课程3.周1-1_Welcome【欢迎】-明文传输不
课程3.周1-2_What is clustering【什么是聚类】-明文传输不
课程3.周1-3_K-means intuition【直观解读K-means】-明文传输不
课程3.周1-4_K-means algorithm【详解K-means算法】-明文传输不
课程3.周1-5_Optimization objective【优化目标】-明文传输不
课程3.周1-6_Initializing K-means【初始化K-means】-明文传输不
课程3.周1-7_Choosing the number of clusters【选择聚类数量】-明文传输不
课程3.周1-8_Finding unusual events【异常事件检测】-明文传输不
课程3.周1-9_Gaussian (normal) distribution【高斯（正态）分布】-明文传输不
课程3.周1-10_Anomaly detection algorithm【异常检测算法】-明文传输不
课程3.周1-11_Developing and evaluating an anomaly detection system【开发与评估异常检测系统】-明文传
课程3.周1-12_Anomaly detection vs. supervised learning【异常检测与监督学习的对比】-明文传输不
课程3.周1-13_Choosing what features to use【如何选择特征】-明文传输不
课程3.周2-1_Making recommendations【推荐系统入门】-明文传输不
课程3.周2-2_Using per-item features【物品特征的应用】-明文传输不
课程3.周2-3_Collaborative filtering algorithm【协同过滤算法】-明文传输不
课程3.周2-4_Binary labels：favs, likes and clicks【二元标签：收藏、喜欢与点击】-明文传输不
课程3.周2-5_Mean normalization【均值归一化】-明文传输不
课程3.周2-6_TensorFlow implementation of collaborative filtering【使用TensorFlow实现协同过滤
课程3.周2-7_Finding related items【寻找相关物品】-明文传输不
课程3.周2-8_Collaborative filtering vs Content-based filtering【协同过滤与基于内容过滤的对比】-明文传输
课程3.周2-9_Deep learning for content-based filtering【基于内容过滤的深度学习】-明文传输不
课程3.周2-10_Recommending from a large catalogue【从大规模目录中推荐】-明文传输不
课程3.周2-11_Ethical use of recommender systems【推荐系统的伦理使用】-明文传输不
课程3.周2-12_TensorFlow implementation of content-based filtering【使用TensorFlow实现基于内
课程3.周2-13_Reducing the number of features (optional)【减少特征数量（选修）】-明文传输不
课程3.周2-14_PCA algorithm (optional)【PCA算法（选修）】-明文传输不
课程3.周2-15_PCA in code (optional)【PCA代码实现（选修）】-明文传输不
课程3.周3-1_What is Reinforcement Learning【什么是强化学习】-明文传输不
课程3.周3-2_Mars rover example【火星车示例】-明文传输不
课程3.周3-3_The Return in reinforcement learning【强化学习中的回报】-明文传输不
课程3.周3-4_Making decisions：Policies in reinforcement learning【做出决策：强化学习中的策略】-明文传输
课程3.周3-5_Review of key concepts【关键概念回顾】-明文传输不
课程3.周3-6_State-action value function definition【状态-动作价值函数定义】-明文传输不
课程3.周3-7_State-action value function example【状态-动作价值函数示例】-明文传输不
课程3.周3-8_Bellman Equation【贝尔曼方程】-明文传输不
课程3.周3-9_Random (stochastic) environment (optional)【随机（随机性）环境（选修）】-明文传输不
课程3.周3-10_Example of continuous state space applications【连续状态空间应用示例】-明文传输不
课程3.周3-11_Lunar lander【月球着陆器】-明文传输不
课程3.周3-12_Learning the state-value function【学习状态价值函数】-明文传输不
课程3.周3-13_Algorithm refinement：Improved neural network architecture【算法改进：改良的神经网络
课程3.周3-14_Algorithm refinement：ϵ-greedy policy【算法改进：ϵ-贪婪策略】-明文传输不
课程3.周3-15_Algorithm refinement：Mini-batch and soft updates (optional)【算法改进：小批量和软
课程3.周3-16_The state of reinforcement learning【强化学习现状】-明文传输不
课程3.周3-17_Summary and thank you【总结与致谢】-明文传输不
课程3.周3-18_Andrew Ng and Chelsea Finn on AI and Robotics【吴恩达与切尔西·芬恩谈人工智能与机器人】-明文传
