# fiscal 2023 : 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

- Week 2
- 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
- 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,,,)