Week 1

主要記住的知識(shí)點(diǎn)：

1.Regression & Classification

? ?1.回歸問(wèn)題的應(yīng)用場(chǎng)景

回歸問(wèn)題通常是用來(lái)預(yù)測(cè)一個(gè)值，如預(yù)測(cè)房?jī)r(jià)驼唱、未來(lái)的天氣情況等等蔗彤，例如一個(gè)產(chǎn)品的實(shí)際價(jià)格為500元，通過(guò)回歸分析預(yù)測(cè)值為499元，我們認(rèn)為這是一個(gè)比較好的回歸分析蝇更。一個(gè)比較常見(jiàn)的回歸算法是線性回歸算法（LR）。另外呼盆，回歸分析用在神經(jīng)網(wǎng)絡(luò)上年扩，其最上層是不需要加上softmax函數(shù)的，而是直接對(duì)前一層累加即可访圃〕茫回歸是對(duì)真實(shí)值的一種逼近預(yù)測(cè)扇商。

? ?2.分類問(wèn)題的應(yīng)用場(chǎng)景

分類問(wèn)題是用于將事物打上一個(gè)標(biāo)簽居灯，通常結(jié)果為離散值。例如判斷一幅圖片上的動(dòng)物是一只貓還是一只狗恭垦，分類通常是建立在回歸之上圈匆，分類的最后一層通常要使用softmax函數(shù)進(jìn)行判斷其所屬類別漠另。分類并沒(méi)有逼近的概念，最終正確結(jié)果只有一個(gè)跃赚，錯(cuò)誤的就是錯(cuò)誤的笆搓，不會(huì)有相近的概念。最常見(jiàn)的分類方法是邏輯回歸纬傲，或者叫邏輯分類满败。

2.supervised learning & ?unsupervised learning

個(gè)人覺(jué)得最好分辨的是，監(jiān)督學(xué)習(xí)是確定了數(shù)據(jù)集中一定存在某種關(guān)系叹括，而另一種是去探究它們有沒(méi)有關(guān)系算墨。

3.Machine learning definition

第一周包含的話題有以下幾個(gè)

以下是week 1 的測(cè)試題目：

Quiz Introduction

1.

A computer program is said to learn from experience E with respect to some task T and some performance measure P if its performance on T, as measured by P, improves with experience E. Suppose we feed a learning algorithm a lot of historical weather data, and have it learn to predict weather. What would be a reasonable choice for P?

(x) The probability of it correctly predicting a future date's weather.

( ) The weather prediction task.

( ) The process of the algorithm examining a large amount of historical weather data.

( ) None of these.

2.

Suppose you are working on weather prediction, and your weather station makes one of three predictions for each day's weather: Sunny, Cloudy or Rainy. You'd like to use a learning algorithm to predict tomorrow's weather.

Would you treat this as a classification or a regression problem?

(x) Classification

( ) Regression

3.

Suppose you are working on stock market prediction, Typically tens of millions of shares of Microsoft stock are traded (i.e., bought/sold) each day. You would like to predict the number of Microsoft shares that will be traded tomorrow.

Would you treat this as a classification or a regression problem?

( ) Classification

(x) Regression

4.

Some of the problems below are best addressed using a supervised learning algorithm, and the others with an unsupervised learning algorithm. Which of the following would you apply supervised learning to? (Select all that apply.) In each case, assume some appropriate dataset is available for your algorithm to learn from.

[ ] Given data on how 1000 medical patients respond to an experimental drug (such as effectiveness of the treatment, side effects, etc.), discover whether there are different categories or "types" of patients in terms of how they respond to the drug, and if so what these categories are.

[x] Have a computer examine an audio clip of a piece of music, and classify whether or not there are vocals (i.e., a human voice singing) in that audio clip, or if it is a clip of only musical instruments (and no vocals).

[x] Given genetic (DNA) data from a person, predict the odds of him/her developing diabetes over the next 10 years.

[ ] Given a large dataset of medical records from patients suffering from heart disease, try to learn whether there might be different clusters of such patients for which we might tailor separate treatments.

5.

Which of these is a reasonable definition of machine learning?

( ) Machine learning is the field of allowing robots to act intelligently.

(x) Machine learning is the field of study that gives computers the ability to learn without being explicitly programmed.

( ) Machine learning is the science of programming computers.

( ) Machine learning learns from labeled data.