【Course slides AI Concept | Proposition | First-order Logic | Genetic Algorithm |Ant Algorithm |Probability table| 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 | Generative Adversarial Networks | Large Language Model | Retrieval-Augmented Generation | Ensemble Learning】
1. 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, and Ensemble Learning.
2. Textbook: Self-developed teaching materials, research papers.
3. Place/Time: Wednesday 13:10~16:00, Technology Research Building 1223.
TA office hours: Wednesday 18:10~20:00, Research Building 1223.
4. Instructor: Dr. Jong-Yih, jykuo@ntut.edu.tw
TA: (@ntut.edu.tw)
5. Scopes:
Artificial Intelligence Concept: Predicate and Proposition, Theory Proving.
Optimization Concept: Genetic Algorithm, Ant Algorithm.
Statistical Inference: Hypothesis Testing, T-test, Logistic Regression, Naive Bayes Classifier
Machine Learning Clustering: K-Means.
Machine Learning Classification: KNN, Decision Tree, Random Forest, Support Vector Machine.
Reinforcement Learning: Q-Learning.
Deep Learning: Deep Neural Networks, Convolutional Neural Networks, Long Short-Term Memory.
Large Language Model, Retrieval-Augmented Generation, Ensemble Learning.
[Prerequisite knowledge: Familiar with Python programming]
6. Exam and Grading:
30% Class Practices and Assignments
30% Quiz
40% Projects (Personal and Teams)
7. Schedules
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): Sports Day
Week06(03/26): ANOVA and T-test
Week07(04/02): Logistic Regression, Naive Bayes Classifier
Week08(04/09): Quiz 1
Week09(04/16): K-Means, KNN, Association Rules
Week10(04/23): Decision Tree, Random Forest
Week11(04/30): Support Vector Machine, Reinforcement Learning,
Week12(05/07): Neural Networks, Deep Neural Networks
Week13(05/14): Convolutional Neural Networks
Week14(05/21): Long Short-Term Memory, Generative Adversarial Networks
Week15(05/28): Large Language Model, Retrieval-Augmented Generation, Ensemble Learning
Week16(06/04): Quiz 2
Week17(06/11): Term Project: Final Report & Demo
Week18(06/18): Term Project: Final Report & Demo