Weekly outline

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    【瀏覽課程大綱Syllabus】【列印Print】

  • General

  • 1. 14 September - 20 September

    課程內容簡介、學期評分方式說明。

    • 2. 21 September - 27 September

      Introduction (I)

      1.1 History
      1.2 Biological neural networks

      • 3. 28 September - 4 October

        Introduction (II)

        1.3 Artificial intelligence and neural networks

        • 4. 5 October - 11 October

          Supervised Learning Network (I)

          2.1 Single-Layer Networks (Adalines)
          a. Single-Layer Perceptrons (SLPs)
          b. Optimization Method (Least-Square Learning Rule )

          • 5. 12 October - 18 October

            Supervised Learning Network (II)

            2.2 Multi-Layer Networks (Madalines)
            a. Multi-Layer Perceptrons (MLPs)
            b. Optimization Method ( Back propagation, Conjugate Gradient method, Levenberg-Marquardt (LM) method
            2.3 Radial-Basis Networks

            • 6. 19 October - 25 October

              Supervised Learning Network (III)

              2.4 Cascade-Correlation Networks
              2.5 Polynomial Networks

              • 7. 26 October - 1 November

                Supervised Learning Network (IV)

                2.6 Recurrent Networks (Time series, Back propagation through time, Finite Impulse Response (FIR) MLP ), Temporal Differences method (TD).

                • 8. 2 November - 8 November

                  Unsupervised Learning Network (I)

                  3.1 Simple Competitive Networks: Winner-take-all
                  3.2 Counter propagation Networks (CPN)

                  • 9. 9 November - 15 November

                    期中考

                    • 10. 16 November - 22 November

                      Unsupervised Learning Network (II)

                      3.3 Hamming Network
                      3.4 Principal Component Analysis and Hebbian Learning (PCA).

                      • 11. 23 November - 29 November

                        Unsupervised Learning Network (III)

                        3.5 Learning Vector Quantization (LVQ)
                        3.6 Adaptive Resonance Theory (ART)

                        • 12. 30 November - 6 December

                          Unsupervised Learning Network (III)

                          3.7 Learning Vector Quantization (LVQ)
                          3.8 Kohonen Self-Organizing Maps (SOMs)

                          • 13. 7 December - 13 December

                            Others

                            4.1 The Discrete Hopfield Net
                            4.2 Neural Network Based Control

                            • 14. 14 December - 20 December

                              Others

                              4.3 Adaptive Neuro-Fuzzy Inference Systems (ANFIS)
                              4.4 Neural Networks and the Soft Computing

                              • 15. 21 December - 27 December

                                Final report (I)

                                分組討論、專題報告

                                • 16. 28 December - 3 January

                                  Final report (II)

                                  分組討論、專題報告

                                  • 17. 4 January - 10 January

                                    Final report (III)

                                    分組討論、專題報告

                                    • 18. 11 January - 17 January

                                      期末考