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Covers the fundamentals of Nonlinear Programming and its applications in Optimal Control, exploring techniques, examples, optimality definitions, and necessary conditions.
Introduces Natural Language Processing (NLP) and its applications, covering tokenization, machine learning, sentiment analysis, and Swiss NLP applications.
Covers the basics of Natural Language Processing, including tokenization, part-of-speech tagging, and embeddings, and explores practical applications like sentiment analysis.
Introduces statistical signal processing tools for wireless communications, emphasizing practical applications and hands-on experience with Python or Matlab.
