Introduction to Artificial Intelligence (AI)
About Course
Introduction
This course provides a comprehensive introduction to the fundamental concepts and techniques of Artificial Intelligence (AI). Students will explore the history, philosophy, and applications of AI, as well as the ethical and societal implications of AI. Students will also discover the theoretical foundations of AI, algorithmic techniques, and practical applications in various domains.
Participants will gain a basic understanding of AI and its impact on various industries.
The module will also include real-world examples and case studies to enhance the learning experience. This module is suitable for professionals who are interested in gaining a fundamental understanding of AI, including managers, team leaders, business analysts, project managers, and anyone involved in decision-making processes related to technology adoption.
Course Content
Modules 1: Introduction to Artificial Intelligence
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Lesson 1 Introduction to Artificial Intelligence
03:10
Definition of AI
Brief history of AI development
Types of AI: weak vs strong, narrow vs general
Module 2: Key Concepts in AI
Supervised learning
Unsupervised learning
Reinforcement learning
Deep Learning
Natural Language Processing (NLP)
Robotics
Neural networks
Knowledge representation
Module 3: Machine Learning
Introduction to machine learning: supervised, unsupervised, and reinforcement learning
Supervised learning algorithms: linear regression, logistic regression, support vector machines
Unsupervised learning algorithms: k-means clustering, hierarchical clustering, principal component analysis
Deep learning frameworks: TensorFlow, PyTorch
Module 4: Neural Networks and Deep Learning
Introduction to neural networks: perceptron, multilayer perceptron, backpropagation
Convolutional neural networks and recurrent neural networks
Module 5: Natural Language Processing
Introduction to NLP: syntax, semantics, pragmatics
Text preprocessing and normalization
Word embeddings and language models
Module 6: Computer Vision
Module 7: Intelligent Agents
Agent architectures: reactive, deliberative, hybrid
Agent decision-making: deterministic, probabilistic, utility-based
Agent architectures: reactive, deliberative, hybrid
Agent communication: language and protocols
Module 8: Problem Solving and Search
Problem-solving strategies: brute force, divide and conquer, hill climbing
Heuristics and optimization techniques
Module 9: Knowledge Representation and Reasoning
Types of knowledge representation: propositional logic, first-order logic, semantic networks
Reasoning techniques: forward and backward chaining, resolution, rule-based systems
Inference in first-order logic: unification, resolution refutation
Module 10: Common Techniques Used in Problem-Solving
Classification algorithms
Module 11: Applications of AI
Module 12: Benefits and Challenges of Implementing AI
Module 13: Ethical Considerations
Module 14: Questions and answers
40 Practical questions
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