傳統計算的主要特徵是嚴格、確定和精確，但其並不適合處理現實生活中的許多問題，例如駕駛汽車、下棋、家電控制…等。但軟式計算基於其不確定、不精確及不完全真值的容錯特性，可提供低成本的方案解決許多日常生活中的問題。本課程將介紹軟式計算的基本原理、相關計算模式，及其在人工智慧與機器學習領域的應用。本課程將介紹的軟式計算的計算模式主要包括了: Neural networks、fuzzy logic、evolutionary computation、simulated annealing、swarm intelligence…等。此外，由於人工智慧及大數據分析技術在商業領域的應用越來越頻繁而重要，本課程將著重介紹資料科學在行銷領域的應用，包含了關鍵績效指標與視覺化、行銷參與度背後的驅動因素、參與度與轉換率間的關係、產品可見度與行銷、個人化行銷、產生更好的行銷決策...等。本課程所介紹的行銷資料科學案例，均可使用 R 及 Python 進行實作，以協助學生將課堂所學應用在實際的場合上。The main characteristics of traditional computing are strict, deterministic and precise, but it is not suitable for dealing with many problems in real life, such as driving a car, playing chess, controlling home appliances, etc. However, soft computing can provide low-cost solutions to many problems in daily life based on its fault-tolerant characteristics of uncertainty, imprecision and incomplete truth values. This course will introduce the basic principles of soft computing, related computing models, and its applications in the fields of artificial intelligence and machine learning. The computing models of soft computing that this course will introduce mainly include: neural networks, fuzzy logic, evolutionary computation, simulated annealing, swarm intelligence...etc. In addition, since the application of artificial intelligence and big data analysis technology in the business field is becoming more and more frequent and important, this course will focus on the application of data science in the marketing field, including key performance indicators and visualization, and the driving force behind marketing engagement. factors, the relationship between engagement and conversion rates, product visibility and marketing, personalized marketing, generating better marketing decisions...etc. The marketing data science cases introduced in this course can all be implemented using R and Python to help students apply what they have learned in the classroom to practical situations.
1. Yoon-Hyup Hwang, Hands-On Data Science for Marketing, Packt Publishing Limited., March 2019.
2. Sebastian Raschka, Vahid Mirjalili, Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition, Packt Publishing Limited., September 2017.
3. Prateek Joshi, Artificial Intelligence with Python, Packt Publishing Limited., January 2017.
1. Yoon-Hyup Hwang, Hands-On Data Science for Marketing, Packt Publishing Limited., March 2019.
2. Sebastian Raschka, Vahid Mirjalili, Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow, 2nd Edition, Packt Publishing Limited., September 2017.
3. Prateek Joshi, Artificial Intelligence with Python, Packt Publishing Limited., January 2017.
| 評分項目 Grading Method | 配分比例 Grading percentage | 說明 Description |
|---|---|---|
| 課堂參與課堂參與 class participation |
15 | |
| 課堂作業課堂作業 Classwork |
35 | |
| 期中考期中考 midterm exam |
25 | |
| 期末專題期末專題 Final topic |
25 |
