Course Info

Everything practical in one place: prerequisites, grading, how the PC Labs work, software setup, and how to reach us.

At a glance

Prerequisites

You should be comfortable with Python and its data stack (pandas, scikit-learn) from first-year courses in machine learning, databases, and big data processing.

No finance background is required — the course builds the finance toolkit from the ground up, starting in Week 1.

Grading

For attending students:

Component Weight
PC Labs (team-based; the lowest score is dropped) 30%
Final project or paper presentation 30%
Final written exam 30%
Class participation 10%

For non-attending students: final project or paper presentation (50%) and final written exam (50%).

Details on the final project / paper presentation and on the exam are announced in class. The course is interactive and hands-on, so attendance is strongly recommended.

PC Labs: how they work

  • One lab per week, done in groups, in Python. Each lab is a short case study with real data — see PC Labs.
  • Groups of 3 students (4 is fine too). Form your group at the start of the semester; it normally stays the same throughout.
  • You submit one Jupyter notebook per group, by email to both the instructor and the TA.
  • Email subject — and first line of the notebook: PCLab#N - Group X - Name1 Name2 Name3.
  • Deadline: Friday at midnight of the lab’s week.
  • Your lab grade rewards: submitting on time, code quality (comments, readability, functions), notebook structure (explain what you do and why), and completing the tasks.
  • After each deadline, a full model solution is published on the lab page.

Use of AI tools

This is a course about extracting signal from data with modern tools — AI assistants are part of that toolbox, and pretending otherwise would be dishonest. The policy:

  • Allowed: using AI tools (ChatGPT, Claude, Copilot, …) in the PC Labs to write and debug code, explore approaches, and polish your writing.
  • Required: you must understand and be able to defend every line you submit — in class we may ask any group member to explain any part of their notebook. Interpretation, economic reasoning, and design choices must be your own: that is what we grade.
  • Disclose it: add a short note in your notebook saying how you used AI (“Copilot for boilerplate, ChatGPT to debug the optimizer” is fine). Honest disclosure is never penalized; hidden reliance you cannot explain is.
  • The written exam is AI-free, so treat the labs as training for your own understanding, not as a delegation exercise.

Software setup

Any working Python 3 + Jupyter setup is fine. Two easy routes:

  • Google Colab — runs in the browser, nothing to install, most packages preinstalled.
  • Local install — the Anaconda distribution ships Python, Jupyter, and most of the scientific stack.

Packages used across the semester (each lab states what it needs — install as you go, or all at once):

pip install pandas numpy matplotlib plotly scikit-learn xgboost catboost nltk gensim transformers beautifulsoup4 yfinance

A couple of labs use extra tools for optional parts (e.g. ekphrasis for tweet preprocessing, TensorFlow/Keras for autoencoders) — they are introduced in the lab text when needed.

Communication & questions

  • This site is the single reference for materials: slides, assignments, datasets, and solutions.
  • Official announcements and course files also go through Bboard.
  • Questions? Ask in class, or post on the course forum — an open Q&A board where the teaching team and your classmates answer. A question asked there helps everyone stuck on the same line; the same question by email helps one person.
  • For anything personal — grades, deadlines, circumstances — email us directly.

Readings & resources

The papers behind the course, by theme. Items marked 📄 are hosted on this site; the lecture slides cite further references week by week. None of this is exam material unless said so in class — read what the current week makes you curious about.

Asset pricing: the classics (Weeks 2–4)

Machine learning in finance (Weeks 4–5, Labs 3–4, 8)

Sustainable finance (Week 6 Hackathon)

Banking (Weeks 7–9)

Payments & crypto (Weeks 10–11)

Documentation you will live in

pandas · scikit-learn · statsmodels · Plotly · nltk