Course description

This course provides a thorough exploration of Natural Language Processing (NLP), the field of Artificial Intelligence dedicated to enabling computers to understand, interpret, and generate human language. Starting with the foundational concepts and classic rule-based approaches, the curriculum progresses through the statistical machine learning revolution and culminates in the state-of-the-art deep learning models that power today's most advanced language technologies. Students will gain both a theoretical understanding and a practical perspective on building systems that can work with text and speech.

What will i learn?

• Explain the core challenges of NLP, such as ambiguity, and identify key applications like sentiment analysis and machine translation.
• Perform standard text preprocessing tasks, including tokenization, stemming, lemmatization, and stop-word removal.
• Implement and understand traditional NLP techniques like n-gram models and TF-IDF for text representation.
• Apply classic machine learning algorithms, such as Naive Bayes and SVMs, to solve NLP problems like text classification.
• Describe the architecture and significance of modern deep learning models for NLP, including RNNs, LSTMs, and the Transformer.
• Critically evaluate the ethical implications of NLP, including model bias and the potential for misuse, and discuss future trends in the field.

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