Machine learning algorithms are computer techniques the allow computers to achieve results or improve on them based on data. Machine Learning is used in a wide variety of applications and this lesson will offer a brief overview on various topics of this area of artificial intelligence.
Prerequisites
For this lesson, it is assume a familiarity with Python language. All examples and exercises will use python.
| Setup | Download files required for the lesson | ||
| Day 1 | 09:00 | 1. Introduction | What do we mean by Machine Learning? |
| 09:30 | 2. Data Cleaning, Imputation, Cross-Validation | Key question (FIXME) | |
| 09:40 | 3. Linear and Logistic Regression | How to do regression from real data using Pandas and Scikit-learn | |
| 10:40 | 4. Training, Validation, Test Data and Overfitting | Key question (FIXME) | |
| 11:40 | 5. Linear Classifiers | Key question (FIXME) | |
| 12:40 | 6. Lunch Break | Break | |
| 13:40 | 7. Decision trees and random forests | Using Scikit-learn for decision trees, decisions nad random forest | |
| 14:40 | 8. Navie Bayes and Kernel Methods | Key question (FIXME) | |
| 15:10 | 9. Clustering: K-means and Hierarchical Clustering | Key question (FIXME) | |
| 16:10 | Finish | ||
| Day 2 | 09:00 | 10. Dimensionality reduction | Key question (FIXME) |
| 10:30 | 11. Principal and independent component analysis | Key question (FIXME) | |
| 12:00 | 12. Neural Networks and Back Propagation | Key question (FIXME) | |
| 13:00 | 13. Lunch Break | Break | |
| 14:00 | 14. Deep Learning and Convolutional Neural Networks | Key question (FIXME) | |
| 16:00 | Finish |
The actual schedule may vary slightly depending on the topics and exercises chosen by the instructor.