Introduction to Artificial Intelligence

Course Outline:

– Introduction to Artificial Intelligence
• Overview of artificial intelligence and its history
• Types of AI: narrow vs. general AI
• Applications of AI in various domains

– Introduction to Machine Learning
• Basics of machine learning: supervised, unsupervised, and reinforcement learning
• Machine learning workflow: data collection, preprocessing, modeling, evaluation, and deployment
• Machine learning algorithms overview

– Supervised Learning
• Introduction to supervised learning
• Regression algorithms (linear regression, polynomial regression)
• Classification algorithms (logistic regression, decision trees, k-nearest neighbors)
– Unsupervised Learning
• Introduction to unsupervised learning
• Clustering algorithms (k-means clustering, hierarchical clustering)
• Dimensionality reduction techniques (PCA, t-SNE)

– Neural Networks and Deep Learning
• Basics of artificial neural networks (ANNs)
• Introduction to deep learning
• Building and training neural networks using TensorFlow/Keras

– Convolutional Neural Networks (CNNs)
• Introduction to CNNs and their architecture
• Applications of CNNs in computer vision tasks (image classification, object detection)

– Recurrent Neural Networks (RNNs) and Natural Language Processing (NLP)
• Introduction to RNNs and their architecture
• Applications of RNNs in sequential data tasks (time series prediction, text generation)
• Basics of natural language processing and text preprocessing

– Ethical and Societal Implications of AI
• Ethical considerations in AI and machine learning
• Bias and fairness in AI algorithms
• Privacy and security concerns
• AI for social good: applications in healthcare, education, and sustainability