EE 708: Fundamentals of Data Science And Machine Intelligence
This course aims to equip students with the fundamental concepts and techniques central to the fields of exploratory data analysis, statistical inference, and machine learning leading to machine intelligence. Students from all disciplines, both engineering and sciences can develop proficiency in data analysis/visualization, statistical data analysis, machine learning algorithms, and machine learning tools, enabling them to obtain actionable insights from complex datasets in various domains by completing this course. Students will be exposed to the design and implementation of machine learning models, and handling AI frameworks in Python via a course project. By the end of the course, students are expected to be able to design, implement, and evaluate machine intelligence in general and thus preparing them for careers in data science, artificial intelligence, and related disciplines. The course is targeted at all engineering and science disciplines who wish to understand the emerging and popular paradigm of Data Science and Machine Intelligence.
Course Content
The course on Data Science and Machine Intelligence aims to equip students with a comprehensive understanding of the foundational principles and advanced techniques in machine learning/AI. The course will blend theory with practical issues such as AI frameworks and programming. The course will provide a detailed understanding of exploratory statistical data analysis, visualization, and inference. Regression analysis and modeling, Classification Modeling, Decision Trees and Random Forests, Boosting/Bagging, Clustering, LDA, PCA, vector quantization, Gaussian Mixture modeling, Artificial Neural Networks, Deep Neural Networks, Convolutional Neural Networks, and Recurrent Neural Networks. Applications of machine intelligence covering areas in both science and engineering will be discussed using data spread across disciplines and applications. This course will have significant focus on regular hands on assignments and end with a course mini project to be implemented in Python using AI/ML frameworks.
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