S. Raschka and V. Mirjalili, Python Machine Learning: Machine Learning and Deep Learning with Python,

Scikit-Learn, and TensorFlow, 2nd Edition. Packt Publishing, 2017.

*References

Machine Learning

1. A. Geron, Hands-On Machine Learning with Scikit-Learn & TensorFlow: Concepts, Tools, and Techniques to

Build Intelligent Systems. O’Reilly, 2017.

2. F. Chollet, Deep Learning with Python. Manning, 2017.

3. S. Shalev-Shwartz and S. Ben-David, Understanding Machine Learning: From

Theory to Algorithms. Cambridge University Press, 2014.

4. M. Mohri, A. Rostamizadeh, and A. Talwalkar, Foundations of Machine Learning. The MIT Press, 2012.

5. S. Marsland, Machine Learning - An Algorithmic Perspective. Chapman & Hall, 2009.

6. T. M. Mitchell, Machine_Learning. McGraw-Hill, 1997.

Python

1. J. V. Guttag, Introduction to Computation and Programming Using Python: With Application to

Understanding Data, 2nd edition. The MIT Press, 2016.

2. R. Johansson, Numerical Python: A Practical Techniques Approach for Industry. Apress, 2015. (There is

an electronic version of this book in NTHU Library.)

*Teaching Method

Prerequisite knowledge on probability, statistics, stochastic processes, linear algebra, optimization and

algorithmic analysis will be briefly reviewed whenever needed. For students who do not know Python yet or

are not familiar with scientific libraries such as SciPy, NumPy, Matplotlib, and pandas, http://learnpython.org/

is a great place to start. You may refer to the official tutorial on python.org (http://docs.python.org/3/tutorial/).

We will follow the contents of the textbook. Supplemental materials will be taken from the references.

*Syllabus

1. Learning from data

2. Simple machine learning algorithm for classification

3. Machine learning classifiers using Scikit-Learn

4. Data preprocessing

5. Dimension reduction

6. Model evaluation and hyperparameter tuning

7. Ensemble learning

8. Sentiment analysis

9. Regression analysis

10. Clustering analysis

11. Multil

12. Neural network training with TensorFlow

13. The mechanics of TensorFlow

14. Deep convolutional neural networks

15. Recurrent neural networks