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This course provides a fundamental introduction to machine learning for students of arts. The goal of this course is to introduce the fundamental machine learning methods and tools, show how to apply the machine learning packages in Python, and demonstrate applying machine learning techniques is effective for solving relevant problems arising from applications. This course will not go into the technical details behind machine learning methods, e.g. the math, optimization algorithms and theoretical properties, will only cover the most fundamental supervised and unsupervised machine learning methods, and will not focus on programming or developing new machine learning methods or tools.
It presents the fundamental concepts of database management in an intuitive manner geared toward allowing students to begin working with databases as quickly as possible. It also contains additional material that can be used as supplements or as introductory material for an advanced course. Because the authors present concepts as intuitive descriptions, a familiarity with basic data structures, computer organization, and a high-level programming language are the only prerequisites. Important theoretical results are covered, but formal proofs are omitted. In place of proofs, figures and examples are used to suggest why a result is true.
