answers. Machine learning algorithms help build models, which as the name suggests is an approximate mathematical model of what outputs correspond to a given input. To illustrate, when you visit Netflix’s homepage very useful, because they help us convert abstract concepts hidden in natural language into a mathematical representation that our models can use. The quality of these models scales with the number of

should be familiar with all terminologies related with this course. You should understand the mathematical theories behind the machine learning algorithm. Practice what you have learned. Work hard!

and analysis of multivariate observations[C]//Proceedings of the fifth Berkeley symposium on mathematical statistics and probability. Oakland, CA, USA, 1(14): 281–297. [9] CHEN T, GUESTRIN C. XGBoost:A

neural network operation is as follows: f(X; W, b) = σ(XW + b) Here, X, W and b are tensors (mathematical term for an n-dimensional matrix) to denote inputs, weights and the bias respectively. To simplify

used in the fields of image and video recognition. It is based on the concept of convolution, a mathematical concept. It is almost similar to multi-layer perceptron except it contains series of convolution

https://medium.freecodecamp.org/an-intuitive-guide-to-convolutional-neural- networks-260c2de0a050 Notation Input_channels: Kernel_channels: 2 ch Kernel_size: Stride: Padding: Multi-Kernels https://skymind

wikipedia.org/wiki/Carl_Friedrich_Gauss 18 https://www.maa.org/press/periodicals/convergence/mathematical‐treasures‐jacob‐kobels‐geometry 19 https://en.wikipedia.org/wiki/Ronald_‐Fisher 32 1. 引言 学 Pitts, W. (1943). A logical calculus of the ideas immanent in nervous activity. The bulletin of mathematical biophysics, 5(4), 115–133. [Merity et al., 2016] Merity, S., Xiong, C., Bradbury, J., & Socher methods of speeding up the convergence of iteration methods. USSR Computational Mathematics and Mathematical Physics, 4(5), 1–17. [Radford et al., 2018] Radford, A., Narasimhan, K., Salimans, T., & Sutskever

∀x, y, j Feng Li (SDU) GDA, NB and EM September 27, 2023 69 / 122 MLE for Naive Bayes (Contd.) Notation: The number of training data whose label is y count(y) = m � i=1 1(y (i) = y), ∀y = 0, 1 The

和 W. Pitts, “A logical calculus of the ideas immanent in nervous activity,” The bulletin of mathematical biophysics, 卷 5, pp. 115-133, 01 12 1943. [2] F. Rosenblatt, The Perceptron, a Perceiving