This module introduces the foundational concepts of Artificial Intelligence (AI). It covers the history, significance, and various branches of AI, including how it has evolved over time. Key topics include:
By the end of this module, students will understand the basic principles guiding AI and its impact on technology and society.
This module introduces the foundational concepts of Artificial Intelligence (AI). It covers the history, significance, and various branches of AI, including how it has evolved over time. Key topics include:
By the end of this module, students will understand the basic principles guiding AI and its impact on technology and society.
This module focuses on problem-solving techniques used in AI through search strategies. It emphasizes the importance of search in AI and the various search algorithms. Key topics include:
Students will learn how to apply these techniques to effectively navigate and solve complex problems.
This module delves into the concept of searching with costs, where the efficiency of the search process is evaluated based on the cost associated with actions. It covers:
By the end of this module, students will appreciate how cost considerations influence search strategies in AI.
The Informed State Space Search module emphasizes advanced search techniques that utilize heuristics to improve efficiency. Key topics include:
Students will engage in exercises to apply heuristic methods to enhance search performance.
This module focuses on heuristic search techniques, specifically the A* algorithm and its variations. It covers:
Students will implement the A* algorithm and explore its effectiveness in solving complex search problems.
This module introduces problem reduction search techniques, specifically focusing on AND/OR graphs. The content includes:
Students will learn to apply these techniques to optimize search processes effectively.
This module examines the strategies used to search game trees, which are essential in AI applications related to gaming. Key topics include:
Students will practice implementing these strategies in various game scenarios.
This module covers knowledge-based systems, focusing on logic and deduction as fundamental components. The topics include:
Students will learn how to design and implement knowledge-based systems using logical frameworks.
This module focuses on First Order Logic (FOL), a crucial aspect of knowledge representation in AI. Key topics include:
Students will practice formulating statements and arguments using First Order Logic.
This module covers inference in First Order Logic (FOL), focusing on techniques used to derive conclusions from FOL statements. Key topics include:
Students will engage in exercises to practice inference techniques and develop their reasoning skills.
This module introduces resolution and refutation proofs in First Order Logic, emphasizing their significance in AI reasoning. Key topics include:
Students will learn to apply these methods to solve logical problems effectively.
This module further explores resolution and refutation proofs, providing deeper insights into their application in logical reasoning. Key topics include:
Students will engage in practical exercises to enhance their understanding and application of resolution techniques.
This module introduces Logic Programming with Prolog, highlighting its role in AI. Key topics covered include:
Students will learn to write basic Prolog programs and explore their applications in solving logical problems.
This module focuses on Prolog programming, enhancing students' skills in writing and executing Prolog code. Key topics include:
Students will engage in hands-on programming exercises to solidify their understanding of Prolog.
This module covers exercising control in Prolog programming, focusing on strategies to manage flow and execution. Key topics include:
Students will practice implementing control strategies in Prolog to handle complex logical scenarios.
This module discusses additional topics relevant to AI, providing students with a broader perspective of the field. Key areas include:
Students will engage in discussions and projects addressing these vital areas of AI.
This module introduces planning in AI, focusing on the fundamentals of how AI systems can develop plans to achieve goals. Key topics include:
Students will study various planning methodologies and their effectiveness in different scenarios.
This module examines partial order planning, a sophisticated approach to planning in AI. Key topics include:
Students will learn to apply partial order planning techniques to optimize decision-making in complex scenarios.
This module focuses on GraphPLAN and SATPlan, two important algorithms in AI planning. Key topics include:
Students will practice using these algorithms in various planning scenarios to enhance their understanding.
This module further explores SATPlan, focusing on its detailed mechanics and applications in AI planning. Key topics include:
Students will engage in exercises to apply SATPlan techniques in real-world scenarios.
This module introduces reasoning under uncertainty, a crucial aspect of AI, focusing on how AI systems can make decisions when faced with uncertain information. Key topics include:
Students will learn to apply various techniques to handle uncertain data effectively.
This module focuses on Bayesian Networks, a powerful tool for reasoning under uncertainty. Key topics include:
Students will practice building and analyzing Bayesian Networks in different scenarios.
This module covers reasoning with Bayesian Networks, focusing on techniques for inferring probabilities and making decisions based on network structures. Key topics include:
Students will engage in exercises to apply these reasoning techniques in practical situations.
This module continues the exploration of reasoning with Bayesian Networks, emphasizing advanced techniques and applications. Key topics include:
Students will analyze case studies and apply advanced techniques to deepen their understanding of Bayesian Networks.
This module focuses on Learning: Neural Networks, a fundamental topic in artificial intelligence. Participants will delve into the architecture and functioning of neural networks, which are inspired by the human brain's structure.
Key points covered include:
By the end of this module, learners will have a solid grasp of how neural networks can be utilized for complex problem-solving tasks in AI.
This module discusses the issues surrounding reasoning under uncertainty in artificial intelligence. As many real-world applications involve uncertainty, it is crucial to understand how AI systems can make decisions based on incomplete or ambiguous information.
The topics covered include:
Participants will learn how to manage uncertainty effectively and apply these concepts to improve AI decision-making processes.
This module on Back Propagation Learning delves into one of the most effective algorithms for training neural networks. Back propagation is essential for optimizing the weights of networks to minimize error in predictions.
Key components of this module include:
By the end of this module, learners will be equipped with the knowledge to implement back propagation in various neural network architectures.
This module focuses on Learning: Decision Trees, a widely used method in machine learning for classification and regression tasks. Decision trees are intuitive and provide a clear visualization of decision-making processes.
The module covers:
Participants will leave with practical knowledge on creating and utilizing decision trees for data analysis and predictive modeling.