【投影片 AI Concept | Proposition | First-order Logic | Genetic Algorithm |Ant Algorithm |機率表| Hypothesis Testing| ANOVA| T-test | Simple Regression | Logistic Regression | Naive Bayes Classifier | KMeans | KNN |Association Rules| Decision Tree | Random Forest | Support Vector Machine | Reinforcement Learning | Neural Networks | Deep Neural Network| Convolutional Neural Networks | Long Short-Term Memory | Large Language Model | Retrieval-Augmented Generation | Ensemble Learning】
一、Objective: To introduce artificial intelligence algorithms and application technologies. Students taking this course will have a basic knowledge of AI and be able to engage in related practice experiments. The content includes Predicate and Proposition, Theory Proving, Genetic Algorithm, T-test, Logistic Regression, Naive Bayes Classifier, KMeans, KNN, Decision Tree, Random Forest, Support Vector Machine, Reinforcement Learning, Deep Neural Networks, Convolutional Neural Networks, Long Short-Term Memory, Large Language Model, Retrieval-Augmented Generation, Ensemble Learning.
二、Text Book: 自製教材、研究論文.,
三、上課時間地點：星期三 13:10~16:00；科研1223，資工二。
助教時間地點：星期三 18:10~20:00，科研大樓1223。
四、教師：Dr. Jong-Yih，jykuo@ntut.edu.tw
助教: (@ntut.edu.tw)
五、Scopes:
1. Artificial Intelligence Concept: Predicate and Proposition, Theory Proving.
2. Optimization Concept: Genetic Algorithm, Ant Algorithm.
3. Statistical Inference: Hypothesis Testing, T-test, Logistic Regression, Naive Bayes Classifier
4. Machine Learning Clustering: KMeans.
5. Machine Learning Classification: KNN, Decision Tree, Random Forest, Support Vector Machine.
6. Reinforcement Learning: Q-Learning.
7. Deep Learning: Deep Neural Networks, Convolutional Neural Networks, Long Short-Term Memory.
8. Large Language Model, Retrieval-Augmented Generation, Ensemble Learning.
【Prerequisite knowledge: Familiar with Python programming】
六、Exam and Grading:
30% Class Practices and Assignments
30% Quiz
40% Projects (Personal and Teams)
七、課程進度及綱要
Week01(02/19): AI Concept, Proposition
Week02(02/26): First-order Logic and Theory Proving
Week03(03/05): Genetic Algorithm, Ant Algorithm
Week04(03/12): Hypothesis Testing
Week05(03/19): ANOVA and T-test
Week06(03/26): Logistic Regression, Naive Bayes Classifier
Week07(04/02): Quiz 1
Week08(04/09): KMeans, KNN, Association Rules
Week09(04/16): Decision Tree, Random Forest
Week10(04/23): Support Vector Machine
Week11(04/30): Reinforcement Learning, Q-Learning
Week12(05/07): Neural Networks, Deep Neural Networks
Week13(05/14): Convolutional Neural Networks
Week14(05/21): Long Short-Term Memory, Ensemble Learning
Week15(05/28): Large Language Model, Retrieval-Augmented Generation
Week16(06/04): Quiz 2
Week17(06/11): Term Project: Final Report & Demo
Week18(06/18): Term Project: Final Report & Demo