Machine Learning

Overview

What you'll learn

• To provide a broad survey of approaches and techniques in machine learning;
• To develop a deeper understanding of several major topics in machine learning;
• To develop the design and programming skills that will help you to build intelligent, adaptive artifacts;
• To develop the basic skills necessary to pursue research in machine learning.

Syllabus 16

1. Week 1: ML is the ROX/SL 1- Decision Trees
2. Week 2: SL 2- Regression and Classification
3. Week 3: SL 3- Neutral Networks
4. Week 4: SL 4- Instance Based Learning
5. Week 5: SL 5- Ensemble B&B
6. Week 6: SL 6- Kernel Methods & SVMs
7. Week 7: SL 7- Comp Learning Theory
8. Week 8: SL 8- VC Dimensions
9. Week 9: SL9- Bayesian Learning
10. Week 10: SL 10- Bayesian Inference
11. Week 11: UL 1- Randomized Optimization
12. Week 12: UL 2- Clustering/ UL 3- Feature Selection
13. Week 13: UL 4- Feature Transformation/UL 5- Info Theory
14. Week 14: RL 1- Markov Decision Processes
15. Week 15: Reinforcement Learning
16. Week 16: RL 3 Game Theory/Outro

Skills you'll practise

Unsupervised LearningAlgorithmsMachine LearningArtificial Intelligence

Advantages and disadvantages

Some points apply to every course of this kind; see how we rank.

Taught by

• Charles IsbellExecutive Associate Dean and Professor