TEACHING ARCHIVESTIEI
Course Description (for 2014)
Course Description
Tianjin International Engineering Institute
Course Name(Chinese): 应用机器学习
(English):Applied Machine Learning
Course Name: Applied Machine Learning
Course Code:
Semester: 3
Credit:
Programme: Electronic
Course Module: Specialized Subjects
Responsible: Xu Yan
E-mail: xuyan@tju.edu.cn
Department:School of Electronic Information Engineering
Time Layout (1 credit hour = 45 minutes)
Practice
Lecture
Lab-study
8
24
16
Project
Internship(days)
Personal Work
30
Course Resume
Because machine Learning can only be understood through practice, by using the
algorithms, the course is accompanied with home works during which students test a variety of
machine learning algorithm with real world data. The course uses the MLDEMOS TOOLBOX that
entails a large variety of Machine Learning algorithms.
 Binary and multi-class classifiers: LDA, GMM with Bayes, SVM, Boosting
 Pattern recognition and clustering
 Non-linear Regression
 Markov-Based Techniques for Time Series Analysis
Pre-requirements
Probability; Linear Systems; Optimization Methods
Course Objectives
Introduction to the basic principles of the design and analysis of modern digital
communication systems. Topics include source coding; channel coding; baseband and passband
modulation techniques; receiver design; channel equalization; information theoretic techniques;
block, convolutional, and trellis coding techniques; multiuser communications and spread
spectrum; multi-carrier techniques and FDM; carrier and symbol synchronization. Applications to
design of digital telephone modems, compact disks, and digital wireless communication systems
are illustrated. The concepts are illustrated by a sequence of MATLAB exercises.
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TEACHING ARCHIVESTIEI
Course Description (for 2014)
Course Syllabus
1.Introduction
Basic concepts.
2.Supervised learning.
1.1 Supervised learning setup. LMS.
1.2 Logistic regression. Perceptron. Exponential family.
1.3 Generative learning algorithms. Gaussian discriminant analysis. Naive Bayes.
1.4 Support vector machines.
1.5 Model selection and feature selection.
1.6 Ensemble methods: Bagging, boosting.
1.7 Evaluating and debugging learning algorithms.
3.Learning theory.
3.1 Bias/variance tradeoff. Union and Chernoff/Hoeffding bounds.
3.2 VC dimension. Worst case (online) learning.
3.3 Practical advice on how to use learning algorithms.
4.Unsupervised learning.
4.1 Clustering. K-means.
4.2 EM. Mixture of Gaussians.
4.3 Factor analysis.
4.4 PCA (Principal components analysis).
4.5 ICA (Independent components analysis).
5.Reinforcement learning and control.
5.1 MDPs. Bellman equations.
5.2 Value iteration and policy iteration.
5.3 Linear quadratic regulation (LQR). LQG.
5.4 Q-learning. Value function approximation.
5.5 Policy search. Reinforce. POMDPs.
Text Book & References
Richard Duda, Peter Hart and David Stork, Pattern Classification, 2nd ed. John Wiley & Sons,
2001.
Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
Richard Sutton and Andrew Barto, Reinforcement Learning: An introduction. MIT Press, 1998.
Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning.
Springer, 2009.
Capability Tasks
CT1
CT2
CT3
CT4
CT10
Achievements
Choose an appropriate ML method for a given problem.-Level M
Assess / Evaluate appropriately and comparatively ML methods given a set of data.-Level A
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TEACHING ARCHIVESTIEI
Course Description (for 2014)
Apply appropriately ML methods.
Students: Electronic year 3
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