Foundations of Artificial Intelligence
( 25 Modules )

Module #1
Introduction to Artificial Intelligence
Overview of AI, its history, and applications

Module #2
Types of Artificial Intelligence
Narrow or weak AI, general or strong AI, and superintelligence

Module #3
AI and Machine Learning
Relationship between AI and machine learning, and their differences

Module #4
Problem Solving and Search
Introduction to problem solving, search algorithms, and heuristics

Module #5
Uninformed Search Strategies
Breadth-first search, depth-first search, and uniform-cost search

Module #6
Informed Search Strategies
Best-first search, greedy search, and A* search

Module #7
Local Search and Optimization
Hill climbing, simulated annealing, and genetic algorithms

Module #8
Knowledge Representation
Introduction to knowledge representation, propositional logic, and first-order logic

Module #9
Semantic Networks and Frames
Semantic networks, frames, and their applications

Module #10
Rule-Based Expert Systems
Introduction to rule-based systems, forward and backward chaining

Module #11
Machine Learning Fundamentals
Supervised, unsupervised, and reinforcement learning, bias-variance tradeoff

Module #12
Linear Regression and Gradient Descent
Linear regression, gradient descent, and stochastic gradient descent

Module #13
Decision Trees and Random Forests
Decision trees, random forests, and ensemble methods

Module #14
Neural Networks and Deep Learning
Introduction to neural networks, perceptrons, and backpropagation

Module #15
Convolutional Neural Networks
Convolutional neural networks, convolutional and pooling layers

Module #16
Natural Language Processing
Introduction to NLP, language models, and text processing

Module #17
Computer Vision and Image Processing
Introduction to computer vision, image processing, and object recognition

Module #18
Robotics and Planning
Introduction to robotics, motion planning, and control

Module #19
AI and Ethics
Ethical considerations in AI development and deployment

Module #20
AI and Society
Social implications of AI, job displacement, and AI governance

Module #21
Case Studies in AI
Real-world applications of AI in various industries

Module #22
AI and Data Science
Relationship between AI and data science, data preprocessing, and visualization

Module #23
AI and Programming
Programming languages for AI, Python, and R

Module #24
AI Tools and Frameworks
Popular AI tools and frameworks, TensorFlow, PyTorch, and Keras

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Foundations of Artificial Intelligence career

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Foundations of Artificial Intelligence ( 25 Modules )

Foundations of Artificial Intelligence
( 25 Modules )