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About
本課程第二部分著重在和人工智慧密不可分的機器學習。課程內容包含了機器學習基礎理論（包含 1990 年代發展的VC理論）、分類器（包含決策樹及支援向量機）、神經網路（包含深度學習）及增強式學習（包含深度增強式學習。 此部份技術包含最早追溯至 1950 年代直到最近 2016 年附近的最新發展。此課程從基礎理論開始，簡介了各機器學習主流技法以及從淺層學習架構演變到最近深度架構的轉換。 本課程之核心目標為： （一）使同學對人工智慧相關的機器學習技術有基礎概念 （二）同學能夠理解機器學習基礎理論、分類器、神經網路、增強式學習 （三）同學能將相關技術應用到自己的問題上 修課前，基礎背景知識： 需要的先備知識：計算機概論 建議的先備知識：資料結構與演算法Modules
1.CONCEPT LEARNING
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6
Videos
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2
Readings
6
Videos
- 1-1 Brief Introduction to Machine Learning, Learning from Example
- 1-2 Hypotheses ,Relation between Instance Space and Hypotheses
- 1-3 The Find-S Algorithm
- 1-4 Version Space and The List-Then Eliminate Algorithm
- 1-5 The Candidate Elimination Algorithm
- 1-6 Biased and Unbiased Hypothesis Space, Futility of Bias-Free Learning
2
Readings
- NTU MOOC 課程問題詢問與回報機制
- 課程投影片開放下載公告
課後測驗
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1
Assignment
1
Assignment
- Week 1 Quiz
2. Computational Learning Theory
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8
Videos
8
Videos
- 2-1 Introduction to Computational Learning Theory, Setting of Sample Complexity
- 2-2 Setting 3, PAC Learnable
- 2-3 Exhausting the Version Space: Definition, Theorem ,Proof and some examples
- 2-4 Shatter, Dichotomy, VC dimension
- 2-5 Some examples and discussion about VC dimension
- 2-6 Upper and Lower Bounds on Sample Complexity with VC dimension, The Mistake Bound for Algorithms
- 2-7 Optimal Mistake Bound
- 2-8 The Weighted-Majority Algorithm and its Bound
課後測驗
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1
Assignment
1
Assignment
- Week 2 Quiz
3. Classification
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6
Videos
6
Videos
- 3-1 Decision Trees and its Hypothesis Space
- 3-2 Learning Decision Tree, Information
- 3-3 Generalization and Overfitting, Kai Square Pruning,Rule Post-Pruning
- 3-4 Model Evaluation: Metrics for Performance Evaluation, Methods for Model Comparison
- 3-5 Ensemble: Embedding, Bagging and Boosting
- 3-6 Support Vector Machine: Optimization, Soft Margins, and Kernel Trick
課後測驗
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1
Assignment
1
Assignment
- Week 3 Quiz
4. Neural network and Deep learning
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9
Videos
9
Videos
- 4-1 Introduction to Neural Network
- 4-2 Single-Layer Network and Perceptron Learning Rule
- 4-3 Multi-Layer Perceptron, Back Propagation Learning, Decline of ANN
- 4-4 Cascade Correlation Neural Networks, Deep or Shallow Structure
- 4-5 Deep Learning: Convolutional Neural Networks
- 4-6 LeNet 5, Dropout, ReLU and the Variants, Maxout, Residual Net
- 4-7 Recurrent Networks, Long Short-Term Memory (LSTM), Neural Turing Machine, Memory-Augmented Neural Networks (MANN)
- 4-8 Autoencoder: Denoising Autoencoder, Stacked Autoencoder and Variational Autoencoder
- 4-9 Generative Adversarial Net (GAN), AE+GAN and Its Applications
課後測驗
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1
Assignment
1
Assignment
- Week 4 Quiz
5. Reinforcement learning
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7
Videos
7
Videos
- 5-1 Basic Term of Markov Decision Process, Sequential Decisions, Policy, Utility
- 5-2 Derivation of Bellman Equation
- 5-3 Value iteration and Policy Iteration
- 5-4 Q-learning, Learning Policy,Simple GLIE Scheme
- 5-5 Temporal Difference Algorithm,Generalization
- 5-6 Deep Q-learning and Improvement
- 5-7 Deep Policy Network, Partially Observable MDP,Summary
課後測驗
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1
Assignment
1
Assignment
- Week 5 Quiz
Auto Summary
Explore the world of Artificial Intelligence with a focus on Machine Learning and foundational theories. This course covers essential ML theories, classifiers, neural networks, and reinforcement learning, tracing developments from the 1950s to recent advances. Ideal for learners with basic computer science knowledge, it aims to provide a solid understanding of AI techniques and their applications. Available on Coursera, with Starter and Professional subscription options. Perfect for those looking to deepen their expertise in Data Science and AI.
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