機器學習已成功應用於許多現實世界的問題，目前產業界、科研領域都需要此方面人才。 這課程為基礎簡介， 對象大學部學生為主。我們以深入淺出，介紹了機器學習基礎的model、核心概念和幾個常用的深度學習，也包含數學理論間意基礎與實作，model包括線性模型、regression、非線性模型、深度前饋網絡、深度學習、CNN的演算法，為同學日後研究打下基礎。課程並說明許多實際實務應用，包含g.手寫字辨識、語音辦認、影像、EC廣告推撥。另外， 也教導pythont程式語言與TensorFlow，以此實作機器學的範例與練習。Machine learning has been successfully applied to many real world problems, and talents in the industry and scientific research fields are currently needed. This course is a basic introduction, mainly targeting university students. We have introduced the model, core concepts and several commonly used in-depth learning based on machine learning, and also includes mathematical theory-based basics and implementations. Models include linear models, regression, non-line models, deep preemptive networks, and depth Learning and CNN algorithms lay the foundation for future research of classmates. The course also explains many practical applications, including g. manual word identification, voice recognition, video, and EC advertising promotion. In addition, we also teach pythont programming language and TensorFlow to use this to implement examples and practices of machine learning.
PYTHON之數學基礎與實作
Mathematical Basics and Integration of PYTHON
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 小考一小考一 A small test |
15 | 上機 |
| 期中考期中考 Midterm exam |
30 | 期中上機+期中專題各15% |
| 小考二小考二 Tips two |
15 | 筆試+上機 |
| 期末考期末考 Final exam |
30 | 期末上機(10%)+期末專題(20%) |
| 作業作業 Action |
10 | 6~8次作業 |
