| 課程大綱 Syllabus |
學生學習目標
Learning Objectives |
單元學習活動
Learning Activities |
學習成效評量
Evaluation |
備註
Notes |
序
No. | 單元主題
Unit topic |
內容綱要
Content summary |
| 1 | 簡介 |
1. 何謂機器學習
2. 機器學習之分類
3. 機器學習之應用 |
認識本課程將討論之各項主題，並簡要的複習相關數學 |
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| 2 | 建立資料收集、呈現、分析環境 |
1. 安裝資料分析Tableau desktop工具.
2. Tableau 功能介紹 |
1. 培養學生能夠安裝與使用Tableau工具的能力 |
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| 3 | 資料蒐集 |
1. Excel 資料蒐集
2. Tabluau 資料蒐集 |
1. 了解資料收集的方法
2. 培養資料蒐集的能力 |
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| 4 | 資料收集、資料呈現、資料分析 |
1. 資料的蒐集與資料的預處理
2. 利用工具呈現資料
3. 建置資料儀表板 |
1. 具有資料的蒐集的能力
2. 具有資料的預處理的能力
3. 具有資料的呈現的能力
4. 具有建置資料儀表板的能力 |
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| 5 | 建立機器學習環境 |
1. 安裝Anaconda
2. 建立Anaconda虛擬環境
3. 安裝TensorFlow、Keras |
1. 培養學生能夠安裝與使用機器學習環境工具的能力 |
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| 6 | 監督式學習 – 迴歸與分類 |
1. 線性與非線性迴歸監督式學習 |
1. 理解線性與非線性迴歸監督式學習
2. 具有實作監督式學習的能力 |
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| 7 | 神經網路與深度學習方法 |
1. 介紹機器學習 vs. 深度學習
2. 介紹神經元（Neuron）感知器（Perceptron）類神經網路（Neural Network）激勵函數（Activation Function） |
1. 了解機器學習與深度學習的差異
2. 了解深度學習神經網路的基本知識 |
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| 8 | Midterm |
None |
None |
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| 9 | 深度學習模型 |
1. 遷移式學習 (Transfer Learning)
2. XGboost (eXtreme Gradient Boosting 模型應用) |
1. 理解多層感知器深度學習模型
2. 培養實作出神經網路模型的能力 |
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| 10 | Version Space |
1. Inductive Learning
2. Hypothesis Space
3. Inductive Learning
4. Find-S Algorithm
5. Version Space --- Candidate Elimination Algorithm
6. Induction Bias |
Introduce the concept learning based on symbolic or logical representations.
It also discusses the general-to-specific ordering over hypotheses, and
the need for inductive bias in learning. |
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| 11 | Decision Tree Learning |
1. Concept Learning System (CLS)
2. IDS
3. C4.5
4. Occam's razor |
The concept of decision tree learning and the problem of overfitting the
training data. It also examines Occam's razor---- a principle recommending
the shortest hypothesis among those consistent with the data. |
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| 12 | Adaboost |
1. Weak Learner
2. The Adaboost algorithm
3. How and Why Adaboost works |
Theorectically understand the Adaboost learning agorithm |
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| 13 | Adaboost |
1. AdaBoost for Face Detection
2. Attentional Cascade
3. ROC curve |
Introduce application and implementation of Adaboost algorithm |
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| 14 | Bayesing Learning |
1. Review Probability
2. Bayes Theorem
3. Bayes Theorem & Concept Learning
4. Maximum Likelihood & Least-Squared Hypotheses
5. ML Hypothesis for Predicting Probability |
Understand Bayes theorem & apply it for concept learning |
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| 15 | Bayesing Learning |
1. Minimum Description Length Principle
2. Bayes Optimal Classifier
3. Gibbs Algorithm
4. Naïve Bayes classifier
5. Bayesian Belief Networks |
Understand Bayes theorem & apply it for concept learning |
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| 16 | EM Algorithm |
1. Maximum Likelihood Estimator (MLE)
2. The problems tackled by EM algorithm |
1. Introduce MLE in statistics
2. Introduce the applications of EM algorithm, including missing attributes, mixing attribute & mixtures |
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| 17 | EM Algorithm |
1. Main body of EM algorithm
2. Mixture Model
3. EM-Algorithm on GMM |
1. Understand EM-algorithm in detail
2. Implememt GMM |
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| 18 | Final exam |
None |
None |
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