| (*)Students have a comprehensive understanding of natural language processing (NLP) concepts and techniques, with a strong focus on machine learning and neural networks for language modeling and text analysis. They are capable of critically analyzing, designing, and evaluating NLP solutions for applications such as sentiment analysis, information retrieval, and bias detection, considering the strengths and limitations of different approaches.
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(*)- Implementing Text Processing Pipelines (k3)
Students can preprocess and clean text data, effectively preparing it for downstream NLP applications and analyses.
- Applying Machine Learning for Sentiment Analysis (k4)
Students are able to implement and evaluate sentiment analysis models using machine learning techniques, understanding how to extract sentiment from textual data.
- Utilizing Neural Networks for Language Modeling (k4)
Students can apply neural network architectures to language modeling tasks, creating models that understand and generate human language.
- Working with Word Embeddings and Compositional Models (k4)
Students are capable of employing word embedding techniques such as word2vec and GloVe, as well as building compositional embeddings to represent and analyze the meaning of words and phrases in context.
- Applying Principles of Information Retrieval (k4)
Students can implement basic information retrieval techniques to extract relevant data from large text corpora and evaluate the performance of these methods.
- Analyzing and Addressing Societal Biases in NLP (k5)
Students are able to detect and critically analyze societal biases present in NLP models and propose methods to address and mitigate these biases.
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(*)Students have in-depth knowledge of the fundamental techniques in natural language processing, including text processing, sentiment analysis, language modeling, and word embedding methodologies. They know how to apply machine learning and neural networks to NLP tasks and are aware of the ethical and societal implications of NLP, particularly in identifying and mitigating biases.
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