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    Foundations of Artificial Intelligence 

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      Level

      CDP (Module from Year 2 Degree)

      Duration

      11 Weeks

      Study method

      Online

      Provider

      University of Leicester

      Course overview

      AI is likely to disrupt many of the existing technologies in various areas, including in healthcare and transport.
      As such, a foundational knowledge of AI, e.g., regarding modelling, search, planning and optimisation, will be regarded as a major asset for many of the future IT jobs.

      This module will provide you with such a foundational knowledge and you are likely to find it useful if you are employed as a programmer, system architect, business analyst, or team manager in the future: you will be able to spot the opportunities of applying AI to problems in your domain, have an idea about the possible solutions provided by AI, and estimate the benefits they can bring about.

      Additional information

      The module covers the following subject matters
      • The basic concepts and history of AI,
      • Translating real-world problems into AI specifications,
      • Providing solutions to AI problems using search algorithms and heuristics, and
      • Formulating optimisation problems for AI agents, and be able to apply an array of out-of-the-box methods and tools for solving optimisation problems,
      • Introducing some of the prominent challenges in the application of AI, including societal and ethical issues.

      On completion of the module, successful students should be able to:
      • Translate an AI problem to a specification of the agent’s task environment;
      • Provide a precise problem formulation for a problem-solving agent;
      • Explain and discuss different algorithms for uninformed search, and identify the most suitable approach for a given problem;
      • Explain and discuss different algorithms for informed search, explain the effect of heuristics on performance, demonstrate familiarity with methods for constructing good heuristics;
      • Formulate optimisation problems for AI agents, and be able to apply an array of out-of-the-box methods and tools for solving optimisation problems;
      • Identify the appropriate type of environment for a given problem, and the corresponding methods for solving search problems within this environment;
      • Implement and apply AI techniques to typical application domains such as video games and robotics.

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