Introduction to Machine Learning and Artificial Intelligence - online

Description:

This two-day online introductory course offers an overview of Artificial Intelligence (AI) and Machine Learning (ML), covering key concepts, real-world applications, basic learning algorithms, and ethical considerations. Designed for beginners, it includes hands-on activities and case studies to illustrate how AI and ML are shaping industries like healthcare, finance, and communication. The course also features a real-time application demo using R or Python programming language.

The course covers: 

Introduction to AI and ML

1. What is Artificial Intelligence (AI)
   • Definition and history
   • AI vs. Machine Learning vs. Deep Learning
2. Real-World Applications
   • Healthcare, finance, autonomous vehicles, chatbots
3. Types of AI
4. Ethical Considerations in AI

Basics of Machine Learning

1. What is Machine Learning?
2. Types of ML
   • Supervised Learning (Classification and Regression)
   • Unsupervised Learning (Clustering)
   • Reinforcement Learning (Brief Overview)

How Machines Learn

1. The Learning Process
   • Data collection and processing
   • Training vs. Testing data
2. Basic Algorithms Overview
   • Linear Regression (Simple Example)
   • Decision Trees
3. Evaluation Metrics: Accuracy, Precision, Recall

Hands on Exercises using R/Python

1. Exploring and Visualizing Data
2. Hands on exercise (supervised learning)
   • Decision Trees
   • Random Forests
   • Support Vector Machine (SVM)
3. Hands on exercise (unsupervised learning)
   • K-Means Clustering
   • Hierarchical Clustering

AI in the Real World and Future Trends

1. Case Studies: (healthcare, finance, NLP)
2. Limitations of AI
   • Bias in AI models
   • Data privacy concerns
3. Future of AI

By the end of the course participants will:

•  Understand the core concepts and types of AI and ML.
• Recognize key real-world applications of AI across industries.
• Differentiate between supervised, unsupervised, and reinforcement learning.
• Apply basic ML algorithms using Orange open-source software.
• Identify ethical issues and limitations associated with AI systems.

Pre-requisites:

Basic programming skills in R.

IMPORTANT: Please note that this course includes computer workshops. Before registering please check that you will be able to access the software noted below. Please bear in mind minimum system requirements to run software and administration restrictions imposed by your institution or employer with may block the installation of software.

• Software: Participants will use R and RStudio for hands-on exercises (Participants should install R and RStudio before the workshop to ensure smooth participation).

R Version: Latest stable release (R 4.3.x or newer).

RStudio: Recommended version (2023.09+ or newer).

• Key R Packages: Pre-install essential packages (list will be provided before the course).
• Internet Access: Required for package installations and data downloads.

Cost:

The fee per teaching day is: £60 per day for registered students / £150 per day for staff at academic institutions, Research Councils researchers, public sector staff, staff at registered charity organisations and recognised research institutions / £350 per day for all other participants.

In the event of cancellation by the delegate a full refund of the course fee is available up to two weeks prior to the course. No refunds are available after this date.

If it is no longer possible to run a course due to circumstances beyond its control, NCRM reserves the right to cancel the course at its sole discretion at any time prior to the event. In this event every effort will be made to reschedule the course. If this is not possible or the new date is inconvenient a full refund of the course fee will be given. NCRM shall not be liable for any costs, losses or expenses that may be incurred as a result of the cancellation of a course.

The University of Southampton’s Online Store T&Cs also continue to apply.

Website and registration:

Register for this course

Region:

South East

Keywords:

Explanatory Research and Causal analysis, Digital Social Research, Regression Methods, Data Mining, Quantitative Software, Research Ethics, Research Management and Impact (other), Data Visualisation

Related publications and presentations from our eprints archive:

Explanatory Research and Causal analysis
Digital Social Research
Regression Methods
Data Mining
Quantitative Software
Research Ethics
Research Management and Impact (other)
Data Visualisation

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