- Introduction to Artificial Intelligence
1.1 Defining AI
Overview of what constitutes artificial intelligence.
Distinction between weak AI and strong AI.
Brief introduction to various subfields, including machine learning, natural language processing, robotics, etc.
https://en.wikipedia.org/wiki/Artificial_intelligence
https://en.wikipedia.org/wiki/Machine_learning
https://en.wikipedia.org/wiki/Natural_language_processing
https://en.wikipedia.org/wiki/Robotics
1.2 The Importance of AI in the 21st Century
Impact of AI on various sectors: healthcare, finance, education, transportation, etc.
https://hbr.org/2017/11/the-business-case-for-ai
https://www.forbes.com/sites/bernardmarr/2019/05/01/the-top-5-artificial-intelligence-trends-in-healthcare-in-2019/
Discussion of ethical implications and societal concerns.
https://www.brookings.edu/research/ai-ethics-and-governance/
1.3 Purpose of the Module
Relevance to current trends in AI research and applications.
https://towardsdatascience.com/current-trends-in-ai-research-4b5cf95cabc1
- Historical Milestones in AI Development
2.1 Early Concepts and Foundations (1940s-1950s)
Mathematical Logic and Computing
Contributions of figures like George Boole and Gottfried Wilhelm Leibniz.
https://en.wikipedia.org/wiki/George_Boole
https://en.wikipedia.org/wiki/Gottfried_Wilhelm_Leibniz
Development of Boolean algebra and its importance in computing.
Mathematics in AI: https://www.coursera.org/specializations/mathematics-machine-learning
Alan Turing and the Turing Test
Overview of Turing’s work on computation and artificial intelligence.
https://en.wikipedia.org/wiki/Alan_Turing
Explanation of the Turing Test and its significance.
2.2 The Birth of AI as a Field (1956)
Dartmouth Conference
Details of the conference that marked the official birth of AI as a field of study.
Key figures involved: John McCarthy, Marvin Minsky, Nathaniel Rochester, and Claude Shannon.
https://en.wikipedia.org/wiki/John_McCarthy_(computer_scientist)
https://en.wikipedia.org/wiki/Marvin_Minsky
https://en.wikipedia.org/wiki/Nathaniel_Rochester
https://en.wikipedia.org/wiki/Claude_Shannon
2.3 The Golden Years of AI (1956-1974)
Advancements in Problem Solving and Theorem Proving
Development of early AI programs like the Logic Theorist and General Problem Solver.
https://en.wikipedia.org/wiki/Logic_Theorist
https://en.wikipedia.org/wiki/General_Problem_Solver
Symbolic AI and Expert Systems
The rise of rule-based systems and their applications in various domains.
2.4 The AI Winter (1974-1980)
Challenges and Setbacks
Overview of funding cuts and loss of interest in AI research.
Reasons for disillusionment: limited computing power and unrealistic expectations.
https://en.wikipedia.org/wiki/AI_winter
2.5 Resurgence of AI (1980s-Present)
Expert Systems and Commercial Applications
Revival of interest in AI through successful applications in industries.
Introduction of Machine Learning
Explanation of how machine learning changed the landscape of AI.
Key figures: Geoffrey Hinton, Yann LeCun, and others.
https://en.wikipedia.org/wiki/Geoffrey_Hinton
https://en.wikipedia.org/wiki/Yann_LeCun
Deep Learning Revolution
Overview of deep learning techniques and breakthroughs in neural networks.
https://en.wikipedia.org/wiki/Deep_learning
Explanation of neural networks and their architecture.
- Theoretical Foundations of AI
3.1 Logic and Reasoning
Propositional and Predicate Logic
Basics of formal logic and its application in AI.
https://en.wikipedia.org/wiki/Propositional_logic
https://en.wikipedia.org/wiki/Predicate_logic
Inference Mechanisms
Explanation of deduction, induction, and abduction.
https://en.wikipedia.org/wiki/Deductive_reasoning
https://en.wikipedia.org/wiki/Inductive_reasoning
https://en.wikipedia.org/wiki/Abductive_reasoning
Applications in AI
Use in knowledge representation and automated reasoning.
3.2 Knowledge Representation
Semantic Networks
Explanation of how knowledge is structured in networks.
https://en.wikipedia.org/wiki/Semantic_network
Frames and Ontologies
Overview of how frames and ontologies are used to represent knowledge.
https://en.wikipedia.org/wiki/Ontology_(information_science)
Reasoning with Uncertainty
Introduction to probabilistic reasoning and Bayesian networks.
3.3 Learning Theories
Supervised, Unsupervised, and Reinforcement Learning
Definitions and key differences.
https://en.wikipedia.org/wiki/Supervised_learning
https://en.wikipedia.org/wiki/Unsupervised_learning
https://en.wikipedia.org/wiki/Reinforcement_learning
Examples of algorithms used in each category.
Neural Networks
Overview of how neural networks work and their architecture.
Deep Learning
Explanation of convolutional and recurrent neural networks.
https://en.wikipedia.org/wiki/Convolutional_neural_network
https://en.wikipedia.org/wiki/Recurrent_neural_network
Transfer Learning and Its Applications
Discussion on how transfer learning enhances AI models.
[https://en.wikipedia.org/wiki/Transfer_learning](https://en.wikipedia
Top comments (0)