The primary focus of this course is to provide a comprehensive introduction to the concepts, techniques, and applications of artificial intelligence.
Two types of artificial intelligence are narrow (or weak) AI and general (or strong) AI.
Supervised learning is a machine learning approach where the algorithm learns from labeled training data to make predictions or decisions. An example is a spam email filter that learns from labeled emails (spam or not spam) to classify future emails.
One application of natural language processing is sentiment analysis, where AI analyzes text data to determine the emotional tone, such as positive, negative, or neutral, in social media posts, reviews, and customer feedback.
Bias in AI development refers to the presence of unfair or discriminatory outcomes in AI systems due to the data used for training. It can result from historical biases present in training data and can lead to AI systems making unjust decisions or perpetuating inequalities.