Computer Science (Fall 2020)
Professor: Santi Seguí & Josep Fortiana
Email: santi.segui@ub.edu
Tutorial times: On demand.
| Theory | Python Session | R Session | |
|---|---|---|---|
| Week 1 | Introduction | Introduction to R | |
| Week 2 | A typical Machine Learning project | Your "first" DS problem | Regression |
| Week 3 | Regression | A typical Machine Learning project | Regression |
| Week 4 | Classification | Regression | Regression |
| Week 5 | Trainining Models | Classification | Classification |
| Week 6 | Support Vector Machines | Trainining Models | Classification |
| Week 7 | Tree Based Methods | Support Vector Machines | Classification |
| Week 8 | Boosting & Bagging - Ensembles | Tree Based Methods | Unsupervised Learning |
| Week 9 | Neural Networks | Boosting & Bagging - Ensembles | Neural Networks |
| Week 10 | Convolutional Neural Networks | Neural Networks | Neural Networks |
| Week 11 | Unsupervised Learning | Dimensionality Reduction | Unsupervised Learning |
| W1eek 12 | Dimensionality Reduction | Unsupervised Learning |
- Project 1: https://www.kaggle.com/c/mlub-housing-house-prediction/
- Project 2: https://www.kaggle.com/c/mlub-housing-house-prediction-v2/
- Project 3: https://www.kaggle.com/c/mlub-housing-house-prediction-LinearRegression/
- Project 4
- Project 5: https://www.kaggle.com/c/mlub-learning-to-count/
- Project 6: https://www.kaggle.com/c/mlub-unsupervised-learning
- Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems. Aurelien Geron
- An Introduction to Statistical Learning: with Applications in R http://faculty.marshall.usc.edu/gareth-james/