Course Code: B63008H
Course Name: Introduction to Machine Learning (B)
Pre-requisite: Advanced algebra, Probability and Mathematical Statistics
Lecture Time: 10 weeks, 2 sessions/week, 2 hours/session
Instructors: Dr. Yanyan Lan
This course is designed for undergraduate students who should master basic principle and method. The requirements are: (1) Have a basic understanding of ideas and methods of machine learning; (2) Master methods of supervised learning, unsupervised learning, and semi-supervised learning. (3) Have a basic understanding of machine learning theory.
Topics and Schedule
1.1. History of machine learning and practical application
1.2. Introduction of relevant mathematics knowledge
2.1. Linear regression
2.3. Decision tree
2.4. Logistic regression
2.5. Naïve Bayesian Model
2.6. Maximum likelihood and maximum a posteriori
2.7. Support Vector Machine
2.8. Regularization and model selection
3.1. Sample complexity
3.2. VC dimension
3.3. Generalization bounded
4.1. EM method
4.2. Gaussian Mixture
4.3. Hidden Markov Model
5.1. K-Means clustering
5.2. Principal Component Analysis (PCA)
5.3. Singular Value Decomposition (SVD)
5.4. Fisher LDA
5.6. Artificial Neural Networks
Students will be given a big project demonstration (2 hrs). Design algorithm or programming to solve practical problems according to the learned knowledge such as recommendation system, search engine, and etc.
M. Bishop (2006), Pattern Recognition and Machine Learning. Springer.
Trevor Hastie, Robert Tibshirani, and Jerome Friedman (2001). The Elements of Statistical Learning. Springer.
Kevin Patrick Murphy, Machine Learning: A Probabilistic Perspective, the MIT Press