Introduction to AI

  1. Basic Concepts and Terminology.
    1. Elements Of Intelligence.
    2. Difference Between Human and Machine Intelligence.
  2. Sub-areas of AI.
    1. Machine Learning.
    2. Deep Learning.
    3. Neural Networks.
    4. Fuzzy Logic.
    5. Cognitive Computing.
    6. Natural Language Processing.
    7. Computer Vision.
  3. Successful Use Cases.
    1. Facial Recognition.
    2. Plate Recognition.
    3. Traffic Signs Recognition.
    4. Deep-Fakes in Media.
    5. Go and Chess Playing.
    6. Protein Folding.

  1. History of Computer Science.
    1. What is a Computer.
    2. Ancient and Old Machines.
    3. First Computers.
    4. Current Computers and Internet.
    5. Smartphones.
  2. Origins of Artificial Intelligence.
    1. The Golden Age of AI.
    2. The Turing test.
    3. Information and Knowledge.
    4. Rational Machines.
    5. Current AI.
  3. Future of AI.
    1. Current trends in AI.
    2. Impact on Society.
    3. Breakthroughs in AI.
    4. What May Go Wrong.
    5. Consciousness.

  1. Introduction.
  2. Sciences.
    1. Chemistry and Biology.
    2. Health.
    3. Physics.
    4. Materials Science.
    5. Energy System.
    6. Telecommunications.
    7. Agriculture.
  3. Finances.
    1. Banking.
    2. Phishing.
    3. Brokers and Traders.
    4. Risk Assessment.
    5. Fraud Prevention and Detection.
  4. Industry.
    1. Autonomous Vehicles.
    2. Route Planning.
    3. Environmental monitoring.
    4. Early-warning systems.
    5. Military.
  5. ICT.
    1. Programming Assistance.
    2. Neural Network Design.
    3. Quantum Computing.
    4. Data Mining.
    5. Gaming.
    6. Internet and e-Commerce.
  6. Humanities.
    1. Law.
    2. Social.
    3. Arts.
    4. Education.

  1. Lifecycle of the data.
    1. The Source Data.
    2. Features of the Model.
    3. Model Selection.
    4. Data Locality.
    5. Trust and Lineage.
  2. Legal issues.
    1. Data Protection.
    2. AI and the European Union.
    3. National Strategies on Artificial Intelligence.
    4. National Agencies to Supervise AI.
  3. Ethical principles of AI.
    1. Observe Human Decisions.
    2. Prevent Damages.
    3. Equity.
    4. Explainability.
  4. Cultural and social implications.
    1. Arts & Culture.
    2. Employment.
    3. Social Networks.
    4. Responsibility.
    5. Risks.

Methods and Tools in AI