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機器學習中的數學原理
Mathematical Principles of Machine Learning
王奕翔   107下
課程概述
this course aims to introduce some theoretical foundations of machine learning. the course is roughly divided into two parts: (1) the statistical principles and (2) the algorithmic principles. for the former, we will focus on statistical aspects of learning theory, where the main themes are what can be learned and how well a machine can learn from a finite number of training samples. for the latter, we focus on algorithmic aspects of learning theory, where the main theme is how fast a machine can learn with theoretical performance guarantees.
課程目標
1. introduce main concepts underlying machine learning with mathematical rigor.
2. uncover mathematical principles underlying various machine learning techniques.
3. introduce methods to theoretically analyze learning algorithms.
4. develop theory-oriented thinking which helps understand existing algorithms and create novel ones.
課程要求
prerequisite: calculus, probability, linear algebra.
preferable (but optional): machine learning, convex optimization, real analysis.
grading: exam (25%), homework (50%), project (25%)
指定閱讀
lectures will be based on lecture notes and slides.
參考書目
1. shai shalev-shwartz and shai ben-david, understanding machine learning: from theory to algorithms, cambridge university press, 2014.
2. y. nesterov, introductory lectures on convex optimization: a basic course. kluwer academic publishers, 2004.
3. additional references: research papers and surveys to be assigned during lectures.
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