Introduction to machine learning
CS-233
This file is part of the content downloaded from Introduction to machine learning.
Introduction to the Class
Nearest Neighbors
Linear Models
Non Linear Models
Optimization
Regression
Deep Learning
Dimensionality Reduction
Conclusion
Data representations and processing
P&P and coding exercise sessions: Work on your team project (Milestone 1 due on Sunday)!
Deep learning (part 3)
Dimensionality reduction
Clustering
Review session
P&P and coding exercise sessions: Work on your team project (Milestone 2 on Friday)!
Pen and Paper Exercises
- Pen & Paper Exercise 1 - KNN (File)
- Pen & Paper Exercise 1 - KNN solution (File)
- Pen & Paper Exercise 2 - KMeans (File)
- Pen & Paper Exercise 2 -KMeans Solution (File)
- Pen & Paper Exercise 3 - Logistic Regression (File)
- Pen & Paper Exercise 3 - Logistic Regression Solution (File)
- Pen & Paper Exercise 5 (File)
- Pen & Paper Exercise 5 - Solution (File)
- Pen & Paper Exercise 6 - SVM (File)
- Pen & Paper Exercise 6 SVM Solution (File)
- Pen & Paper Exercise 7 - Kernels (optional) (File)
- Pen & Paper Exercise 7 - Kernels (Solution) (optional) (File)
- Pen & Paper Exercise - Decision Tree (File)
- Pen & Paper Exercise Solution - Decision Tree (File)
- Pen & Paper Exercise Optimization (File)
- Pen & Paper Exercise Optimization Solution (File)
- Pen & Paper Exercise - Linear Regression (File)
- Pen & Paper Exercise - Linear Regression Solution (File)
- Pen & Paper Exercise 9 - Deep Learning (File)
- Pen & Paper Exercise 9 Solution (File)
- Pen & Paper Exercise - CNN (File)
- Pen Paper Exercise CNN Solution (File)
- Pen & Paper Exercise 11 - Transformers (File)
- Pen & Paper Exercise 11 Solution (File)
- Pen & Paper Exercise 12 - PCA (File)
- Pen & Paper Exercise 12 Solution (File)
Coding Exercises
- matplotlib cheatsheet (File)
- NumPy cheatsheet (File)
- Python cheatsheet (File)
- Exercise 1 - Introduction to Python (File)
- Exercise 1 - Introduction to Python Solutions (File)
- Exercise 2- Introduction to Numpy (File)
- Exercise 2- Introduction to Numpy Solution (File)
- Exercise 3 - kNN (File)
- Exercise 3 - kNN Partial Solution (File)
- Exercise 4 - Logistic Regression (File)
- Exercise 4 - Logistic Regression Partial Solution (File)
- Exercise 5 - KMeans (File)
- Exercise 5 - KMeans Partial Solution (File)
- Exercise 6 -SVM and Kernel Methods (File)
- Exercise 6 - Solution (File)
- Exercise 7 - Linear Regression (File)
- Exercise 7 - Linear Regression Solution (File)
- Exercise 9 - MLPs (File)
- Exercise 9 - Solution (File)
- Exercise 10: CNN (File)
- Exercise 10: CNN solution (File)
- Exercise 11: Transformers (File)
- Exercise 11 - Transformers Solution (File)
- Exercise 12: PCA (File)
- Exercise 12: PCA Solutions (File)
Project
- Project Group Registration - Deadline: March 23rd 23:59 (URL)
- Project Description Document 2025 (URL)
- Project Framework MS1 2025 (File)
- Data MS1 2025 (File)
- Milestone 1 test file (File)
- Project Description Document MS2 (URL)
- Project Framework MS2 2025 (File)
Previous year exams
- 2019 Exam - without solutions (File)
- 2019 Exam - with solutions (File)
- 2020 Exam - without solutions (File)
- 2020 Exam - with solutions (File)
- 2021 Exam - without solutions (File)
- 2021 Exam - with solutions (File)