Machine Learning I

Objectives

1. To introduce supervised learning algorithms.
2. To implement supervised learning algorithms using Python libraries.

Course Contents

1. Introduction to Machine Learning
2. Nearest Neighbour Algorithms
3. Linear Regression and Logistic Regression
4. Perceptrons
5. Support Vector Machines (SVMs)
6. Multilayer Neural Networks
7. Decision Trees
8. Python Machine Learning Libraries

Learning Outcomes

At the end of the course unit, the student will be able to,

1. Define the term Machine Learning.
2. Describe how machine learning is different from conventional computer programming.
3. Describe the three main styles of learning: supervised, unsupervised and reinforcement learning and their differences.
4. Explain supervised learning algorithms learnt in the course unit.
5. Derive supervised learning algorithms.
6. Collect data and prepare them for machine learning algorithms.
7. Formulate supervised learning algorithms to different applications.
8. Implement supervised learning algorithms using Python libraries.
9. Interpret the results obtained from supervised learning algorithms.
10. Explain the problem of overfitting, along with techniques for detecting and managing the problem.
11. Integrate trained supervised learning models into an online software system.