*Text Books
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. Multilayer artificial neural networks
12. Neural network training with TensorFlow
13. The mechanics of TensorFlow
14. Deep convolutional neural networks
15. Recurrent neural networks