Machine Learning Basics, Pattern Recognition, PCA Dimensionality Reduction, Classification and Regression
This course provided a comprehensive introduction to the fundamentals of machine learning and pattern recognition. I studied key models such as linear and nonlinear regression, classification algorithms, Bayesian theory, and maximum likelihood estimation. I also learned common dimensionality reduction techniques like Principal Component Analysis (PCA). The course combined mathematical derivation with experimental implementation to deepen my understanding of the underlying principles and improve my ability to model and analyze complex data using these methods.
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Advanced Algorithm Design, Complexity Analysis, Optimization Algorithms, Computer Engineering Fundamentals
This course extended my understanding of algorithm design and analysis, focusing on modeling and solving more complex problems. I studied advanced algorithms such as network flow, graph matching, number theory algorithms, and NP-completeness. The course emphasized efficiency, asymptotic complexity, and space optimization. Compared to basic algorithm courses, this one placed greater focus on theoretical reasoning and abstraction, equipping me with the skills to analyze complex systems and optimize computational resources in engineering applications.