发帖
雷达卡
This course will cover the science of machine learning. It focuses on the mathematical foundations and analysis of machine learning methods and how they work.
课题包括：
- Overview of machine learning: learning from data; overfitting, regularization, cross-validation.
- Supervised learning: decision trees, naive Bayes, logistic regression, support vector machines, neural networks
- Unsupervised and semi-supervised learning: clustering (k-means, Gaussian mixtures); principal components analysis
- Learning theory: probably approximately correct (PAC) learning, model complexity, bias and variance
- Structured models: Bayesian networks, Markov random fields, hidden Markov models
- Deep learning: convolutional neural networks, recurrent neural networks
- Other topics: reinforcement learning, machine learning applications (vision, natural language processing, recommendation)
slides.zip
(68.11 MB, 需要: 10 个论坛币)
ml19-hw4-solution-master.zip
(3.5 MB, 需要: 25 个论坛币)
ml19-hw3-solution-master.zip
(459.45 KB, 需要: 25 个论坛币)
ml19-hw2-solution-master.zip
(553.21 KB, 需要: 25 个论坛币)
ml19-hw1-solution-master.zip
(7.55 MB, 需要: 25 个论坛币)
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