浙江大学人工智能课程课件
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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本站所有资源不代表本站的观点或立场，基于网友分享，根据中国法律《信息网络传播权保护条例》第二十二条之规定，若资源存在侵权或相关问题请联系本站客服人员，zhiweidada#qq.com，请把#换成@，本站将给予最大的支持与配合，做到及时反馈和处理。关于更多版权及免责申明参见 版权及免责申明