COMPSCI 670: Computer Vision (Grade: A)
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Course Overview:
This graduate-level course delves into the sophisticated techniques for analyzing visual data, particularly color images. The course is structured in two key parts:
Academics
COMPSCI 685: Advanced Natural Language Processing (Grade: A)
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Course Overview:
Natural Language Processing (NLP) is both an engineering art and a science, focused on teaching computers to understand human language. As a subset of artificial intelligence, NLP has become ubiquitous, powering voice assistants, web-based question answering, discussion analysis in social media, and even human language translation. The complexity of language—rich in nuances and ambiguity—makes it challenging for computers to understand, but through the application of data, mathematics, and linguistic insights, these engineering problems can be tackled.
COMPSCI 532: Systems for Data Science (Grade: A)
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Course Overview:
COMPSCI 532: Systems for Data Science is a 3-credit course designed to provide students with a deep understanding of the systems and infrastructures that underpin large-scale data science. The course focuses on the principles and challenges associated with scaling computational processes both vertically (to many processors) and horizontally (to many nodes), enabling efficient and fast analyses of large datasets.
COMPSCI 682: Neural Networks - A Modern Introduction (Grade: A)
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Course Overview:
COMPSCI 682 is a 3-credit course focused on modern and practical methods for deep learning. The course began with an introduction to simple classifiers such as perceptrons and logistic regression and progressively moved into more complex topics, including standard neural networks, convolutional neural networks, and elements of recurrent neural networks and transformers. While the course provided a strong emphasis on practical applications, it maintained a solid foundation in the fundamentals of deep learning.
COMPSCI 589: Machine Learning (Grade: A)
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Course Overview:
This course provided a comprehensive introduction to core machine learning models and algorithms, covering key concepts in classification, regression, clustering, and dimensionality reduction. It balanced theoretical understanding with practical application, allowing students to grasp both the mathematical foundations and the real-world usage of machine learning techniques.