Syllabus

Topics Covered

This course will cover a range of essential topics in machine learning and data analytics specifically tailored for the finance sector, including:

Data Analytics Fundamentals
Data Preprocessing and Cleaning
Exploratory Data Analysis (EDA)
Feature Engineering
Predictive Modeling Techniques
Machine Learning Workflows
Time Series Analysis
Advanced Machine Learning Techniques
Model Evaluation and Performance Metrics
Applications in Finance
Ethics in Data Science

Homework Assignments

Throughout the course, students will complete several homework assignments aimed at reinforcing the concepts covered in lectures and reading materials (See the homework page, which will be updated during the course). The due dates for these assignments will be posted on this website.

Please follow the instructions to turn in your homework:

Homeworks must be submitted electronically as PDF files.
Files should be named according to the following scheme:
- HW<2-digits homework number>_<LastName>_<FirstName>.pdf.
- For instance, my first homework would be called HW01_Vazan_Milad.pdf.
Email your homework to vazan.sbu@gmail.com

Exams

The course will include two exams. Both exams will assess the required readings and topics covered in class. The first exam will be an “in-class” midterm, while the second will be a final exam scheduled during the University’s final examination period at the end of the semester.

If you need to miss the midterm test due to illness or a family affliction, please contact me by email. A make-up exam for the midterm might be organized.

Attendance and Participation

Attendance is required, and exceeding four absences may result in a penalty of up to 2 points off your total grade. Active participation in both in-class activities and the online message board is highly encouraged.

Grading

Class attendance and participation: 2 points
Homeworks: 5 points
Midterm exam (1404/?/?): 5 points
Final exam (1404/04/02 - 14:00-16:00): 8 points

Seeking Assistance

Here are the available help resources, organized by the urgency of your issue:

Messaging

Our course will utilize a Telegram group (link to be provided in class) as the primary communication platform for announcements and discussions. This is an ideal space for asking questions that can be answered by anyone. It’s best to use this resource for non-urgent inquiries.

Talk with the Instructor

For any issues at all, please reach out to the instructor:

Speak with me before class
Raise your hand or speak up during class

Collaboration Policy

You are encouraged to discuss the content of this course with anyone you like; however, it is essential to maintain academic integrity in your work. All homework assignments, projects, and exams must be completed independently, meaning you are not permitted to copy any part of another student’s solution, collaborate with others on your assignments, or use solutions from unauthorized sources, including the Internet. Therefore, the solution you submit for each assignment must be solely your own, reflecting your understanding and effort.

Generative AI Usage Guidelines

You are encouraged to use Generative AI tools like ChatGPT for general coding-related queries. However, refrain from asking these tools to directly solve problems from the course problem sets.

Textbooks/Materials

My lecture notes are sufficient for students to successfully pass the examination.

There is no single textbook for this class. Instead, the material will be drawn from various texts and sources.

Lecture notes, homework assignments and their solutions, and other supplementary materials will be made available on this website throughout the course.

A note on self care.

Please take care of yourself. Do your best to maintain a healthy lifestyle this semester by eating well, exercising, getting enough sleep and taking some time to relax. This will help you achieve your goals and cope with stress.