Artificial Intelligence & Machine Learning

Understanding Intelligent Systems. Build Skills for the Future

Artificial Intelligence is transforming industries, scientific research, business operations, education, and everyday life. Behind many of these developments are machine learning systems capable of identifying patterns, learning from data, generating predictions, and supporting complex decisions.

The Artificial Intelligence & Machine Learning course at CA Institute introduces students to the principles, methods, and practical applications of these technologies. Students explore how machine learning models are developed, trained, evaluated, and used to solve real-world problems.

Through guided activities, practical demonstrations, case studies, and a final applied project, students develop an understanding of the machine learning process, common AI tools, and the opportunities and limitations of intelligent systems.

The course combines technical foundations with critical thinking, responsible innovation, and practical applications. It is designed to make this rapidly developing field accessible to motivated learners while providing a foundation for further study.

Course Overview

Course Title: Artificial Intelligence & Machine Learning

Course Code: AI 201 (proposed)

Course Level: Undergraduate / University Foundation — Applied Intermediate

Recommended English Proficiency: CEFR B1–B2

Proposed Credit Value: 3 U.S. Credits / 6 ECTS

Proposed Contact Hours: 45 hours

• Lectures: 25 hours
• Seminars and practical sessions: 20 hours

Language of Instruction: English

Recommended Duration: 15 weeks

Prerequisites: No previous professional experience in artificial intelligence is required. Completion of an introductory AI course, such as AI 101, is recommended. Basic computer literacy and a willingness to work with data are expected. Previous programming experience is helpful but not required.

Academic level, course code, credits, contact hours, and prerequisites are proposed and should be confirmed by CA Institute before publication as official course specifications.

This course provides a structured introduction to the principles and practice of Artificial Intelligence and Machine Learning. Students examine the relationship between AI, machine learning, deep learning, data science, and modern intelligent applications.

The curriculum explores how machine learning systems use data to identify patterns and produce predictions or classifications. Students are introduced to supervised and unsupervised learning, data preparation, model training, performance evaluation, and the limitations of automated prediction.

Through demonstrations and guided practical exercises, students investigate how machine learning can be applied to business forecasting, image classification, language technologies, recommendation systems, and other professional contexts.

The course also examines generative AI, neural networks, data quality, algorithmic bias, privacy, transparency, and the responsible use of AI. Students learn to assess the suitability of AI-based solutions, interpret basic model results, and communicate the strengths and limitations of machine learning applications.

The emphasis is on developing practical understanding and analytical competence rather than advanced mathematical theory. Students completing the course will have a foundation for further study in artificial intelligence, data science, technology management, and related fields.

The course aims to:

Develop an understanding of the fundamental principles of Artificial Intelligence and Machine Learning.

• Explain the relationship between AI, machine learning, deep learning, and data science.

• Introduce the main stages of a machine learning workflow, from defining a problem to evaluating a model.

• Develop awareness of data preparation, feature selection, model training, testing, and validation.

• Introduce common machine learning approaches and their appropriate applications.

• Enable students to interpret basic model outputs and recognize limitations in predictive performance.

• Explore practical AI applications across business, education, healthcare, industry, and research.

• Develop critical thinking about bias, privacy, fairness, transparency, and responsible AI.

• Strengthen academic and professional English related to AI and machine learning.

• Prepare students for further learning in applied AI, data analytics, and emerging technologies.

Upon successful completion of the course, students will be able to demonstrate the following competencies.

Knowledge & Understanding

• Define Artificial Intelligence, Machine Learning, deep learning, and related concepts.

• Explain the differences between supervised, unsupervised, and reinforcement learning.

• Describe the principal stages of a typical machine learning workflow.

• Explain how data quality and data preparation influence model performance.

• Identify common applications and limitations of machine learning systems.

Technical & Analytical Skills

• Identify an appropriate machine learning approach for a clearly defined introductory problem.

• Organize and examine a basic dataset using guided analytical tools.

• Distinguish between training, validation, and test data.

• Interpret basic classification and regression results with guidance.

• Recognize common evaluation measures, including accuracy, precision, recall, and mean absolute error.

• Compare alternative approaches and explain their relative advantages and limitations.

• Develop a simple machine learning demonstration or guided project using an appropriate tool.

Applied Problem-Solving

• Formulate a basic problem that could potentially be addressed using machine learning.

• Identify relevant data requirements and potential sources of error.

• Evaluate whether a proposed AI application is suitable for a given context.

• Communicate findings and recommendations based on a practical exercise or case study.

Ethical & Critical Awareness

• Recognize potential bias in datasets and model outputs.

• Explain the importance of privacy, data protection, and responsible data use.

• Identify risks associated with overfitting, unreliable predictions, and inappropriate automation.

• Discuss the importance of human oversight, transparency, and accountability in AI applications.

Academic & Professional Communication

• Use key AI and machine learning terminology accurately in English.

• Explain basic technical concepts to a non-specialist audience.

• Prepare a structured report or presentation describing a machine learning application.

• Evaluate information about AI technologies using critical and evidence-informed reasoning.

Week 1: Introduction to Artificial Intelligence & Machine Learning

• What is Artificial Intelligence?

• Machine learning as a branch of AI

• Differences between AI, machine learning, and deep learning

• Current applications and industry trends

• The machine learning development process

Practical activity: Identify AI and machine learning applications in everyday life.

Week 2: Data, Algorithms & Computational Thinking

• What is an algorithm?

• Structured and unstructured data

• Variables, features, and observations

• The relationship between data and model performance

• Introduction to computational problem-solving

Practical activity: Examine a dataset and identify its variables and potential uses.

Week 3: Mathematics & Statistics for Machine Learning

• Descriptive statistics and data distributions

• Mean, median, variability, and correlation

• Basic probability concepts

• Relationships between variables

• Understanding patterns and uncertainty

Practical activity: Interpret summary statistics and visualizations.

Week 4: Data Collection, Preparation & Feature Engineering

• Data sources and data formats

• Missing values and inconsistent data

• Data cleaning and transformation

• Selecting useful features

• Data leakage and common preparation errors

Practical activity: Prepare a small dataset for analysis using guided tools.

Week 5: Supervised Learning — Regression

• Introduction to supervised learning

• Independent and dependent variables

• Linear regression concepts

• Predictions and residual errors

• Practical applications in forecasting

Practical activity: Explore a simple regression model and interpret its predictions.

Week 6: Supervised Learning — Classification

• Classification problems and class labels

• Logistic regression and decision trees

• Classification boundaries

• Confusion matrices

• Accuracy, precision, recall, and related measures

Practical activity: Examine the performance of a basic classification model.

Week 7: Unsupervised Learning & Pattern Discovery

• Introduction to unsupervised learning

• Clustering and grouping

• Similarity and distance concepts

• Dimensionality reduction: introductory overview

• Applications in customer segmentation and exploratory analysis

Practical activity: Explore how a dataset can be grouped into meaningful clusters.

Week 8: Midterm Review & Assessment

• Review of core concepts

• Data preparation and model selection

• Interpretation of model outputs

• Short applied assessment

• Feedback and review of common challenges

Assessment: Midterm knowledge check and practical interpretation exercise.

Week 9: Model Training, Validation & Evaluation

• Training, validation, and test datasets

• Generalization and unseen data

• Overfitting and underfitting

• Cross-validation: introductory concepts

• Selecting appropriate evaluation measures

Practical activity: Compare model performance and identify potential overfitting.

Week 10: Neural Networks & Deep Learning

• Introduction to artificial neural networks

• Neurons, layers, weights, and activation

• Training and optimization: conceptual overview

• Deep learning applications

• Strengths and limitations of neural networks

Practical activity: Explore a neural network demonstration using a guided tool.

Week 11: Natural Language Processing & Generative AI

• How machines process human language

• Text classification and sentiment analysis

• Large language models: introductory principles

• Generative AI and content creation

• Hallucinations, reliability, and verification

Practical activity: Evaluate an AI-generated output and identify its limitations.

Week 12: Computer Vision & Intelligent Applications

• How computers process images

• Image classification and object detection

• Introduction to computer vision systems

• Applications in manufacturing, healthcare, retail, and security

• Practical and ethical limitations of image-based AI

Practical activity: Explore a guided image-classification demonstration.

Week 13: Responsible AI, Bias & Model Limitations

• Data bias and algorithmic fairness

• Privacy and data protection

• Transparency and explainability

• Reliability, robustness, and security

• Human oversight and responsible implementation

Practical activity: Analyze a case study involving potential bias or inappropriate AI use.

Week 14: Applied Machine Learning Project

• Defining a practical problem

• Selecting and preparing data

• Choosing an appropriate approach

• Reviewing model results

• Communicating findings and limitations

Practical activity: Develop a guided project or case-study solution, individually or in a small group.

Week 15: Final Presentations & Course Assessment

• Presentation of applied projects

• Interpretation of results

• Discussion of model limitations and ethical considerations

• Final knowledge assessment

• Review of further study and professional development pathways

Assessment: Final project presentation and course assessment.

Teaching & Learning Methodology

The course combines theoretical foundations with guided practical learning.

Teaching methods may include:

• Lectures and structured discussions

• Demonstrations of AI and machine learning tools

• Guided data analysis exercises

• Practical case studies

• Individual and group activities

• Applied assignments and project work

• Student presentations and reflective evaluation

The course is designed to develop both conceptual understanding and practical competence. Programming and analytical activities will be introduced progressively, with appropriate guidance for students who have limited prior technical experience.

Assessment & Completion Requirements

Student progress may be assessed through a combination of:

• Class participation and practical exercises: 20%

• Assignments and applied case studies: 25%

• Midterm assessment: 20%

• Final applied project and presentation: 35%

Total: 100%

Students are expected to participate in learning activities, complete required assignments, demonstrate understanding of core concepts, and submit a final applied project.

Successful completion is subject to the attendance, assessment, and academic requirements established by CA Institute for the course.

The proposed assessment weights and completion requirements should be formally approved before publication.

Professional Applications & Further Study

Machine learning knowledge can support further study and professional development in a range of technology-related and analytical fields.

Potential areas of application include:

• Artificial Intelligence and Machine Learning

• Data Science and Business Analytics

• Business Intelligence

• Predictive Analytics

• AI Product Development

• Technology and Innovation Management

• Digital Transformation

• AI-Assisted Research and Problem-Solving

The course provides an educational foundation rather than a guarantee of employment or qualification for specialized technical positions. Career opportunities depend on students’ previous education, practical experience, and additional training.

This course may be suitable for:

• Students interested in AI and emerging technologies

• Professionals seeking to develop their understanding of machine learning

• Business and technology students

• Entrepreneurs exploring AI-enabled products and services

• Data-oriented professionals seeking introductory machine learning knowledge

• Educators and researchers interested in AI applications

• Learners planning further study in data science or related fields

No advanced mathematical background is required, although an interest in analytical problem-solving is recommended.

Tuition

Course start date: February 15, 2027