多模态 CMU-MOSEI的多模态情感分析体系结构。 描述 该信息库包含四种多模式体系结构以及用于CMU-MOSEI的情感分析的相关培训和测试功能。 在数据文件夹中，提供了转录和标签，以用于的标准培训，验证和测试语句。 可以通过以下链接下载BERT嵌入（文本模式），COVAREP功能（音频模式）和FACET功能（视频模式）： BERT嵌入： ：//drive.google.com/file/d/13y2xoO1YlDrJ4Be2X6kjtMzfRBs7tBRg/view？usp COVAREP： ://drive.google.com/file/d/1XpRN8xoEMKxubBHaNyEivgRbnVY2iazu/view usp sharing 脸部表情： ：//drive.google.com/file/d/1BSjMfKm7FQM8n3HHG5Gn9-dTifULC

10701_Coding CMU 10701机器学习的代码。

10601_代码 这些是为卡耐基梅隆大学2020年Spring机器学习课程生成的代码。整个代码是通过numpy库完成的，并且在任何地方都不会使用scikitlearn / keras / pytorch。 所有模型都是从头开始构建的。 以下是使用的代码： 决策树桩算法：Decisionstump.py 决策树算法：用于计算Gini增益的inspection.py和用于具有二进制属性的实际决策树回归的Decisiontree.py Logistic回归：feature.py用于生成逻辑回归的稀疏矩阵，而lr.py用于生成实际logistic回归 神经网络：与随机梯度下降一起使用的Neuronet.py 语音标记的隐马尔可夫模型：learnhmm.py计算模型的参数，而forwardbackward.py实际预测语音标记的部分 强化学习：使用线性函数逼近通过q学习解决OpenAI提供的

1000行规模的模拟逻辑电路，完全通过Python实现。能够携带1000+逻辑门，拥有链接逻辑门、通电、断电、保存于重置等等操作。

数据库系统课程实验六（exercise10）参考答案，仅供学习参考使用 Exercise 10 Index Choice and Query Optimization

CMU多语种语音数据集：700多种语言的语音/文本对齐语料

CMU-Oxford Sculpture 是一个3D雕塑图像数据，包括 143000 张雕塑图像，来自 242位 艺术家的 2197个 雕塑作品。每张图像包括12个预定义的属性。

CMU的概率图模型PPT（2019年版本），Eric Xing的课，网址https://sailinglab.github.io/pgm-spring-2019/lectures/ 英文无字幕，有一定基础学习效果更好。

上传者不拥有讲义的原始版权。所有版权归属CMU。 该文件集是CMU开设的11-777课程，名为multimodal machine learning，每年fall学期开设。 本讲义是2019 Fall的版本。 课程介绍： Description Multimodal machine learning (MMML) is a vibrant multi-disciplinary research field which addresses some of the original goals of artificial intelligence by integrating and modeling multiple communicative modalities, including linguistic, acoustic and visual messages. With the initial research on audio-visual speech recognition and more recently with language vision projects such as image and video captioning, this research field brings some unique challenges for multimodal researchers given the heterogeneity of the data and the contingency often found between modalities. The course will present the fundamental mathematical concepts in machine learning and deep learning relevant to the five main challenges in multimodal machine learning: (1) multimodal representation learning, (2) translation mapping, (3) modality alignment, (4) multimodal fusion and (5) co-learning. These include, but not limited to, multimodal auto-encoder, deep canonical correlation analysis, multi-kernel learning, attention models and multimodal recurrent neural networks. We will also review recent papers describing state-of-the-art probabilistic models and computational algorithms for MMML and discuss the current and upcoming challenges. The course will discuss many of the recent applications of MMML including multimodal affect recognition, image and video captioning and cross-modal multimedia retrieval. This is a graduate course designed primarily for PhD and research master students at LTI, MLD, CSD, HCII and RI; others, for example (undergraduate) students of CS or from professional master programs, are advised to seek prior permission of the instructor. It is required for students to have taken an introduction machine learning course such as 10-401, 10-601, 10-701, 11-663, 11-441, 11-641 or 11-741. Prior knowledge of deep learning is recommended.

数据库系统课程实验五（exercise9）参考答案，仅供学习参考使用 Exercise 9 Programming with Transactions 事务编程