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fiscal 2022 : Advanced Data Mining (was: Data Mining Theory)

Syllabus: "Advanced Data Mining" and 「データマイニング特論」
Contact: see also syllabus.
References
Lecture documents
- Week 1 : Guidance
- Part 1: Data Mining, Practical Machine Learning Tools and Techniques, 4th edition, 2017.
  - Week 2
    Chap. 1, What's it all about?
    
    Chap. 2, Input: concepts, instances, attributes
  - Week 3
    Chap. 3, Output: knowledge representation
    
    Chap. 4, Algorithms: the basic methods, sec. 4.1 to 4.5
  - Week 4
    cont., Chap. 4, Algorithms, section 4.6 to remains
    
    Chap. 5, Credibility: evaluating what's been learned
  - Week 5
    Chap. 6, Trees and rules
    
    Chap. 7, Extending instance-based and linear models
  - Week 6
    Chap. 8, Data transformations, section 8.1 to 8.3
    
    Cont., section 8.4 to remains
  - Week 7
    Chap. 9, Probabilistic methods, section 9.1 to 9.5
    
    Cont., section 9.5 to remains
  - Week 8
    Chap .10, Deep learning, section 10.1 to 10.3
    
    Cont., section 10.4 to remains
  - Week 9
    Chap. 11, Beyond supervised and unsupervised learning
    
    Chap. 12, Ensemble learning
  - Week 10
    Chap. 13, Moving on: applications and beyond
- Part 2: discussion about your research. (from Week 11 to remains)

#1 : Guidance

[Lecture documents]
- Guidance: Guidance
assignments: see "schedule.xlsx" on Teams.
Homework
- read and ready your assigned references to explain/introduce. You must make any presentation documents and uploat them to google documents until the previous day.
- e.g., slides (e.g., PowerPoint), commentary documents or demos if desired, etc.

References

Books
- Data Mining: Practical Machine Learning Tools and Techniques, 4th Edition
- Python Machine Learning: Machine Learning and Deep Learning with Python, scikit-learn, and TensorFlow 2, 3rd Edition (reference)
Frameworks or Tools to use Machine Learning
- Weka 3: Data Mining Software in Java
- scikit-learn: Machine Learning in Python
- The R Project for Statistical Computing
- Apache Spark MLlib
- Caffe
- Chainer
- Google TensorFlow
- SkFlow for Tensorflow
- CNTK: Computational Network Toolkit by Microsoft
- DSSTNE: Deep Scalable Sparse Tensor Network Engine by Amazon
Others
- The 38 best tools for data visualization (Chart.js, Tableau,,,)