MY-ASSIGNMENTEXPERT™可以为您提供sydney QBUS6850 Machine Learning机器学习课程的代写代考和辅导服务！
这是悉尼大学机器学习课程的代写成功案例。
QBUS6850课程简介
Machine Learning is a fundamental aspect of data analytics that automates analytical model building in modern business. In the big data era, managers are able to use very large and rich data sources and to make business decisions based on quantitative data analysis. Machine Learning covers a range of state-of-the-art methods/algorithms that iteratively learn from data, allowing computers to find hidden patterns and relationships in such data so as to support business decisions. This unit introduces modern machine learning techniques and builds skills in using data for everyday business decision making. Topics include: Machine Learning Foundation; Modern Regression Methods; Advanced Classification Techniques; Latent Variable Models; Support Vector Machines (SVM) and Kernel Methods; Artificial Neural Networks; Deep Learning; and Machine Learning for Big Data. Emphasis is placed on applications involving the analysis of business data. Students will practise applying machine learning algorithms to real-world datasets by using an appropriate computing package.
Prerequisites
At the completion of this unit, you should be able to:
- LO1. differentiate different types of learning algorithms and identify the advantages and limitations of each method
- LO2. build a strong machine learning skill set for business decision making
- LO3. create machine learning models for studying relationship amongst business variables
- LO4. work with various data sets and identify problems within real-world constraints
- LO5. demonstrate proficiency in the use of statistical software, e.g. Python, for machine learning model implementation
- LO6. work productively and collaboratively in a team
- LO7. present and write insights and suggestions effectively, professionally and ethically.
QBUS6850 Machine Learning HELP（EXAM HELP， ONLINE TUTOR）
What is the growth function of axis-aligned perceptrons in $2 \mathrm{D}$ for $N \geq 4$ ? Those perceptrons are all perceptrons with $w_1 w_2=0$. That is, they are vertical or horizontal lines on the $2 \mathrm{D}$ plane. Choose the correct answer; explain your answer.
[a] $4 N+4$
[b] $4 N+2$
[c] $4 N$
[d] $4 N-2$
[e] $4 N-4$
What is the VC dimension of positively-biased perceptrons in 2D? The positively-biased perceptrons are all perceptrons with $w_0>0$. Choose the correct answer; explain your answer.
[a] 0
[b] 1
[c] 2
[d] 3
[e] 4
The “ring” hypothesis set in $\mathbb{R}^3$ contains hypothesis parameterized by two positive numbers $a$ and $b$, where
$$
h(\mathbf{x})= \begin{cases}+1 & \text { if } a \leq x_1^2+x_2^2+x_3^2 \leq b, \ -1 & \text { otherwise. }\end{cases}
$$
What is the growth function of the hypothesis set? Choose the correct answer; explain your answer.
[a] $\left(\begin{array}{c}N+1 \ 1\end{array}\right)+1$
[b] $\left(\begin{array}{c}N+1 \ 2\end{array}\right)+1$
[c] $\left(\begin{array}{c}N+1 \ 3\end{array}\right)+1$
[d] $\left(\begin{array}{c}N+1 \ 6\end{array}\right)+1$
[e] none of the other choices
Following the previous problem, what is the VC dimension of the ring hypothesis set? Choose the correct answer; explain your answer.
[a] 1
[b] 2
[c] 3
[d] 6
[e] none of the other choices
MY-ASSIGNMENTEXPERT™可以为您提供SYDNEY COMP5318 MACHINE LEARNING机器学习课程的代写代考和辅导服务！
