This course is designed for 16 weeks. Every week has 3 units and each unit is for 45 minutes.
Time: Thurday from 1:30pm to 4:00pm.
2012/12/3 Website is up.
Course DescriptionThe course is designed to provide a broad introduction of the theories, algorithms and applications of machine learning. Topics include: supervised learning (generative/discriminative learning, support vector machines, logistic regressions), unsupervised learning (clustering, feature selection, nonparametric Bayesian methods), learning theory (bias/variance tradeoff, model selection, VC theory), probabilistic graphical models (HMM, structure learning) and applications to data mining, text processing, etc. Students are expected to have basic knowledge of computer, programming skills, and linear algebra. It is beneficial if the students know basic knowledge of probability, statistics and algorithms.
- Pattern Recognition and Machine Learning. Christopher Bishop, Springer, 2006.
- Machine Learning. Tom Mitchell, McGraw-Hill, 1997.
- Additional readings will be provided after each lecture.
- Data representation (e.g., vector space model, language model)
- Basic probability theory (e.g., likelihood, conditional probability, posterior probability, Bayes)
- Basic linear algebra (e.g., linear transformations, eigenvalues, least-squares best fit)
- Programming and other basic CS skills
- C, C++
- Java, C#, .NET
- Perl, Python
- R and Matlab
- Homework (4 assignments, 40%)
- Projects: 40%
- Final presentation: 10%
- Attendance/presentations: 10%
Homework & Projects
There will be four basic homeworks and four projects:
HomeworksHw1: Perceptron implementa
Hw2: PLSI implementa
Hw3: SMO implementa
Hw4: Dirichlet Process Mixtures implementa
ProjectsPrj1: Followback prediction
Prj2: Friendship relationsh
Prj3: Review rating prediction
Prj4: Algorithm analysis for topic models: Implement and compare approximat
Students are required to select three basic homeworks plus one project. The final project is allowed to be completed by a team including two students. You can also determine the subject of the final project by your own and should submit a project proposal and a final project report.
- Daphne Koller and Nir Friedman. Probabilistic Graphical Models. MIT Press, 2009
- John Hopcroft. Computer Science Theory for the Information Age. 2011.
- Michael I. Jordan. An Introduction to Probabilistic Graphical Models. University of California, Berkeley. June 30, 2003.
- Martin J. Wainwright and Michael I. Jordan. Graphical Models, Exponential Families, and Variational Inference, Foundations and Trends in Machine Learning, V1 (1-2), 2008.
- Trevor Hastie, Robert Tibshirani, Jerome Friedman. Elements of Statistical Learning. Springer, 2003.
- Yoshua Bengio. Learning Deep Architectures for AI. Foundations and Trends in Machine Learning, V2 (1), 2009.
- David J.C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.
- Machine Learning by Eric Xing and Aarti Singh. This is the Machine Learning course of CMU, which provides many useful information on this subject.