About This Course

Goal of this Course

Machine learning (ML) becomes a very promising research field in recent years. ML is a sub-area of artificial intelligence that teaches computers to learn. Current successful applications of ML include medicine, social networking, driverless care, autonomous robot, and a lot of image recognition tasks. Computer vision and pattern recognition are two very related fields/courses with ML.

In this course the instructor will teach a selected topics of ML, such as linear regression, logistic regression, SVM(support vector machine), neural networks, and deep learning. Some deep learning models such as CNN and R-CNN will be introduced. This course will focus more on the understanding of those ML topics, but not mathematical foundations of those ML topics. 

Evaluation of student's performance is based on a multitude of metrics, including reading reports, oral presentation, programming results, group collaboration, and peer review. Programming skills including Matlab/C/C++ is necessary to practice and implement the deep learning method. Some topics in the course will be presented by students. Interactive forms of in-classroom activities will be planned in the course. A project will be assigned with paper reading, program coding, oral presentation and report writing. Project can be done by individuals or with team work. Some presentations and reports are evaluated by peer review.

Grading

• Exercise 10%

• Programming 60%

• Project 20%

• Presence 10%

Reference Books

• 👍(Free)  Machine Learning with Python Cookbook 2nd, by Kyle Gallatin and Chris Albon, O'Reilly, 2023  (Scikit-learn, PyTorch)     Free O'Reilly Learning by FJU ID

• (Free) Machine Learning with Python - Theory and Implementation, Amin Zollanvari, Springer, 2023.  (Scikit-learn, TensorFlow, Keras)  

• (Free) Python最強入門邁向頂尖高手之路: 王者歸來 (第2版)，洪錦魁，2020  URL 輔大圖書館  HyRead電子書

• (Free) Mathematics for Machine Learning, by Marc Peter Deisenroth, A Aldo Faisal, and Cheng Soon Ong. Cambridge University Press. 2020.  (PDF)

• Introduction to Machine Learning, 3rd, E. Alpaydin, MIT Press, 2014.

• Peter Harrington, Machine Learning in Action. Manning Publications Co., 2012. (Python)

• T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: data mining, inference and prediction, Second Edition, Springer, 2009. (R code)(classical textbook)

• Deep Learning

  • Neural Networks and Deep Learning, Michael Nielsen, 2015. (Free online book)

  • Deep Learning, MIT Press, in preparation, Y. Bengio, I. Goodfellow, A. Courville, 2015. (Free PDF)

  • L. Deng, D. Yu. “Deep learning: methods and applications.” Foundations and Trends in Signal Processing, NOW Publishers, 7.3–4, 197-387, 2014. (Free PDF)

  • Yoshua Bengio, "Learning Deep Architectures for AI," Foundations and Trends in Machine Learning, 2(1), pp.1-127, 2009. (Free PDF)