Syllabus

1. Machine Learning
Yuh-Jye Lee
yuhjye@math.nctu.edu.tw
Spring, 2017
“Google’s always used machine learning. In all the areas we applied it
to, speech recognition, then image understanding, and eventually language
understanding, we saw tremendous improvements.”
−John Giannandrea, then VP of Engineering, Google
In the last decade, machine learning has been applied to many real world problems
successfully. It is considered as the most essential and fundmental knowledge for
a data scientist. We introduce core concept of machine learning and several use-
ful learning methods including linear models, nonlinear models, kernel methods,
dimension reduction, unsupervised learning (Clustering) and deep learning. Also
some special topics and applications will be discussed.
Schedule: 15 : 30 − 17 : 20, Tuesday & 09 : 00 − 09 : 50, Wednesday
Room: SC 204
Prerequisites:
• Mathematical analysis
• Numerical Methods
• Linear Algebra
• Probability
• Programming skills
Textbook: Deep Learning,
Ian Goodfellow, Yoshua Bengio and Aaron Courville, 2016
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2. Topics in this course:
• Introduction to Machine Learning
• Fundamental of Mathematics Background
• Three Fundamental Learning Algorithms
– k-Nearest Neighbor Algorithm
– Naive Bayes Algorithm
– The Perceptron Algorithm
• Evaluating the Learning Models
• Support Vector Machines
• Generalization Theory: Bias vs. Variance
– PAC: Probably Approximately Correct
– VC-dimension
• Ensemble Learning: Adaboosting
• Online Learning
• Unsupervised Learning
– k-means Algorithm
– Mixture of Gaussians
– EM algorithm
• Dimension Reduction
• Deep Learning
Grading:
• Homework: 30%
• Final Exam: 40%
• Final Project: A Kaggle Competition, 30%
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3. Reading Assignment:
•• Wing, Jeannette M. “Computational thinking.” Communications of
the ACM 49.3 (2006): 33-35.
• Domingos, Pedro. “A few useful things to know about machine learn-
ing.” Communications of the ACM 55.10 (2012): 78-87.
• Dhar, Vasant. ”Data science and prediction.” Communications of the
ACM 56.12 (2013): 64-73.
Reference:
•• Ethem Alpaydin, Introduction to Machine Learning, 3rd Edition, 2014,
ISBN: 978-0-262-028189
http://www.cmpe.boun.edu.tr/∼ethem/i2ml3e/
• Abu-Mostafa, Yaser S., Malik Magdon-Ismail, and Hsuan-Tien Lin.
Learning from data. Berlin, Germany: AMLBook, 2012.
• Friedman, Jerome, Trevor Hastie, and Robert Tibshirani. The ele-
ments of statistical learning. Vol. 1. Springer, Berlin: Springer series
in statistics, 2001.
• Witten, Ian H., and Eibe Frank. Data Mining: Practical machine
learning tools and techniques. Morgan Kaufmann, 2005.
• Vapnik, Vladimir Naumovich, and Vlamimir Vapnik. Statistical learn-
ing theory. Vol. 1. New York: Wiley, 1998.
• Mitchell, Tom M. Machine learning. McGraw Hill,1997.
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