December 13, 2016
Machine Learning | 代写 | CSE 491: Introduction to Machine Learning (Fall 2016)

CSE 491: Introduction to Machine Learning (Fall 2016)
Exam 3 Take Home, Due: 11:30AM on Dec 14, 2016
• The exam should be completed independently and discussions of any type are NOT allowed.
• A PDF version should be electronically submitted to D2L Dropbox with the file name
LastName_FirstName_CSE491EXAM3.pdf
1. (20 points) Support Vector Machines. Given two data points x1 = (1, 0)T , y1 = −1, and x2 = (3, 0)T , y2 = 1.
(a) Compute the optimal w and b in support vector machine by solving the primal formu- lation given as follows:
min 1wTw w,b 2
subject to yi(wT xi + b) ≥ 1, ∀i.
(b) Compute the optimal α in the dual formulation of support vector machine.
(c) Compute the optimal w based on the optimal α obtained from the dual formulation of support vector machine and compare with the results in (a).

June 24, 2016
Machine Learning | Sentiment Analysis on Tweets about the Scotland Independent Vote in 2014

Topic Description
As we know the result of Scotland independent vote is that Scotland still a part of the UK in 2014. In this practicum, we will implement an appropriate machine learning model which could predict the opinion (supportive or not opposed or neutral) of the related tweets to Scotland independence.

Machine Learning
Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI.

Sentiment Analysis
Sentiment analysis, also called opinion mining or sentiment mining, is a process which analysing and reasoning the subjective sentimental text, is the detection of attitudes.

Brief Study Steps
1. Download the tweets (as the dataset) through the twitter’s API, i.e. each tweet should at least has the hashtag like #Scotland #independent #vote etc. before 18 September 2014 (the voting date), or other conditions could show that tweet is related to the Scotland independent vote around that time. Then clean the dataset, divide it into training set (more than 500 tweets), tuning set (more than 500 tweets) and testing set (more than 500 tweets).

2. Annotate each tweet in the training set as “positive”, “negative” or “neutral”.

3. Training a machine translation model to predict the attitude of the tweets whether it was supportive or opposed or neutral to Scotland independence. Different machine learning models/algorithms/functions will be tested with the combination of different feature selection and parameter setting.

May 14, 2016
Machine Learning | Neural Network

Machine learning is the science of getting computers to act without being explicitly programmed. In the past decade, machine learning has given us self-driving cars, practical speech recognition, effective web search, and a vastly improved understanding of the human genome. Machine learning is so pervasive today that you probably use it dozens of times a day without knowing it. Many researchers also think it is the best way to make progress towards human-level AI. In this class, you will learn about the most effective machine learning techniques, and gain practice implementing them and getting them to work for yourself. More importantly, you’ll learn about not only the theoretical underpinnings of learning, but also gain the practical know-how needed to quickly and powerfully apply these techniques to new problems. Finally, you’ll learn about some of Silicon Valley’s best practices in innovation as it pertains to machine learning and AI. This course provides a broad introduction to machine learning, datamining, and statistical pattern recognition. Topics include: (i) Supervised