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courses:be4m33ssu [2018/02/11 00:35] marcimat [Zkouška] |
courses:be4m33ssu [2025/01/03 18:23] (aktuální) |
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| Řádek 10: | Řádek 10: | ||
| ===== Zkouška ===== | ===== Zkouška ===== | ||
| - | ===== Materials ===== | ||
| - | Literature | + | ==== 27.1.2017 ==== |
| + | {{:courses:courses_be4m33ssu_20170127.jpg?direct&200|}} | ||
| + | |||
| + | FIXME doplňte prosím zbytek příkladů | ||
| + | ~~DISCUSSION~~ | ||
| + | |||
| + | |||
| + | ===== Helpful materials ===== | ||
| + | |||
| + | **Literature** | ||
| https://web.stanford.edu/~hastie/Papers/ESLII.pdf | https://web.stanford.edu/~hastie/Papers/ESLII.pdf | ||
| - | Majority of SSU subjects understandably explained here: | ||
| - | http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf | ||
| - | SVM | + | Majority of SSU subjects understandably explained here: http://www.cs.huji.ac.il/~shais/UnderstandingMachineLearning/understanding-machine-learning-theory-algorithms.pdf |
| - | MIT best :) https://www.youtube.com/watch?v=_PwhiWxHK8o | + | |
| - | MIT explains, what is SVM, how it works, how we derive dual optimization equation, fundamental components of SVM. | + | **SVM** |
| - | https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/tutorials/MIT6_034F10_tutor05.pdf | + | |
| + | Lecture on SVM on MIT https://www.youtube.com/watch?v=_PwhiWxHK8o | ||
| + | |||
| + | MIT notes: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/tutorials/MIT6_034F10_tutor05.pdf | ||
| https://www.youtube.com/watch?v=IOetFPgsMUc + pokracovanie v part II. a III. | https://www.youtube.com/watch?v=IOetFPgsMUc + pokracovanie v part II. a III. | ||
| - | Neural nets + convolutional | + | **Neural nets + convolutional** |
| https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv | https://www.youtube.com/watch?v=vT1JzLTH4G4&list=PL3FW7Lu3i5JvHM8ljYj-zLfQRF3EO8sYv | ||
| Whole course on neural nets and convolutional networks. Very comprehensive lectures, explained from the basic concepts plus nice motivation examples. | Whole course on neural nets and convolutional networks. Very comprehensive lectures, explained from the basic concepts plus nice motivation examples. | ||
| - | MLE | + | **MLE** |
| First what is likely hood? | First what is likely hood? | ||
| VYD lecture slides (course on FEL,CTU) | VYD lecture slides (course on FEL,CTU) | ||
| https://www.youtube.com/watch?v=2vh98ful3_M | https://www.youtube.com/watch?v=2vh98ful3_M | ||
| - | EM + gaussian mixture | ||
| - | MIT notes for same topic. But it is useful to understand MLE first. | ||
| - | https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/lec15.pdf | ||
| - | Bayes learning | + | **EM + gaussian mixture** |
| - | MIT lecture notes once agian https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/tutorials/MIT6_034F10_tutor06.pdf | + | |
| + | https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-867-machine-learning-fall-2006/lecture-notes/lec15.pdf | ||
| + | **Bayes learning** | ||
| + | |||
| + | MIT notes: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-034-artificial-intelligence-fall-2010/tutorials/MIT6_034F10_tutor06.pdf | ||
| - | GMB | ||
| - | http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/ | ||
| - | + | **GMB** | |
| - | ==== 27.1.2017 ==== | + | http://blog.kaggle.com/2017/01/23/a-kaggle-master-explains-gradient-boosting/ |
| - | {{:courses:courses_be4m33ssu_20170127.jpg?direct&200|}} | + | |
| - | + | ||
| - | FIXME doplňte prosím zbytek příkladů | + | |
| - | ~~DISCUSSION~~ | + | |
