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Machine Learning

iTunes U




Yaser Abu-Mostafa, Unknown, Jerry Cain, Oussama Khatib, Mehran Sahami, Julie Zelenski, Andrew Ng, Nicholas Outram

Computer Science



A real Caltech course, not a watered-down version This is an introductory course on machine learning that can be taken at your own pace . It covers the basic theory, algorithms and applications. Machine learning ( Scientific American introduction ) is a key technology in Big Data, and in many financial, medical, commercial, and scientific applications. It enables computational systems to adaptively improve their performance with experience accumulated from the observed data. Machine learning is one of the hottest fields of study today, taken up by graduate and undergraduate students from 15 different majors at Caltech. The course balances theory and practice, and covers the mathematical as well as the heuristic aspects. The lectures follow each other in a story-like fashion; what is learning? can we learn? how to do it? how to do it well? what are the take-home lessons? The technical terms that go with that include linear models, the VC dimension, neural networks, regularization and validation, support vector machines, Occam's razor, and data snooping. The focus of the course is understanding the fundamentals of machine learning. If you have the discipline to follow the carefully-designed lectures, do the homeworks, and discuss the material with others on the forum, you will graduate with a thorough understanding of machine learning, and will be ready to apply it correctly in any domain. Welcome aboard! Tips on taking the course: Prerequisites: Basic probability, matrices, and calculus. Familiarity with some programming language or platform will help with the homework. The lectures: The 18 lectures use incremental viewgraphs to simulate the pace of blackboard teaching. Detailed explanations and insights will guide you through the difficult parts of the theory and make you understand where the techniques came from. Our focus is on real understanding, not just "knowing." Homework: After every 2 lectures, there is a homework based on what was covered in these lectures. We recommend that you complete the homework then check your answers before you move on to the next lecture. Forum: You can discuss the course material and ask questions on the course forum where there is a dedicated section for each homework. Live lectures: This course was broadcast live from the lecture hall at Caltech, including Q&A sessions with online audience participation. Here is a sample of a live lecture as the online audience saw it in real time.

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