI
Course Coverage:
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Inductive Learning
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Concepts and Process
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Example: Decision Tree Learning (ID3 Algorithm)
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Deductive Learning
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Concepts and Process
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Applications in Knowledge-Based Systems
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Classification and Regression Trees
Course Coverage:
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Understanding Decision Trees
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Structure: Nodes, Branches, Leaves
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Building Trees: Information Gain, Gini Index
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Introduction to Perceptrons and Multi-layer Perceptrons (MLPs)
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Comparison of Perceptrons with SVMs
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Regression Trees
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Continuous Value Prediction
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Example: Housing Price Prediction
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Bayesian Methods in Machine Learning
Course Coverage:
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Introduction to Bayesian Inference
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Concepts: Prior, Likelihood, Posterior
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Applications: Spam Filtering, Diagnosis
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Building Bayesian Networks
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Structure and Learning
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Example: Naive Bayes Classifier
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Practical Implementation
Course Coverage:
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Developing a Machine Learning Model
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Problem Selection and Data Preparation
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Model Training and Validation
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Case Study: Implementing a Classifier for Image Recognition
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Ethical Concerns in AI
Course Coverage:
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Bias in AI Systems
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Sources of Bias: Data, Algorithms, and Human Input
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Impact of Bias: Fairness and Discrimination
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Transparency and Explainability
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The Black Box Problem
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Approaches to Explainable AI
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Responsible AI Development
Course Coverage:
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Principles of Responsible AI
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Fairness, Accountability, and Transparency (FAT)
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Ethical AI Guidelines and Frameworks
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AI Governance and Policy
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Role of Governments and Organizations
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Case Study: GDPR and AI Compliance
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Societal Impact of AI
Course Coverage:
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Economic Implications
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AI and the Future of Work
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Automation and Job Displacement
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AI in Critical Sectors
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Healthcare: Diagnosis, Treatment Planning
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Public Safety: Surveillance, Predictive Policing
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Public Perception and Trust in AI
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Building Trust through Transparency
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Addressing Public Concerns
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Global Collaboration on AI Challenges
Course Coverage:
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International AI Initiatives
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Partnerships: EU AI Strategy, US National AI Initiative
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Research Collaborations and Global Standards
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Addressing AI’s Global Challenges
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Ensuring Equitable Access to AI
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Tackling Climate Change with AI Solutions
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Developing Ethical AI Practices
Course Coverage:
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Formulating AI Ethics Policies
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Policy Development: Steps and Considerations
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Stakeholder Engagement and Implementation
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Case Study: Ethical AI in Industry
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Analysis of AI Ethics in Tech Companies
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Lessons Learned and Best Practices
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Indicative reading list
Core texts:
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Russell, S., & Norvig, P. (2020). Artificial Intelligence: A Modern Approach. Pearson.
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Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press.
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Poole, D., & Mackworth, A. (2017). Artificial Intelligence: Foundations of Computational Agents. Cambridge University Press.
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Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.
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Pearl, J. (2009). Causality: Models, Reasoning, and Inference. Cambridge University Press.
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Bishop, C. M. (2006). Pattern Recognition and Machine Learning. Springer.
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Chollet, F. (2018). Deep Learning with Python. Manning Publications.
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Koller, D., & Friedman, N. (2009). Probabilistic Graphical Models: Principles and Techniques. MIT Press.
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Russell, S., & Dewey, D. (2015). Maximizing Intelligence: AI Safety and Ethics. Oxford University Press.
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Sutton, R. S., & Barto, A. G. (2018). Reinforcement Learning: An Introduction. MIT Press.
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Floridi, L. (2014). The Ethics of Information. Oxford University Press.
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Zhang, Y., et al. (2019). Graph Neural Networks: A Review of Methods and Applications. arXiv:1812.08434
Additional reading:
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Harvard Business School Publishing: www.hbsp.harvard.edu
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Institute of Electrical and Electronics Engineers (IEEE): www.ieee.org
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Association for the Advancement of Artificial Intelligence (AAAI): www.aaai.org
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The Alan Turing Institute: www.turing.ac.uk
Entry requirements
To enroll The Master Award, the learner must possess:
- Graduated with a Bachelor’s degree from an accredited university or achieved a Level 6 Diploma according to the European Qualifications;
- For a degree from non-recognized universities; The learner should have followed Accreditation of Prior Experiential Learning for Qualifications (APEL.Q) policy of MI Swiss and/or University Partners;
- Learners must be over 21 years old.
English requirements
If a learner is not from a predominantly English-speaking country, proof of English language proficiency must be provided.
- Common European Framework of Reference (CEFR) level B2 or equivalent;
- Or A minimum TOEFL score of 101 or IELTS 5.5; Reading and Writing must be at 5.5 or equivalent.
The UKeU reserves the highest decision-making authority regarding admissions and may accept or reject applicants following a thorough review of each applicant’s profile, ensuring that only those capable of benefiting from the course are admitted. Qualifications from diploma mills or fake universities/institutions will not be accepted by UKeU and/or Partner University.
After graduating with Master Award, learners receive all certified documents from the UKeU.
Certified Documents:
- e-Certificate from the UK eUni Worldwide (UKeU);
- Hard copy certificate from UK eUni Worldwide (UKeU) (Optional);
- Accreditation of Prior Experiential Learning for Qualifications (APEL.Q) certified from UKeU for credit and tuition fee transfer.
Because the course is accredited and recognized, learners can easily use their qualifications in the workplace and enjoy many opportunities for career advancement. In addition, if you wish to pursue a degree from UKeU and/or a Partner University, all credits and 50% paid tuition fees can be transferred.
The UKeU’ Master Award means:
UKeU Master Award is the award at the master level and is equivalent to:
- Level 7 certificate of Regulated Qualifications Framework (RQF) of UK
- Level 10 certificate of Scottish Credit and Qualifications Framework (SCQF)
- Level 7 certificate of Credit and Qualifications Framework (CQFW)
- Level 7 certificate of European Qualifications Framework (EQF)
- Level 9 certificates of the Australian Qualifications Framework (AQF)
- Level 7 certificate of ASEAN Qualifications Reference Framework (AQRF)
- Level 9 certificate of the African Continental Qualifications Framework (ACQF)
Learners can transfer all credits and 50% of their tuition fees when enrolling in UKeU and/or Partner University academic programs if they wish to pursue an academic degree.
Credits transfer:
Learners can transfer 20 credits from the Master Award course when participating in the Master program. Please see the credit transfer policy HERE.
Tuition fee transfer:
When enrolling in the Master program, graduates from the Master Award will receive a fee reduction equal to 50% of the tuition fees paid for the Master Award. Please refer to the tuition fee transfer policy HERE.
The UKeU Micro Degree course allows learners to transfer credits and 50% of their tuition fees toward full degree programs offered by UKeU and/or Partner University. UKeU reserves the right to limit admissions once enrollment exceeds the set quotas.
Apply Policy:
- To participate in the UKeU Micro Degree course, learners need to meet the entry criteria corresponding to each level. Please see the “Entry” tab for more details.
- UKeU will not accept applicants whose entry qualifications are from fake universities or institutions that are not accredited.
- For Master Award courses, if an entry bachelor is unavailable, learners must demonstrate a minimum of 5 years of work experience in the relevant field. Please note that a bachelor’s degree is required for the Master’s program at UKeU and Partner University so that you could study Master Award but could not move to the Master’s program of UKeU and/or Partner University.
- English is not a mandatory entry requirement for Micro Degree course, but candidates need to ensure that English is used in reading documents, listening to lectures, and doing assignments. Applicants should note that English is a mandatory requirement when switching to an academic program at UKeU and Partner University.
Apply Process:
- Step 1: To request a consultation for a course that best suits your needs, please email support@ukeu.uk. Our admissions department will contact you to guide you through the required documentation and the next steps in the application process.
- Step 2: Once your application documents are approved and the application fee is paid, UKeU will issue a Letter of Acceptance (LOA). You will then follow the provided instructions, including payment of the tuition fee.
- Step 3: After the tuition fee is paid, UKeU will issue a confirmation letter, provide your login details for the e-learning system, and send you all relevant documents.
- At this point, you have officially become a UKeU student. Welcome, and enjoy your learning journey!
The UKeU Micro Degree course is fully online, allowing you to study anytime and anywhere. You also have the option to attend live classes with UKeU. Final exams will be uploaded to the system and assessed by the UKeU academic board. Learners are required to submit assignments on time; failure to do so will require payment of a resit fee (with up to two attempts allowed). Continued non-compliance on a third occasion will result in being considered as having discontinued the course, and tuition fees will not be refunded.
Pricing Plans
Take advantage of one of our non-profit professional certified courses with favorable terms for your personal growing carreers.
- Live Class (Optional)
- Full online videos
- e-Books
- Self study contents
- Online tutor videos
- Assignment guide
- e-Certificate
- Hard copy certificate from UKeU and/or Partner Universities
- APEL.Q certified from UKeU for credit and tuition fee transfer
- Deliver hard copy certificate and all certified documents to your home
- Transfer full credits & 50% tuition fees to equivalent academic programs
- Opportunity to get scholarships when becoming Partner Universities' international students
UKeU MICRO DEGREE
Contact us
If you interested this micro credential course, please feel free to contact with us! Please note that this program is a not for profit and learning with full online model.