Machine Learning

Course Information

• Course Number：7D161007L
• Classroom：4201
• Time：
  　　14:00 - 15:45, Monday
  　　14:00 - 15:45, Wednesday
• Textbook：Bishop, C. M. Pattern Recognition and Machine Learning, Springer, 2006.
• Main reference books：
  　　周志华 著.机器学习,北京:清华大学出版社,2016.
  　　Tom M. Mitchell. Machine Learning, McGraw-Hill, 1997.
  　　Hastie, T., Tibshirani, R., Fridman, J., The Elements of Statistical Learning, Springer, 2nd edition, 2009.
  　　Mackay, D. Information Theory, Inference and Learning Algorithms, Cambridge University Press, 2003.

Slides (password protected)

• Lecture 1 Introduction (slides)
• Lecture 2 Supervised Learning (slides1 slides2)
• Lecture 3 Probabilistic Modeling
• Lecture 4 Unsupervised Learning
• Lecture 5 Sampling Methods
• Lecture 6 Sequence Data and Markov Chains
• Lecture 7 Hidden Markov Models
• Lecture 8 Ensemble Methods (slides)
• Lecture 9 Principles of Learning (slides)
• Lecture 10 Experimental Evaluation (slides)
• Lecture 11 Advanced Topics (slides1 slides2)