Weekly Materials
Lecture slides, week 4
Professor's lecture slides (PDF)
Discussion of the previous exercises; Finger exercises (with TA)
Hands-on tutorials and practice exercises
→
Exercise 2
Distribution of Exercise sheet 2
→
References & Resources
Finger Exercises
Discussion of the previous exercises; Finger exercises (with TA)
Additional Notes
Week 4: Supervised Learning - Regression
Learning Objectives
- Understand the fundamental concepts of supervised learning
- Master linear and polynomial regression techniques
- Learn optimization methods including gradient descent
- Apply regression models to real-world datasets
- Understand model complexity and overfitting
Topics Covered
- Supervised Learning: The general framework and approach
- Linear Regression: Single and multiple variables
- Gradient Descent: Optimization algorithm for model training
- Polynomial Regression: Non-linear relationships
- Tuning Model Complexity: Bias-variance tradeoff
- Practical Applications: Stock market prediction (if time permits)
- Data Manipulation: Introduction to Pandas
Schedule
- Lecture: Monday, October 6, 2025 (10:15 - 12:00)
- Practice Session: Monday, October 6, 2025 (16:30 - 18:00)
- TA Session: Discussion of exercises and finger exercises
Key Concepts
- Cost functions and loss minimization
- Normal equation vs gradient descent
- Feature scaling and normalization
- Model evaluation metrics (MSE, MAE, R²)
- Overfitting vs underfitting
Practical Skills
- Implementing regression from scratch
- Using scikit-learn for regression tasks
- Data preprocessing with Pandas
- Model evaluation and validation
Assignments
- Exercise 2: Distributed this week
- Complete finger exercises with TA guidance
- Practice implementing gradient descent