Machine Learning

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


  1. Fundamentals of Machine Learning (ML)
    1. Intro to artificial intelligence (EMBA-week01)
    2. Intro to machine learning: video-2019 (EMBA-week01)
    3. Commonly used datasets: video-2017
    4. Supervised learning
    5. Unsupervised learning:
    6. Density estimation
    7. Optimization
    8. Dimensionality reduction
    9. Distance metrics and loss functions
    10. Performance indices
      • ROC & DEC
      • Confusion matrix, specificity, sensitivity,
      • Accuracy, precision, recall, f-measure
    11. Performance evaluation
    12. Walkthrough
  2. Advaced Topics in Machine Learning
    1. More models
    2. Start your AI group
  3. Unstructured Data in Machine Learning
    1. Chinese word segmentation (EMBA-week02)
    2. Document classification (EMBA-week02)
      • Text normalization
      • POS (part of speech) tagging
      • Feature extraction
        • One-hot representation
        • TF-IDF (term frequency!Vinverse document frequency)
        • SVD (singular value decomposition)
        • Word to vector representation
  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