Machine Learning

J.-S. Roger Jang, CSIE Dept., National Taiwan University


  1. Fundamentals of Machine Learning (ML)
    1. Intro to AI: (EMBA-week01)
    2. AI's dilemma: (EMBA-week01)
    3. Intro to ML: (EMBA-week01)
    4. Datasets for ML:
    5. Supervised learning
      • K-nearest-neightbor classifiers: (EMBA-week01)
      • Linear classifiers: (EMBA-week01)
      • Naive Bayes classifiers:
      • Quadratic classifiers:
      • GMMC:
      • MLP & DNN:
      • Backpropagation:
      • SVM
    6. Unsupervised learning (Clustering)
      • K-mean clustering:
      • Fuzzy c-mean clustering:
      • Hierarchical clustering:
    7. Density estimation
      • Maximum likelihood estimate (MLE):
      • Gaussian mixture models (GMM)
      • Hidden Markov models (HMM):
    8. Optimization
      • Gradient descent: (EMBA-week01)
      • Genetic algorithms:
      • Simulated annealing:
      • Downhill simplex search:
      • Random search
      • Tabu search
    9. Dimensionality reduction
      • Feature selection:
      • Feature extraction
        • PCA:
        • LDA
    10. Distance metrics and loss functions
      • Distance metrics:
      • Dynamic programming (DP):
      • Dynamic time warping (DTW):
      • Triplet loss: https://www.youtube.com/watch?v=d2XB5-tuCWU
    11. Performance indices
      • ROC & DEC (old version):
      • Performance indices: , exercise (20 min, 2021)
    12. Performance evaluation
      • K-fold cross-validation:
    13. Walkthrough
      • A walkthrough of machine learning for leaf identification:
  2. Advaced Topics in Machine Learning
    1. More models
    2. Start your AI group
  3. Unstructured Data in Machine Learning
    1. Chinese word segmentation: (55 min, 2021) (EMBA-week02)
    2. Document classification (55 min, 2017) (EMBA-week02)
  4. Applications of Machine Learning
    1. Manufacturing
    2. Audio processing
    3. Image recognition
    4. FinTech
    5. Chatbots
  5. Summary and demos
    1. SOP for machine learning (EMBA-week02)
    2. Summary of ML (EMBA-week02)
    3. Project summary at MIR Lab (EMBA-week02)
    4. Demos at MIR Lab