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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