This course covers the theory and practical algorithms for machine learning from a variety of perspectives and will introduce the fundamental concepts that enable computers to learn from experience. An emphasis will be placed the practical application to real problems. Topics include classification, clustering, dimension reduction, support vector machines, learning theory, online algorithms, and classical methods such as linear regression and reinforcement learning. This course will also offer a mathematical and practical perspective on artificial neural networks and will investigate the optimization and regularization techniques.

Learning Objectives and Outcomes:

By the end of this course students will be able to:

1. Analyze the advantages of using Machine Learning techniques in real-world problems.
2. Examine machine learning algorithms using a mathematical perspective, to solve variant problems.
3. Compare and contrasts developmental theories to optimize learning.
4. Develop machine learning algorithms with the ability to use them for a wide range of problems.

Course Schedule and Topics

This course will cover the following topics in eight learning sessions, with one Unit per week.
Week 1: Unit 1 – Introduction to Machine Learning
Week 2: Unit 2 – The PAC Learning Framework
Week 3: Unit 3 – Support Machine Vectors
Week 4: Unit 4 – Kernel Methods
Week 5: Unit 5 – Online Learning
Week 6: Unit 6 – Multi-Class Classification
Week 7: Unit 7 – Algorithmic Stability and Dimensionality Reduction
Week 8: Unit 8 – Reinforcement Learning

Learning Guide

This course will cover the following topics in eight learning sessions, with one Unit per week.

Unit 1: Introduction to Machine Learning

• Introduce yourself in the Course Forum
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Written Assignment
• Complete the Reflective Portfolio Assignment

Unit 2: The PAC Learning Framework

• Peer assess Unit 1 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Written Assignment
• Complete the Reflective Portfolio Assignment

Unit 3: Support Machine Vectors

• Peer assess Unit 2 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Written Assignment
• Complete the Individual Project Activity and begin research on your assigned topic (Due Unit 7)
• Complete the Reflective Portfolio Assignment

Unit 4: Kernel Methods

• Peer assess Unit 3 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Written Assignment
• Continue working on the Individual Project
• Complete the Reflective Portfolio Assignment

Unit 5: Online Learning

• Peer assess Unit 4 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Continue Working on the Individual Project
• Complete and submit the Written Assignment
• Complete the Reflective Portfolio Assignment

Unit 6: Multi-Class Classification

• Peer assess Unit 5 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Written Assignment
• Continue working on the Individual Project
• Complete the Reflective Portfolio Assignment

Unit 7: Algorithmic Stability and Dimensionality Reduction

• Peer assess Unit 6 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete and submit the Written Assignment
• Complete and submit the Individual Project
• Complete the Reflective Portfolio Assignment

Unit 8: Reinforcement Learning

• Peer assess Unit 7 Written Assignment
• Read the Learning Guide and Reading Assignments
• Participate in the Discussion Assignment (post, comment, and rate in the Discussion Forum)
• Complete the Reflective Portfolio Assignment
• Complete and submit the anonymous Course Evaluation