Led by Andrew Ng, this course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs, practical advice); reinforcement learning and adaptive control. The course will also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing. For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai
Curated by: Stanford Online (21 videos)
For more information about Stanford’s Artificial Intelligence professional and graduate programs, visit: https://stanford.io/ai Andrew Ng Adjunct Professor of Computer Science https://www.andrewng.org/ To follow along with the course schedule and syllabus, visit: http://cs229.stanford.edu/syllabus-autumn2018.html 0:00 Introduction 0:10 Support vector machine algorithm 2:47 Derivation of this classification problem 7:47 Decision boundary 11:58 The represented theorem 13:20 Logistic Regression 26:31 The dual optimization problem 28:48 Apply kernels 28:56 Kernel trick 31:45 A kernel function 33:56 No free lunch theorem 34:40 Example of kernels 54:13 Kernel matrix 59:16 Gaussian kernel 59:39 The gaussian kernel 1:11:57 Dual form 1:13:35 Examples of SVM kernels 1:14:13 Handwritten digit classification 1:15:39 Protein sequence classifier 1:17:03 Design a feature vector