機器學習

* Note: Due to COVID19, please use this link for the online course before you get registered in the class: https://meet.google.com/hgt-uhxc-mix (1) To build big picture on machine learning field and equip with the ability of implementation machine learning techniques. This course will introduce the theory behind the techniques, so a great deal of time will spend on the mathematics foundation. (2) To understand the properties of different learning algorithms and learn how to use, when to use, which to use, under different scenarios.

Calculus, Probability, Statistics, Linear Algebra, Introduction of Machine learning or equivelent

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Involvement (10%), homework (30%) mid term (30%), final (30%)

• Probability and information theory

  Basics of Machine learning, Shannon Entropy, Bayes' theorem, Naive Bayes classifier

  講授：

  6

• Regression and classification

  Maximum likelihood, SSE, Linear regression, BIC, Logistic regression, overfitting / regularization (Ridge and Lasso), model selection

  講授：

  6

• Dimension reduction and feature extraction

  SVD, FFT, PCA and NMF

  講授：

  6

• Distribution and Statistics

  Gaussian integral, Central limit theory, Gaussian distribution, Moments of distribution, moment generation function

  講授：

  6

• Kernel methods

  Kernel methods, Gaussian Process, Support Vector Machine

  講授：

  6

• Generative Models - Clustering

  K-Means, Kernel K-Means, Spectral Clustering, DBSCAN, Hierarchical Clustering

  講授：

  6

• Generative Models - Dimensionality Reduction

  PCA, Kernel PCA, LDA, IsoMap, LLE, Laplacian Eigenmap, ICA, t-SNE

  講授：

  6

• Generative Models - Graphical Models

  Directed Graph (Bayesian Networks), Undirected Graph (Markov Random Fields), Factor Graph / Belief Propagation, HMM, Sampling

  講授：

  6

[1] Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2007 [2] A. Smola and S.V.N. Vishwanathan, Introduction to Machine Learning, Cambridge University Press, Oct. 2010

地點

office

時間

TBA

聯絡方式

email