Class Announcements

Check on Learning - Block 1 (post-gradient descent): https://colab.research.google.com/drive/15sdczpINY5w3SRXBYXmLFo-gnEAxMBqf

Mathletes,

Welcome to MA477 – Theory and Application of Data Science! I hope you had a great winter break and are looking forward to getting back to work this semester. LTC Weld and I are looking forward to working with each of you this semester.

Do the following before class on day 1:

1. Visit the and bookmark the course website: https://sites.google.com/view/ma477/home

2. Download the course admin documents – there should be a calendar, course guide and CD memo here: https://sites.google.com/view/ma477/admin

3. If you do not have Anaconda Python installed, visit here https://www.anaconda.com/products/individual#download-section, download and install Python 3.9.

4. The three books needed for this course are:

(1) Introduction to Python for Computer and Data Science (https://www.amazon.com/Intro-Python-Computer-Science-Data/dp/0135404673/ref=sr_1_3?keywords=intro+to+python+for+computer+science+and+data+science&qid=1640878332&sprefix=Intro+to+python%2Caps%2C70&sr=8-3)

(2) Hands-On Machine Learning with Scikit-Learn,Keras and TensorFlow (https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_3?crid=3I6Y438QMXJLT&keywords=Hands-On+Machine+Learning+with+Scikit-Learn%2CKeras+and+TensorFlow&qid=1640878479&sprefix=hands-on+machine+learning+with+scikit-learn%2Ckeras+and+tensorflow%2Caps%2C92&sr=8-3 ) and

(3) An Introduction to Statistical Learning with R (ISLR) – it can be downloaded here: https://statlearning.com/ - look for the “Download the book PDF”

5. Sign up for a cocalc account (www.cocalc.com) if you do not have one already and email me your email address that you used - they will need to bring you into the course before paying the $14 fee.

Use this link to view the student guide to cocalc: https://doc.cocalc.com/teaching-students.html

6. Lesson prep for this course is as follows (2 hours per night):

1) Read the section(s) in the book(s).

2) Read the Jupyter Notebook.

3) Watch the videos.

4) Do the suggested problems.