CS7646 Spring 2017

Overview

You are on the page for information specific to the Spring 2017 session of this course. Go here (Machine_Learning_for_Trading_Course) for overall course policies.

2017 Spring Schedule

• [[1]]

Assignments & Grading

Projects:

• [MC1-Project-1: Assess portfolio] 5% (easy)
• [MC1-Project-2: Optimize a portfolio] 5% (easy)
• [MC2-Project-1: Build a market simulator] 10% (moderate)
• [MC3-Project-1: Implement and assess a regression learner using decision trees and random forests] 15% (hard)
• [MC3-Project-2: Q-learning maze navigation] 10% (easy)
• [MC3-Project-3: Implement a "manual" quant strategy, then do it with decision tree classification] 15% (very hard)
• [MC3-Project-4: Q-learning trader] 10% (moderate)
• [MC3-Project-5: Q-learning deathmatch 2000] 15% (extremely hard)

• [MC3-Homework-1: Generate datasets that defeat learners] 5%
• Midterm Study Guide
• Midterm 10%

• Class participation: 2%.

Thresholds:

• A: 90% and above
• B: 80% and above
• C: 70% and above
• D: 60% and above
• F: below 60%

The projects linked to below are from previous semesters. We keep them here so you can peek ahead, but please keep in mind that they will be revised.

• [MC2-Project-2: Implement bollinger bands, and create a simple trading strategy]
• [MC3-Homework-1: Create a Finance midterm question]
• [MC3-Project-1]
• [MC3-Project-2]
• [MC3-Project-3]