机器学习考试卷 midterm2007s-solution

midterm2007s-solution是机器学习课程的期中考试卷解答。在这份考试卷的解答中，通常会包含以下内容：

1. 简答题解答：对于教授在考试卷上提出的问题，会给出简明扼要的解答。这些问题可能涉及机器学习的基本概念、算法、模型等方面。在解答时，需要清晰地表达思路，用通俗易懂的语言回答问题。

2. 计算题解答：有时候，考试卷上会给出一些机器学习算法的具体计算题，要求学生进行计算并给出答案。在解答这种题目时，需要熟悉各种机器学习算法的具体计算过程，按照流程进行计算，并在最后给出正确的答案。

3. 证明题解答：某些考试卷可能会出现证明题，要求学生通过逻辑推理和数学推导，证明某个机器学习定理或结论。在解答这种题目时，需要理解机器学习的基本理论，运用相关的数学知识和推导方法，清晰地展示证明过程和结果。

4. 分析题解答：考试卷中可能会给出一些机器学习案例或实验数据，并要求学生进行分析和讨论。在解答这种题目时，需要仔细分析案例或数据，提取关键信息，结合课程所学知识，对问题进行合理的分析和解释。

因为无法获取具体的midterm2007s-solution试卷，以上只是对一般情况下该类型试卷的解答内容进行的说明。具体解答还需要参考对应试卷的具体要求来进行。

相关问题

There is a midterm.csv file about the grading result of a course's midterm exam. The first row presents the header. Then, each row presents a student's enrolled name and his or her score. Write a program to read the file and collect all the scores in a list (without header row!) Download the midterm.csv file. Classify those scores according to the criteria given by question 2 in Problem Set 1. Then, draw a histogram with plotly for ranks A, B, C, D, F

Sure! For reading and processing the CSV file, we can use the `csv` module in Python. Here's the code to read and collect all the scores in a list:

import csv

scores = []

with open('midterm.csv', newline='') as csvfile:
    reader = csv.reader(csvfile)
    next(reader)  # skip header row
    for row in reader:
        scores.append(int(row[1]))

print(scores)

This code will create a list `scores` containing all the numeric scores from the CSV file.

For the second part of the question, we need to classify the scores into A, B, C, D, and F categories. According to the criteria given in Problem Set 1, we can use the following classification:

- A: score >= 90
- B: 80 <= score < 90
- C: 70 <= score < 80
- D: 60 <= score < 70
- F: score < 60

Here's the code to classify the scores and count the number of scores in each category:

counts = {'A': 0, 'B': 0, 'C': 0, 'D': 0, 'F': 0}

for score in scores:
    if score >= 90:
        counts['A'] += 1
    elif score >= 80:
        counts['B'] += 1
    elif score >= 70:
        counts['C'] += 1
    elif score >= 60:
        counts['D'] += 1
    else:
        counts['F'] += 1

print(counts)

This code will create a dictionary `counts` containing the number of scores in each category.

Finally, we can use the `plotly` library to create a histogram of the scores. Here's the complete code:

import csv
import plotly.graph_objs as go

scores = []

with open('midterm.csv', newline='') as csvfile:
    reader = csv.reader(csvfile)
    next(reader)  # skip header row
    for row in reader:
        scores.append(int(row[1]))

counts = {'A': 0, 'B': 0, 'C': 0, 'D': 0, 'F': 0}

for score in scores:
    if score >= 90:
        counts['A'] += 1
    elif score >= 80:
        counts['B'] += 1
    elif score >= 70:
        counts['C'] += 1
    elif score >= 60:
        counts['D'] += 1
    else:
        counts['F'] += 1

labels = ['A', 'B', 'C', 'D', 'F']
values = [counts[label] for label in labels]

fig = go.Figure([go.Bar(x=labels, y=values)])
fig.show()

This code will create a histogram of the scores, showing the number of scores in each category.

创建一个MySQL触发器，功能是向sc表中插入平时成绩为空时，把考试成绩为0，如果不为空，则按设定值插入

创建MySQL触发器可以确保当新的学生成绩记录插入到`sc`表时，如果平时成绩为空，自动将考试成绩赋值为0；如果平时成绩已经存在，则保持不变。下面是一个简单的示例，假设`sc`表有两个字段：`student_id`（学生ID），`midterm_score`（平时成绩），`final_score`（考试成绩）。我们可以创建一个`AFTER INSERT`触发器来实现这个功能：

CREATE TRIGGER insert_grading_strategy
AFTER INSERT ON sc
FOR EACH ROW
BEGIN
    IF NEW.midterm_score IS NULL THEN
        -- 如果平时成绩为空，插入考试成绩
        UPDATE sc SET midterm_score = 0 WHERE student_id = NEW.student_id;
    ELSE
        -- 平时成绩已存在，不做任何操作
        -- 或者你可以选择忽略此条件，因为默认已经是这种情况
        -- DO NOTHING;
    END IF;
END;

这个触发器会在每次插入新行之后运行。如果新插入的学生平时成绩`midterm_score`为NULL，会更新该行的`midterm_score`为0。如果你只想检查并修改那些平时成绩为空的新记录，可以在`IF`语句中去掉`IS NULL`。