Course Description

This course offers a comprehensive mathematical foundation for machine learning, covering essential topics from linear algebra, calculus, probability theory, and optimization to advanced concepts, including information theory and statistical inferencs. The course aims to equip students with the necessary mathematical tools to understand, analyze, and implement various machine learning algorithms and models at a deeper level.

Prerequisites

1. We will be using Numpy and Pandas in this class, so you will need to be able to program in python3.
2. You will need familiarity with basic calculus (differentiation, chain rule), linear algebra, and basic probability.

Your Supporters

Instructor:

• Moise Busogi : mbusogi@andrew.cmu.edu

TAs:

• Lawrence Francis:lfrancis@andrew.cmu.edu
• Marie Cynthia Abijuru Kamikazi: mabijuru@andrew.cmu.edu
• Brian Kipkirui: bkipkiru@andrew.cmu.edu
• Ayebilla Avoka: aavoka@andrew.cmu.edu

Assignment Policy

Homework not turned before the due will lose 20%. Each additional day loses another 20%. The lowest quiz grade will be dropped. While students in class are encouraged to discuss the assigned problems, each student should write and submit their own solution and should not copy the solutions of other students. This policy will be strictly enforced in this class. If you have any question about whether some activity would constitute cheating, please feel free to ask. You will be granted a maximum of 3 slack days, slack days are self granted extensions which you can use a maximum of one day per assignment.

Attendance:

Every student is expected to attend every class for the whole class duration. Attendance will be checked randomly using different means, including a pop quiz that also assesses the reading assignment. Unexplained absence will result in deduction of point in the attendance score. The attendance score will also include various ways, such as answering your colleague's questions on Piazza, or being active during class. If you need to miss a lecture because of a medical emergency or something equally significant, please inform the instructor before the start of the class.

Grading Algorithm:

35% Homework, 20% Quizzes, 5% Reading & Attendance, 20% Mid-term Exam, 20% Final Exam

Letter Grading Algorithm:

Grade	Letter Grade
94 - 100	A
90 - 93.99	A-
87 - 89.99	B+
84 - 86.99	B
80 - 83.99	B-
77 - 79.99	C+
74 - 76.99	C
70 - 73.99	C-
67 - 69.99	D+
64 - 67.99	D
61 - 63.99	D-
0 - 60.99	F