学习浙大可视化课件1

通过一个学期的可视化系统开发实战,暴露了我的很多问题,除了编程能力上的不足外,还有对可视化理论缺乏了解。所以,为了更深入的理解可视化理论体系,我开始学习浙江大学可视化系列课件。此次学习前三章,我将其总结为思维导图如下:

1_数据可视化简介_屈华民

2_视觉感知与视觉通道_屈华民

3_数据基础_方晓芬

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浙江大学人工智能课程课件,内容有: Introduction Problem-solving by search( 4 weeks) Uninformed Search and Informed (Heuristic) Search (1 week) Adversarial Search: Minimax Search, Evaluation Functions, Alpha-Beta Search, Stochastic Search Adversarial Search: Multi-armed bandits, Upper Confidence Bound (UCB),Upper Confidence Bounds on Trees, Monte-Carlo Tree Search(MCTS) Statistical learning and modeling (5 weeks) Probability Theory, Model selection, The curse of Dimensionality, Decision Theory, Information Theory Probability distribution: The Gaussian Distribution, Conditional Gaussian distributions, Marginal Gaussian distributions, Bayes’ theorem for Gaussian variables, Maximum likelihood for the Gaussian, Mixtures of Gaussians, Nonparametric Methods Linear model for regression: Linear basis function models; The Bias-Variance Decomposition Linear model for classification : Basic Concepts; Discriminant Functions (nonprobabilistic methods); Probabilistic Generative Models; Probabilistic Discriminative Models K-means Clustering and GMM & Expectation–Maximization (EM) algorithm, BoostingThe Course Syllabus Deep Learning (4 weeks) Stochastic Gradient Descent, Backpropagation Feedforward Neural Network Convolutional Neural Networks Recurrent Neural Network (LSTM, GRU) Generative adversarial network (GAN) Deep learning in NLP (word2vec), CV (localization) and VQA(cross-media) Reinforcement learning (1 weeks) Reinforcement learning: introduction
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