1. Understand the fundamental concepts and approaches in AI.

• 1.1 Describe the key classical and modern approaches to AI.

• 1.2 Explain the significance of modern benchmarks for AI beyond the Turing test.

• 1.3 Explain the limitations of the Church Turing thesis in modern AI development.

• 1.4 Analyse the philosophical debates surrounding AI, including the Turing test and Searle’s Chinese Room argument.

• 1.5 Evaluate the principal achievements and shortcomings of AI.

• 1.6 Assess the future challenges and ethical considerations of AI development.

2. Be able to apply search algorithms in AI problem-solving.

• 2.1 Describe different types of search algorithms used in AI.

• 2.2 Explain the differences between finding satisfactory paths and optimal paths.

• 2.3 Critically analyse the effectiveness of heuristic search methods in problem-solving.

• 2.4 Evaluate the application of search algorithms in real-world AI problems.

• 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

• 3.1 Describe various methods of knowledge representation used in AI.

• 3.2 Explain the concepts of monotonic and non-monotonic reasoning.

• 3.3 Analyse the role of data-driven and goal driven reasoning in AI.

• 3.4 Evaluate the challenges of reasoning under uncertainty in AI.

• 3.5 Develop a reasoning system using knowledge representation techniques

4. Be able to apply machine learning techniques in AI

• 4.1 Describe and compare machine learning techniques, including Logistic Regression and Kernel Methods.

• 4.2 Explain the process of inductive and deductive learning in AI

• 4.3 Analyse the role of classification and regression trees in machine learning.

• 4.4 Critically evaluate the effectiveness of Perceptrons and introduce Support Vector Machines (SVMs).

• 4.5 Develop a machine learning model to solve a specific problem.

5. Understand the ethical and societal implications of AI

• 5.1 Describe the key ethical concerns associated with AI development and deployment.

• 5.2 Explain the importance of responsible AI development and governance.

• 5.3 Critically analyse the potential societal impacts of widespread AI adoption.

• 5.4 Evaluate the role of international collaboration in addressing global AI challenges.

• 5.5 Develop recommendations for ensuring ethical AI practices in a given context.

Introduction to Artificial Intelligence

Course Coverage:

• Definition and Scope of AI

• Historical Development of AI

• Key Milestones in AI Evolution

• Overview of Modern Data-Driven Algorithms

  • Introduction to data-driven approaches in AI, focusing on how they leverage large datasets to train models for various tasks.

  • Applications of data-driven algorithms in areas like computer vision, natural language processing, and robotics.

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:

• The Computational Metaphor

  • Understanding Computation in AI

  • The Role of Algorithms

• Church-Turing Thesis

  • Debating Its Relevance in Modern AI Development

• The Turing Test

  • History and Significance

  • Modern Interpretations and Critiques as Benchmark for AI

Philosophical Foundations of AI

Course Coverage:

• The Nature of Intelligence

  • Defining Intelligence in Machines

  • Comparing Human and Machine Intelligence

• Searle’s Chinese Room Argument and Its Implications for AI

  • Overview and Critique

  • Implications for AI Consciousness

Achievements and Limitations of AI

Course Coverage:

• Principal Achievements

  • Success Stories: Chess, Go, Autonomous Vehicles

  • AI in Everyday Applications: Speech Recognition, Recommendation Systems

• Shortcomings and Challenges

  • General AI vs. Narrow AI

  • The Challenge of AI Robustness and Reliability

Future Challenges and Ethical Considerations

Course Coverage:

• Ethical AI

  • Bias, Fairness, and Accountability in AI Systems

  • Transparency and Explainability

• Societal Impact of AI

  • The Future of Work and AI Automation

  • AI in Public Safety, Healthcare, and Education

• Global AI Challenges

  • Ensuring AI Safety and Security

  • International Collaboration and Regulation

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:

• Uninformed Search

  • Depth-First Search (DFS)

  • Breadth-First Search (BFS)

  • Iterative Deepening Search

• Informed (Heuristic) Search

  • Heuristics: Definition and Role

  • A* Algorithm and Its Variants

  • Greedy Best-First Search

Pathfinding in AI

Course Coverage:

• Satisfactory vs. Optimal Paths

  • Defining Satisfactory Paths: Practical Applications

  • Finding Optimal Paths: A* and Branch and Bound

• Dynamic Programming in AI

  • Overview and Applications

  • Bellman Equations and Shortest Path Problems

Heuristic Search in Problem-Solving

Course Coverage:

• Effectiveness of Heuristics

  • Designing Effective Heuristics

  • Trade-offs: Speed vs. Accuracy

• Real-World Applications

  • Robotics: Path Planning

  • Game AI: Strategy and Decision-Making

Practical Implementation

Course Coverage:

• Developing an AI Program

  • Step-by-Step Implementation of a Search Algorithm

  • Testing and Debugging the AI Program

  • Case Study: Solving a Maze Using Search Algorithms

Introduction to Knowledge Representation

Course Coverage:

• What is Knowledge Representation

  • Importance in AI

  • Types of Knowledge: Declarative, Procedural, etc.

Methods of Knowledge Representation

Course Coverage:

• Production Rules

  • Structure and Use Cases

  • Example: IF-THEN Rules in Expert Systems

• Semantic Networks

  • Nodes, Edges, and Relationships

  • Applications in AI: Concept Mapping

• Frames and Scripts

  • Frame-Based Knowledge Representation

  • Understanding Scripts: Sequential Events

• Description Logics

  • Concepts, Roles, and Individuals

  • Use in Ontologies and AI Systems

Monotonic and Non-Monotonic Reasoning

Course Coverage:

• Monotonic Reasoning

  • Principles and Characteristics

  • Applications in AI: Deductive Reasoning

• Non-Monotonic Reasoning

  • Principles and Characteristics

  • Handling Uncertainty: Default Logic, Circumscription

Reasoning Techniques in AI

Course Coverage:

• Data-Driven Reasoning

  • Forward Chaining: Process and Applications

  • Case Study: Medical Diagnosis Systems

• Goal-Driven Reasoning

  • Backward Chaining: Process and Applications

  • Case Study: Automated Planning Systems

Reasoning under Uncertainty

Course Coverage:

• Introduction to Uncertainty in AI

  • Probabilistic Reasoning: Concepts and Challenges

  • Conditional Independence and Causality

• Incorporating Probability Theory in AI Understanding

• Bayesian Networks

  • Structure and Functionality

  • Belief Propagation: Inference Techniques

• Linear Algebra and Its Role in AI

Introduction to Machine Learning

Course Coverage:

• What is Machine Learning?

  • Definitions and Importance in AI

  • Categories: Supervised, Unsupervised, Reinforcement Learning

Types of Machine Learning

Course Coverage:

• Supervised Learning

  • Overview: Labeled Data and Training

  • Algorithms: Linear Regression, Decision Trees, SVMs

• Unsupervised Learning

  • Overview: Unlabeled Data and Clustering

  • Algorithms: K-Means, PCA, DBSCAN

• Reinforcement Learning

  • Overview: Learning from Interaction

  • Algorithms: Q-Learning, SARSA, Deep Q Networks

Learning Techniques in AI

Course Coverage:

• Inductive Learning

  • Concepts and Process

  • Example: Decision Tree Learning (ID3 Algorithm)

• Deductive Learning

  • Concepts and Process

  • Applications in Knowledge-Based Systems

Classification and Regression Trees

Course Coverage:

• Understanding Decision Trees

  • Structure: Nodes, Branches, Leaves

  • Building Trees: Information Gain, Gini Index

• Introduction to Perceptrons and Multi-layer Perceptrons (MLPs)

  • Comparison of Perceptrons with SVMs

• Regression Trees

  • Continuous Value Prediction

  • Example: Housing Price Prediction

Bayesian Methods in Machine Learning

Course Coverage:

• Introduction to Bayesian Inference

  • Concepts: Prior, Likelihood, Posterior

  • Applications: Spam Filtering, Diagnosis

• Building Bayesian Networks

  • Structure and Learning

  • Example: Naive Bayes Classifier

Practical Implementation

Course Coverage:

• Developing a Machine Learning Model

  • Problem Selection and Data Preparation

  • Model Training and Validation

  • Case Study: Implementing a Classifier for Image Recognition

Ethical Concerns in AI

Course Coverage:

• Bias in AI Systems

  • Sources of Bias: Data, Algorithms, and Human Input

  • Impact of Bias: Fairness and Discrimination

• Transparency and Explainability

  • The Black Box Problem

  • Approaches to Explainable AI

Responsible AI Development

Course Coverage:

• Principles of Responsible AI

  • Fairness, Accountability, and Transparency (FAT)

  • Ethical AI Guidelines and Frameworks

• AI Governance and Policy

  • Role of Governments and Organizations

  • Case Study: GDPR and AI Compliance

Societal Impact of AI

Course Coverage:

• Economic Implications

  • AI and the Future of Work

  • Automation and Job Displacement

• AI in Critical Sectors

  • Healthcare: Diagnosis, Treatment Planning

  • Public Safety: Surveillance, Predictive Policing

• Public Perception and Trust in AI

  • Building Trust through Transparency

  • Addressing Public Concerns

Global Collaboration on AI Challenges

Course Coverage:

• International AI Initiatives

  • Partnerships: EU AI Strategy, US National AI Initiative

  • Research Collaborations and Global Standards

• Addressing AI’s Global Challenges

  • Ensuring Equitable Access to AI

  • Tackling Climate Change with AI Solutions

Developing Ethical AI Practices

Course Coverage:

• Formulating AI Ethics Policies

  • Policy Development: Steps and Considerations

  • Stakeholder Engagement and Implementation

• Case Study: Ethical AI in Industry

  • Analysis of AI Ethics in Tech Companies

  • Lessons Learned and Best Practices