My First Commit

I am a student from University of Nottingham.
I am not a Blogger.
I didn't used to write anything in my spare time.
But now i am going to change.

I want to log what i learned in and extra-curriculum. As an amateur programmer, it is a platform for me and others to discuss, recap and discover.

First, i want to summarise what i learned about Data Structure and Algorithm, which will grab something from "G52ACE" course and other resources.

I'd try my best to update everyday and by Christmas i might write something about Operating System and Git.

After the exam in Jan, i will started to write AI things and Android Development.

Let's get started!
地点: 英国诺丁汉

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[MLE] W2 Multivariate linear regression

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Multivariate linear regression What we have talked about in week 1 is linear regression with single feature. For example, in the house pricing predicting problem, we only use the size of house (x) to predict the price (y) . But what if there are other features like age, bedroom numbers and so on? Linear regression with multiple variables is also known as “multivariate linear regression” According to the graph above, we now have new representation symbols: n = number of features m = number of training examples \(x^{(i)}\) = \(i^{th}\) training example \(x^{(i)}_j\) = feature j in \(i^{th}\) training example 1416 is \(x_{1}^{(2)}\) in this case, m is 47 for example, n is 4. Hypothesis function The multivariable form of the hypothesis function accommodating these multiple features is as follows: if we make \(x_0\) to 1, the \(h_{\theta}(x)\) will be \(\theta^Tx\), which is \(\theta\) transpose matrix multiply by x, where \(\theta\) is a vector of 1 by(n+1) dimension an
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[MLE] W1 Introduction

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Before get started The blog is based on the machine learning course on Coursera, taught by Andrew Ng . From today, i will begin my study trip on Machine learning, which will be labelled as MLE. This course will last 11 weeks and today is week 1. The application of machine learning is everywhere: Database mining Applications can’t program by hand Self-customizing program Understanding human learning(brain, real AI) Introduction What is Machine Learning Two definitions of machine learning are offered: Arthur Samuel described it as: “the field of study that gives computers the ability to learn without being explicitly programmed.” This is an older, informal definition. Tom Mitchell provides a more modern definition: “A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E.” If we raise a example of what is machine learning: If
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[AIM] MetaHeuristics

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Day2 Outline Metaheuristics -definition Single point based search method(simple stochastic local search) Iterated local search Scheduling Tabu Search Metaheuristic what is a metaheuristic? A metaheuristic is a high-level problem independent algorithmic framework that provides a set of guidelines or strategies to develop heuristic optimization algorithms. what is the difference between heuristic and metaheuristic? Heuristic : Heuristics are problem-dependent techniques. As such, they usually are adapted to the problem at hand and they try to take full advantage of the particularities of this problem. However, because they are often too greedy, they usually get trapped in a local optimum and thus fail, in general, to obtain the global optimum solution. Metaheuristic : Meta-heuristics, on the other hand, are problem-independent techniques . As such, they do not take advantage of any specificity of the problem and, therefore, can be used as black boxes . In general, t
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