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 rapidly developing research topic in data science in the era of big data. Its application scope includes engineering, biomedicine, social economy and engineering fields. It accelerates the solution of many existing problems and creates more applications. Data science includes three aspects: mathematical and statistical methods, computer software and hardware, and application domain knowledge. Mastering mathematical scientific analysis tools is a basic skill for big data research, and understanding the basic principles of mathematics and statistics behind them can more efficiently select the correct model for analysis based on data characteristics.
This course uses the Python package Scikit-learn as an introduction to machine learning software implementation. Its content includes:
1. Supervised learning
(1) Regression model: simple linear regression, multivariable linear regression, polynomial regression
(2) Classification models: logistic regression, support vector machine, Bayesian classifier, decision tree, random forest
2. Unsupervised learning
(1) Grouping model: K-means (2) Feature reduction: Principal component analysis
3. Problems and solutions faced in machine learning implementation, including data pre-processing, hyperparameter tuning, deviation and mutation, under-fitting, over-fitting, and the behavior of learning curves and test curves.
In addition, after understanding the basic concepts of machine learning, we will learn how to implement artificial neural networks (ANN), deep learning (DNN) and convolutional neural networks (CNN) using Keras, including regression analysis, handwritten digit recognition, image recognition and other practical applications of artificial intelligence, and understand the intuitive mathematical meaning and techniques behind them.

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 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).

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.