add_readme.ipynb

Kernel: Python [conda env:root] *

import re
from glob import glob
import requests
import pandas as pd

Get chapter names

chap_names = {}
for chap_no in range(1,24):
    suppl = f"https://raw.githubusercontent.com/probml/pml-book/main/pml1/supplements/chap{chap_no}.md"
    text = requests.get(suppl).text
    names = re.findall(r"Chapter.+?[(](.+)[)]",text)
    chap_names[chap_no] = names[0]
    print(chap_no, names)

1 ['Introduction']
2 ['Probability: univariate models']
3 ['Probability: multivariate models']
4 ['Statistics']
5 ['Decision theory']
6 ['Information theory']
7 ['Linear algebra']
8 ['Optimization']
9 ['Linear discriminant analysis']
10 ['Logistic regression']
11 ['Linear regression']
12 ['Generalized linear models']
13 ['Neural networks for unstructured data']
14 ['Neural networks for images']
15 ['Neural networks for sequences']
16 ['Exemplar-based methods']
17 ['Kernel methods']
18 ['Trees']
19 ['Learning with fewer labeled examples']
20 ['Dimensionality reduction']
21 ['Clustering']
22 ['Recommender systems']
23 ['Graph embeddings']

df = pd.DataFrame(chap_names.items(), columns=["chap_no","chap_name"])
df

df.to_csv("chapter_no_to_name_mapping.csv", index=None)

Create a Readme.md

content = '''
# "Probabilistic Machine Learning: An Introduction"

## Chapters
|Chapter|Name| Notebooks|
|-|-|-|
'''

for chap_no in range(1,24):
    chap_url = f"https://github.com/probml/pyprobml/tree/master/notebooks/book1/{chap_no:02d}"
    content+=f"| {chap_no} | {chap_names[chap_no]} | [{chap_no:02d}/]({chap_no:02d}/) |\n"
content

'\n# "Probabilistic Machine Learning: An Introduction"\n\n## Chapters\n|Chapter|Name| Notebooks|\n|-|-|-|\n| 1 | Introduction | [01/](01/) |\n| 2 | Probability: univariate models | [02/](02/) |\n| 3 | Probability: multivariate models | [03/](03/) |\n| 4 | Statistics | [04/](04/) |\n| 5 | Decision theory | [05/](05/) |\n| 6 | Information theory | [06/](06/) |\n| 7 | Linear algebra | [07/](07/) |\n| 8 | Optimization | [08/](08/) |\n| 9 | Linear discriminant analysis | [09/](09/) |\n| 10 | Logistic regression | [10/](10/) |\n| 11 | Linear regression | [11/](11/) |\n| 12 | Generalized linear models | [12/](12/) |\n| 13 | Neural networks for unstructured data | [13/](13/) |\n| 14 | Neural networks for images | [14/](14/) |\n| 15 | Neural networks for sequences | [15/](15/) |\n| 16 | Exemplar-based methods | [16/](16/) |\n| 17 | Kernel methods | [17/](17/) |\n| 18 | Trees | [18/](18/) |\n| 19 | Learning with fewer labeled examples | [19/](19/) |\n| 20 | Dimensionality reduction | [20/](20/) |\n| 21 | Clustering | [21/](21/) |\n| 22 | Recommender systems | [22/](22/) |\n| 23 | Graph embeddings | [23/](23/) |\n'

readme_file = "../notebooks/book1/README.md"
with open(readme_file,"w") as fp:
    fp.write(content)