Holiday
GEN II綜二110 T2T3T4
統計與機器學習概論二 (Introduction of Statistics and Machine Learning (II)) aims to introduce basic Machine Learning (ML) knowledge and technique ba<x>sed on mathematics and programming foundation laid in 統計與機 器學習概論一 (Introduction of Statistics and Machine Learning (I)). Those ML foundation includes basic mathematic understanding of probability, statistics, vectors, coordinate systems, series, matrix/determinant operation, eigen problems as well as (basic) programming-ba<x>sed problem-solving skills with at least one programming language. The relevant mathematical concept and programming skills can help students understand the essence of to-be-taught supervised learning, decision tree, random forest, gradient boosting, multivariate analysis, dimension reduction, clustering, linear discriminant analysis, SVM, DNN, CNN, RNN etc, with real-world examples taken from biomedical field.
Course keywords: supervised learning, decision tree, random forest, gradient boosting, multivariate analysis, dimension reduction, clustering, linear discriminant analysis, SVM, DNN, CNN, RNN 11120BAI700500 統計與機器學習概論二 Introduction of Statistics and Machine Learning (II) Instructors: 楊立威、李政霖、羅中泉、劉奕汶、楊自雄 (lwyang@life.nthu.edu.tw; johnli101061255@gmail.com; cclo@mx.nthu.edu.tw; ywliu@ee.nthu.edu.tw; tyang@mx.nthu.edu.tw ) Date: T234 TA: Chris Ortiz (Christopherllynardortiz@gmail.com) Textbook: Introduction to Machine Learning, Fourth Edition, Ethem Alpaydin Course Outline Section 1 2/20 Introduction to Machine Learning (LW Yang) 2/27 Supervised Learning (CL Li) 3/5 Bayesian Decision Theory/ Parametric Model (CL Li) 3/12 Decision tree / Random Forest (CL Li) 3/19 Random Forest/ Gradient Boost (CL Li) 3/26 Multivariates / Dimension reduction (LW Yang) 4/2 No Class in School Anniversary Celebration /校慶週停課 4/9 Clustering I / Linear Discrimination (LW Yang) 4/16 Dimension Reduction & Feature Selection (CC Lo) 4/23 Support Vector Machine I (YW Liu) 4/30 Support Vector Machine II (YW Liu) 5/7 Single layer Perceptron / Multilayer Perceptron (YW Liu) 5/14 Deep learning (Nick Yang) 5/21 DNN, CNN, RNN (Nick Yang) 5/28 Design Suitable Machine Learning Experiments I (Nick Yang) 6/4 CNN in Real-World Applications: Image Segmentation and object Detection (CC Lo) Grading: 4 Homework + 1 Homework or quiz (20% each) AI 使用規則 (Indicate which of the following options you use to manage student use of the AI) - 有條件開放,請註明如何使用生成式AI於課程產出 Conditionally open; please specify how generative AI will be used in course output
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Average Percentage 81.72
Std. Deviation 15.33
本課程為 16 週課程。
生醫學院,電資院,工學院,原科院,智慧生醫博士學程,跨院國際博士優先,第3次選課起開放全校修習
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