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
- Homework 60%
- Group Discussion 10%
- Project 25%
- Presence 5%
Requirements
- Language: Chinese
- Skill: Python, Matlab, or C/C++.
- Instrument for homework: Desktop/Notebook, GPU, Linux
- Reading : Each report with at most 1000 words
- Programming : Each with a brief report at most 500 words, but with many program's illustrations
- Project : Reading + Programming + Report. The report has at least 1500 words
- No plagiary for reports and programs. (不得抄襲，不得由網頁資料複製。抄襲複製之報告一律以零分計算)
Reference Books
- 👍 Introduction to Machine Learning, 3rd, E. Alpaydin, MIT Press, 2014.
- Peter Harrington, Machine Learning in Action. Manning Publications Co., 2012. (Python)
- R. Battiti and Mauro Brunato. The LION Way - Machine Learning plus Intelligent Optimization, Lionsolver Inc., 2013. (This book uses a software: LION solver)
- T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: data mining, inference and prediction, Second Edition, Springer, 2009. (R code)(classical textbook)
- C.M. Bishop, N.M. Nasrabadi. Pattern Recognition and Machine Learning. New York: springer, 2006.
- 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)
- Deep Belief Nets in C++ and CUDA C, by Timothy Masters, 2015. (Book information)