added 1 report(21270) and modified significantly another report(10601)

README.md

@@ -75,6 +75,7 @@ expect from the core classes from the ECE and CS programs at CMU.
 - [18-847F: Foundations of Cloud and Machine Learning Infrastructure](electives/18847F.md)
 - [18-898D: Graph Signal Processing and Geometric Learning](electives/18898D.md)
 - [24-104: Maker Series: Intro to Modern Making](electives/24104.md)
+- [21-270: Introduction to Mathematical Finance](electives/21270.md)
 - [80-180: Nature of Language](electives/80180.md)
 - [80-405: Game Theory](electives/80405.md)
 - [82-208: Eastern Europe: Society and Culture](electives/82208.md)

electives/10601.md

@@ -1,4 +1,4 @@
-# 10-601: Introduction to Machine Learning
+# 10-301/601: Introduction to Machine Learning
 
 | Category | Difficulty |
 |:-:       | :-:        |
@@ -9,6 +9,14 @@ Intro to ML is one of the most popular CS electives at CMU, as ML has been a ver
 The class will cover a good variety of ML concepts, but does not go too much into detail since it is 
 just an introductory class.
 
+## Prerequisites
+
+- One probability course, be it 36-225, 36-217, or some equivalent
+- Concepts (21-127, 15-151 or equivalent)
+- 15-122
+
+Skipping these prerequisits, especially the math ones, is not recommended. This course is essentially half math.
+
 ## Topics covered
 
 - **Decision Trees**
@@ -39,14 +47,13 @@ The class is very well organized, and follows the approximate structure:
 
 ## Homeworks
 
-Homeworks are straightforward implementations of algorithms that are covered in class. Lecture slides and the lectures themselves go into algorithms in a great depth, so the homeworks should not be too bad. In addition, since the algorithms are often well-known in the ML field, you can
-find lots of support for them online as well. The handouts provided for the homework is detailed, so make sure to read that as well. Definitely make sure to write your own tests for your program if it is possible, since that can help you uncover bugs you can't find on Autolab.
+Homework is usually split into programming and written sections. There are also times when homework is exclusively written. No extensive knowledge of latex is needed as a template is usually provided, although it is helpful to know. For many, the programming part may be easier than the written part. The written portion of the assignment is usually key to understanding aspects of the programming section, so skimming the written portion at bare minimum is extremely important. 
+
+The programming sections of homeworks are straightforward implementations of algorithms that are covered in class. Lecture slides and the lectures themselves go into algorithms in a great depth, so the homeworks should not be too bad. In addition, since the algorithms are often well-known in the ML field, you can find lots of support for them online as well. The handouts provided for the homework is detailed, so make sure to read that as well. Definitely make sure to write your own tests for your program if it is possible, since that can help you uncover bugs you can't find on Gradescope.
 
 As a trick, sometimes your program might take a while to run on your computer. If you run your program on the Andrew machines at CMU instead, you might find a considerable boost in your program speed. 
 
-There are often also short-answers for homeworks. They give you LaTeX
-templates to fill in, but you don't need to know that much LaTeX to work
-with it.
+Please note that it is best to use Python for the programming assignments because almost all the TAs and instrutors are best equipped to debug Python code. In prior semesters, Java, C++, and Python were allowed, however as of Spring 2022 only Python is allowed for homework submission.
 
 ## How to study for exams
 
@@ -55,10 +62,13 @@ breadth of material so it is hard to learn everything in depth.
 For exams, it is important to know all your **equations and algorithms**.
 Fortunately, you have a cheatsheet so you can put those on there!
 
+The best thing you can do is to take the practice exams from the course website. 
+There is a chance that the exact same question shows up in the actual exam.
+
 In addition, you can also do other school's exams for extra practice on the topic.
 
-## External materials for the class
 
-The good news about ML is that it is a very widely taught course, so there are lots of good materials about it outside of CMU. The most notable source for ML education is Andrew Ng's CS229 course at Stanford. His course notes are great and his class has lots of great content.
 
-You can find [course materials here](https://github.com/SKKSaikia/CS229_ML).
+## External materials for the class
+
+The good news about ML is that it is a very widely taught course, so there are lots of good materials about it outside of CMU. The most notable source for ML education is Andrew Ng's CS229 course at Stanford. His course notes are great and his class has lots of great content.

electives/21270.md

@@ -0,0 +1,35 @@
+# 21-270: Introduction to Mathematical Finance
+
+| Category | Difficulty |
+| :------: | :--------: |
+|   HW     |     3      |
+|  Exams   |     4      |
+| Quizzes  |     3      |
+
+Disclaimer: Course taken remotely in Spring 2021. 
+
+Course is a math/science elective possible for ECE majors. This course is an introduction to mathematical finance and an introductory course for the computational finance minor/major. This course is primarily using math to price items such as bonds and options. Heavily abstracted, so don't expect to take this course and immediately be able to model the prices of bonds and options, however it is a good starting off point.
+
+## Homework
+
+Homework is due weekly, and its topics are tested on quizzes the following week. Homework at the beginning of the semester is mainly plug and chug, however the homework shifts towards more proof based questions towards the end of the semester. Some of them may be a bit tricky, so don't be afraid of going to office hours.
+
+## Quizzes
+
+Quizzes are assigned weekly and are restated or a variation on homework questions and topics presented in the previous week. One note sheet allowed for quizzes, otherwise closed book.
+
+## Exam
+
+There is one midterm and one final exam. Both are closed book, but one note sheet is allowed. Exam questions are just variants of the quiz questions, so there won't be shock questions that you've never seen before. Remember to pace yourself.
+
+## Course Structure
+15% Homework
+40% Quizzes
+20% Midterm
+25% Final
+
+## Tips to succeed
+
+The quizzes make up a lot of your grade. Don't skip them.
+I would suggest having one note sheet that you keep throughout the semester. Add topics onto the note sheet every week for the quizzes, and you will no longer find the need to create a note sheet by the time the midterm and final happens.
+