機器學習是大數據時代下資料科學快速發展的研究課題，其應用範圍含括在工程、生醫、社會經濟與工程領域，在加速解決許多既有問題時，也創造出更多的應用。數據科學包含數學與統計方法，計算機軟硬體，以及應用領域知識等三個面向，掌握數學科學分析工具是大數據研究的基本技能，而了解其背後數學與統計的基本原理，可更有效率針對資料特性去選擇正確的模型進行分析。
本課程以Python套件Scikit-learn作為機器學習軟體實作入門，其內容含括：
1.監督式學習
(1)迴歸模型：簡單線性迴歸，多變數線性迴歸，多項式迴歸
(2)分類模型：邏輯迴歸，支持向量機，貝氏分類器，決策樹，隨機森林
2.非監督式學習
(1)分群模型：K-means (2) 特徵降微：主成分分析
3.機器學習實作上會面臨的問題與解決的辦法，包含資料前處理、超參數調教、偏差與變異、欠擬合、過度擬合以及學習曲線、測試曲線的行為。
此外，在有了機器學習基本概念後，我們將學習由Keras實作人工神經網路（ANN）、深度學習（DNN）與卷積神經網路（CNN），包含迴歸分析、手寫數字辨識、影像識別等人工智慧實務應用，並了解其背後直觀的數學意義與技巧。Machine learning is a research topic for the rapid development of data science in the big data era. Its application scope includes engineering, medicine, social economy and engineering fields. When accelerating the solution of many existing problems, it also creates more applications. Mathematical science includes three aspects: mathematics and statistical methods, computer software and hardware, and application domain knowledge. Mastering mathematical scientific analysis tools is a basic skill in large data research, and understanding the basic principles of mathematics and statistics behind it can more efficiently select correct models for analysis of data characteristics.
This course uses Python suite Scikit-learn as the machine learning software, and its content includes:
1. Supervisor-style learning
(1) Recitation model: simple linear replication, multiple variable linear replication, multi-type replication
(2) Category model: logical retelling, support vector machine, Belich classification machine, decision tree, random forest
2. Non-supervised learning
(1) Grouping model: K-means (2) Characteristic reduction: principal component analysis
3. The methods for problems and solutions that will be faced in machine learning practice include pre-processing of data, over-parameter training, deviation and variation, under-combination, over-combination, learning curves and testing curves.
In addition, after the basic concepts of machine learning, we will learn Keras’s artificial neural network (ANN), deep learning (DNN) and volume neural network (CNN), including artificial intelligence practical applications such as reproductive analysis, handwriting digital identification, and image identification, and understand the mathematical meanings and techniques behind it.
Machine learning is the science of data analysis that automates a massive number of models building. Its process uses data to iteratively detect patterns and adjust models accordingly, and enables computers to learn without explicitly programmed. This course introduces some important concepts and algorithms of machine learning from both theoretical and practical perspective. The topics include, but not limited to: (1) Supervised learning (Linear Models for Regression and Classification, Kernel Smoothing Methods, Decision Trees, Support Vector Machines, and Neural Networks). (2) Unsupervised learning (Association Rules and Cluster Analysis). (3) Ensemble learning (Bagging, Boosting, Random Forests). (4) Others (MCMC, Optimization Integration).
Machine learning is the science of data analysis that automatically a massive number of models building. Its process uses data to iteratively detect patterns and adjust models accordingly, and enables computers to learn without explicitly programmed. This course introduces some important concepts and algorithms of machine learning from both theoretical and practical perspective. The topics include, but not limited to: (1) Supervised learning (Linear Models for Regression and Classification, Kernel Smoothing Methods, Decision Trees, Support Vector Machines, and Neural Networks). (2) Unsupervised learning (Association Rules and Cluster Analysis). (3) Ensemble learning (Bagging, Boosting, Random Forests). (4) Others (MCMC, Optimization Integration).
G. James, D. Witten, T. Hastie and R. Tibshirani. An Introduction to Statistical Learning: with Applications in R (2013). Springer-Verlag.
G. James, D. Witten, T. Hastie and R. Tibshirani. An Introduction to Statistical Learning: with Applications in R (2013). Springer-Verlag.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 隨堂測驗隨堂測驗 Lutang Test |
20 | |
| 作業作業 Action |
60 | |
| 期末考試期末考試 Final exam |
20 |
