| 課程大綱Course Description： |
| 機器學習是一種數據分析技術，可以教電腦去做人類和動物自然而然的事情：從經驗中學習。機器學習算法使用數學演算法直接從數據中“學習”信息，而無需依賴預先推導的方程式作為模型。隨著可用於學習的樣本數量的增加，這些算法可以自適應地提高其性能。機器學習使用兩種類型的技術：監督學習（利用已知的輸入和輸出數據訓練模型，以便可以預測將來的輸出）和監督學習（在輸入數據中發現隱藏的模式或內在結構）。本課程介紹機器學習演算法，主題包括：（i）有監督的學習（回歸和分類）。（ii）無監督學習（聚類和降維）。（iii）案例研究及其在機器學習中的應用，以學習如何應用學習算法。 |
| English Outline： |
| Machine learning is a data analytics technique that teaches computers to do what comes naturally to humans and animals: learn from experience. Machine learning algorithms use computational methods to “learn” information directly from data without relying on a predetermined equation as a model. The algorithms adaptively improve their performance as the number of samples available for learning increases. Machine learning uses two types of techniques: supervised learning, which trains a model on known input and output data so that it can predict future outputs, and unsupervised learning, which finds hidden patterns or intrinsic structures in input data. This course provides an introduction to machine learning. Topics include: (i) Supervised learning (regression and classification). (ii)Unsupervised learning (clustering and dimensionality reduction). (iii) Case studies and applications in machine learning to learn how to apply learning algorithms. |
| 本科目教學目標Course Objectives： |
| 會使用python語言進行各項機器學習演算法之實現 |