Consider a sequence of approximate value functions $v_0, v_1, v_2, \dots,$ each mapping $\mathcal{S}^+$ to $\mathbb{R}$. The initial approximation, $v_0$ is chosen arbitrarily, and each successive approximation is obtained by using the Bellman equation for $v_\pi$ as an update rule:
for all $s\in\mathcal{S}$.
Code in R for Coursera-ML and CMPUT466/551 in University of Alberta
This post refers to programming exercise 2 in Coursera-ML. All the code can be found in this repo.
setwd("~/Coding/R/CMPUT466/mlclass-ex2")
library(ggplot2)
library(R.matlab)
library(devtools)
source_url("https://raw.githubusercontent.com/ggrothendieck/gsubfn/master/R/list.R")Use a scatter plot to visualize the data.
data <- read.csv("ex2data1.txt", header=FALSE)
X <- as.matrix(data[,c("V1", "V2")])
y <- as.matrix(data$V3)
ggplot(data, aes(x=V1, y=V2, col=factor(V3))) +
geom_point(shape=I(3), size=I(3)) +
labs(x="Exam1 score", y="Exam2 score") +
scale_color_manual(name="", labels=c("Not admitted", "Admitted"), values = c("blue", "red"))
Code in R for Coursera-ML and CMPUT466/551 in University of Alberta
This post refers to programming exercise 5 in Coursera-ML. All the code can be found in this repo.
setwd("~/Coding/R/CMPUT466/mlclass-ex5")
library(ggplot2)
library(R.matlab)
library(devtools)
source_url("https://raw.githubusercontent.com/ggrothendieck/gsubfn/master/R/list.R")Use a scatter plot to visualize the data.
data <- readMat("ex5data1.mat")
X <- data$X
y <- data$y
Xtest <- data$Xtest
ytest <- data$ytest
Xval <- data$Xval
yval <- data$yval
df <- data.frame(X=X, y=y)
ggplot(df, aes(X, y, col="Training data")) + geom_point(shape=I(4), size=I(3)) +
labs(x="Change in water level (x)", y="Water flowing out of the damn (y)") +
scale_color_manual(guide=FALSE, values=c("Red"))
Code in R for Coursera-ML and CMPUT466/551 in University of Alberta
This post refers to programming exercise 1 in Coursera-ML. All the code can be found in this repo.
setwd("~/Coding/R/CMPUT466/mlclass-ex1")
library(ggplot2)
library(MASS)Use a scatter plot to visualize the data.
data <- read.csv("ex1data1.txt", header=FALSE)
ggplot(data, aes(V1, V2, col="Training data")) +
geom_point(shape=I(4), size=I(3)) +
labs(x="Population of City in 10,000s", y="Profit in $10,000s") +
scale_color_discrete(guide=FALSE)
不知不觉已经到加拿大九个月了，虽然一路上磕磕绊绊，但一切似乎都在我预想的轨道上发展，实在有些出乎意料。新的一个学期就要开始了，居然有点踌躇满志的意思呢。
回顾：一开始真的不算顺利，先是遇到一个不怎么样的房东，lease没到就被要求搬走，押金也被扣了。初来乍到，只能认栽了，接下来又稀里糊涂的接了一个快到期的lease，结果住了3个月又得搬家。不到半年就搬了两次家也是醉了……还有吃饭，理发都是不小的问题。不过慢慢的自理能力也上来了，现在基本上生活上已经问题不大了，现在做饭虽然算不上多好吃，但基本饿不死了，周末还可以做个大盘鸡，炖个羊肉汤什么的，剩饭也可以炒个蛋炒饭，感觉还是挺滋润的。不过刚过来的时候碳酸饮料喝的有点多，导致现在牙齿有点敏感，可惜国外看牙太贵了，有机会回国的话一定得把牙齿好好检查一下。
展望：碳酸饮料需要戒掉，甜食也尽量少吃；多多学习烹饪，尽量少出去吃些快餐；多注意一下形象管理，别太邋遢了；还有就是平时多注意锻炼，吃完饭犯困了，可以出去走走，玩会儿pokemon go~
What is probabilistic graphical model (PGM)? Forget about some high-level and complicated sentences on textbook or wikipedia. From my perspective, it’s all about graphs, distributions and the connections between them.
There are three aspects to a graphical model:
Actually, the data is generated by the underlying distribution and the model is the connection between the graph and the distribution. Here comes three fundamental issues:
This page is the public project page and will be updated every week.
There are many infoboxes on wikipedia. Here is a example about football box:
As seen, every infobox has some properties. Actually, every infobox follows a certain template. In my project, the goal is to find mappings between the classes (eg. dbo:Person, dbo:City) in the DBpedia ontology and infobox templates on pages of Wikipedia resources using techniques of machine learning.
There are lots of infobox mappings available for a few languages, but not as many for other languages. In order to infer mappings for all the languages, cross-lingual knowledge validation should also be considered.
The main output of the project will be a list of new high-quality infobox-class mappings.
This blog will record the timeline, resources, projects along the way of my study on machine learning since 2015. And I will keep updating this blog.
As a beginner in machine learning, I plan to sketch out my learning process. And it will be my first post in this series.
We have an input vector $X^T=(X_1, X_2, \dots, X_p)$ and want to predict a real-valued output $Y$. The linear regression model has the form
Here the $\beta_j$’s are unknown parameters or coefficients.