Artificial Intelligence Training
This comprehensive training program that we have prepared for participants at different levels offers a wide range from basic concepts to advanced technologies, from ethical issues to the future.
CHAPTER 1: INTRODUCTION TO ARTIFICIAL INTELLIGENCE AND BASIC CONCEPTS
1.1 What is Artificial Intelligence?
The definition of artificial intelligence and scope
Brief history of AI: from the Dartmouth Conference to the present
AI examples in daily life
1.2 AI vs Automation vs Algorithms
Conceptual differences
Differences between traditional programming and AI
Decision trees and rule-based systems
CHAPTER 2: MACHINE LEARNING (ML)
2.1 Introduction to Machine Learning
What is ML and why is it important?
Traditional programming vs ML approach
The use of ML in business applications
2.2 Types of ML
Supervised Learning
Classification
Regression
Examples: Spam filtering, price prediction
Unsupervised Learning Learning)
Clustering
Dimension reduction
Examples: Customer segmentation, anomaly detection
Reinforcement Learning
Agent, environment, reward concepts
Examples: Game artificial intelligence, robotics
Semi-Supervised and Transfer Learning
2.3 Popular ML Algorithms
Decision Trees
Random Forest
Support Vector Machines (SVM)
K-Nearest Neighbors (KNN)
Naive Bayes
Linear/Logistic Regression
2.4 Stages of ML Project
Data collection and preparation
Feature engineering
Model selection and training
Evaluation metrics (Accuracy, Precision, Recall, F1-Score)
Overfitting and Underfitting
CHAPTER 3: DEEP LEARNING (DL)
3.1 Introduction to Deep Learning
What is DL? Differences from ML
Why "deep" learning?
The rise of DL: GPUs and big data
3.2 Artificial Neural Networks (ANN)
From biological neurons to artificial neuron
Perceptron and multilayer networks
Activation functions (ReLU, Sigmoid, Tanh)
Backpropagation algorithm
Gradient Descent and optimization
3.3 Advanced DL Architectures
Convolutional Neural Networks (CNN)
For image processing CNN
Convolution, Pooling layers
Applications: Facial recognition, medical image analysis, autonomous vehicles
Recurrent Neural Networks (RNN) and LSTM
Sequential data processing
Long-short-term memory (LSTM)
Applications: Text generation, time series prediction
Generative Adversarial Networks (GAN)
Generative and discriminative networks
Deepfake technology
Use in art and design
Transformer Architecture
Attention mechanism
BERT, GPT models
Foundation of modern language models
3.4 Transfer Learning and Fine-Tuning
Use of pre-trained models
High performance with little data
CHAPTER 4: NATURAL LANGUAGE PROCESSING (NLP)
4.1 Introduction to NLP
Language and computers
The challenge of NLP: Uncertainty, context, language diversity
4.2 Basic NLP Techniques
Tokenization
Stemming and Lemmatization
Part-of-Speech (POS) Tagging
Named Entity Recognition (NER)
Word Embeddings (Word2Vec, GloVe)
4.3 Advanced NLP Applications
Sentiment Analysis
Machine translation
Question-answer systems
Text summarization
Chatbots and speech systems
4.4 Large Language Models (LLM)
Models such as GPT, Claude, Gemini
Basic principles of Prompt Engineering
Use of LLM in business world
RAG (Retrieval Augmented Generation)
CHAPTER 5: COMPUTER VISION
5.1 Image Processing Fundamentals
Digital images and pixels
Image preprocessing techniques
5.2 CV Applications
Object recognition and detection
Face recognition and biometric systems
Optical character recognition (OCR)
Medical image analysis
Vision systems in autonomous vehicles
Quality control and defects detection
5.3 Image Generation
Stable Diffusion, DALL-E, Midjourney
Text-to-Image applications
Creative use of AI in business
CHAPTER 6: BIG DATA AND AI
6.1 What is Big Data?
5V: Volume, Velocity, Variety, Veracity, Value
Traditional data vs Big data
Data lakes (Data Lakes) and data warehouses
6.2 Big Data Technologies
Hadoop ecosystem
Apache Spark
NoSQL databases
Distributed systems
6.3 Big Data and AI Relationship
Data is the fuel of AI
Data quality and preprocessing
Feature stores and ML infrastructure
Real-time data processing and ML
CHAPTER 7: INTERNET OF THINGS (IoT) AND AI
7.1 IoT Fundamentals
What is IoT?
Sensors, actuators, connectivity
IoT architecture and protocols
7.2 IoT and AI Integration
Edge AI: Artificial intelligence at the edge
Predictive maintenance
Smart cities and smart homes
Industrial IoT (IIoT)
IoT and AI in Agriculture
7.3 Real World Examples
Smart thermostats
Wearable health devices
Factory 4.0 and digital twins
CHAPTER 8: VIRTUAL REALITY (VR), AUGMENTED REALITY (AR) AND AI
8.1 VR and AR Fundamentals
What is VR? What is AR? What is MR (Mixed Reality)?
Technological infrastructure and devices
8.2 AI-Enriched VR/AR
Smart NPCs (Non-Player Characters)
Real-time translation and AR overlay
Virtual assistants
Procedural content generation
8.3 Application Areas
Education and simulation
Health and medical education
Architecture and design
Retail and e-commerce (Virtual trial)
Metaverse concept
CHAPTER 9: AI AND CYBER SECURITY
9.1 Use of AI in Cyber Security
Anomaly detection
Threat intelligence
Phishing and malware detection
SIEM systems and AI
Behavioral analysis
9.2 Security of AI Systems
Adversarial attacks
Model poisoning
Data poisoning
Privacy-preserving ML
Federated Learning
9.3 Ethical Hacking and AI
AI in penetration tests
Automatic vulnerability scanning
Social engineering with AI
CHAPTER 10: SECTORAL APPLICATIONS OF ARTIFICIAL INTELLIGENCE
10.1 Finance and Banking
Algorithmic trading
Credit scoring
Fraud detection detection)
Robo-advisors
Risk management
10.2 Health
Disease diagnosis
Drug discovery
Personalized medicine
Radiology and pathology analysis
Robotics surgery
10.3 Retail and E-Commerce
Recommendation systems
Pricing optimization
Customer segmentation
Chatbots and virtual assistants
Demand forecasting
10.4 Production
Quality control
Predictive maintenance
Supply chain optimization
Robotics and automation
Digital twin
10.5 Transportation
Autonomous vehicles
Traffic management
Route optimization
Logistics
10.6 Human Resources
CV scanning
Talent matching
Employee retention analysis
Performance evaluation
10.7 Marketing
Personalized campaigns
Customer lifetime value estimation
Social media analysis
Content production
CHAPTER 11: LEGAL DIMENSION OF ARTIFICIAL INTELLIGENCE
11.1 AI and Legislation
EU AI Act
GDPR and data protection
AI regulations in Türkiye
Sectoral regulations (finance, health, etc.)
11.2 Responsibility and Accountability
Responsibility for AI decisions
Legal personality debates
Product liability
Malpractice and medical errors
11.3 Intellectual Ownership
Copyrights of AI-generated content
Patent and AI
Data ownership
Open source models and licensing
11.4 Contracts and Compliance
AI usage agreements
Vendor management
SLAs (Service Level Agreements)
Audit and compliance requirements
CHAPTER 12: ETHICAL DIMENSION OF ARTIFICIAL INTELLIGENCE
12.1 Foundations of AI Ethics
Why are ethics important?
Basic ethical principles: Transparency, justice, privacy
Ethical frameworks and standards
12.2 Prejudice and Discrimination (Bias)
How does algorithmic bias occur?
Historical examples (Amazon's CV scanning system, COMPAS)
Bias in datasets
Fairness metrics
Reducing bias strategies
12.3 Transparency and Explainability
Black box problem
XAI (Explainable AI) methods
Tools such as LIME, SHAP
Right to explanation
12.4 Privacy and Data Protection
Differential Privacy
Data minimization
Anonymization vs De-anonymization
Protection of personal data
12.5 Unemployment and Economic Impact
Automation and job losses
New job areas
Skill transformation
Universal basic income discussions
12.6 Manipulation and Disinformation
Deepfakes
Propaganda and micro-targeting
Echo chambers
Social media algorithms
12.7 Autonomous Weapons and Military Usage
Killer robots (Lethal autonomous weapons)
War ethics
International regulations
12.8 Sustainability
Carbon footprint of AI models
Energy consumption
Green AI (Green AI)
CHAPTER 13: AI PROJECT MANAGEMENT AND IMPLEMENTATION
13.1 Starting the AI Project
Business problem definition
Is AI suitable? (When not to use AI)
Data discovery and feasibility
ROI calculation
13.2 Team Building
Required roles: Data scientist, ML engineer, data engineer
Importance of domain experts
MLOps teams
13.3 AI Project Methodologies
CRISP-DM
Agile for ML
Iterative development
13.4 MLOps: Take to Production
Model versioning
CI/CD for ML
Model monitoring
A/B testing
Model retraining strategies
13.5 Common Mistakes and Prevention
Data leakage
Wrong metric selection
Production-training gap
Technical debt
CHAPTER 14: AI TOOLS AND PLATFORMS
14.1 Programming and Frameworks
Python ecosystem
TensorFlow and Keras
PyTorch
Scikit-learn
Hugging Face
14.2 Cloud AI Services
AWS AI/ML services
Google Cloud AI
Microsoft Azure AI
IBM Watson
14.3 No-Code / Low-Code Platforms
AutoML tools
Google AutoML
DataRobot
14.4 Data Labeling and Annotation
Labelbox
Scale AI
Amazon SageMaker Ground Truth
14.5 Model Deployment
TensorFlow Serving
ONNX
Containerization (Docker, Kubernetes)
CHAPTER 15: THE FUTURE OF ARTIFICIAL INTELLIGENCE
15.1 Technological Trends
Multimodal AI
Foundation models and large models
Quantum Machine Learning
Neuromorphic
Brain-computer interfaces
15.2 On the Road to General Artificial Intelligence (AGI)
What is AGI and when can it happen?
Superintelligence scenarios
Alignment problem
Existential computing risks
15.3 Social Transformations
Evolution of the education system
Future of working life
Democracy and governance
Revolution in healthcare
15.4 Regulatory Future
Global AI governance
Standards and certification
International cooperation
15.5 Ethics and Community Preparedness
AI literacy
Social dialogue
Inclusive AI development
Diversity and representation
CHAPTER 16: PRACTICAL WORKSHOP AND EXAMPLES
16.1 Hands-on Exercises
Creating a simple ML model (Jupyter Notebook)
Prompt engineering with ChatGPT/Claude
Model with AutoML tool training
Image classification demo
16.2 Case Studies
Netflix recommendation system
Tesla autonomous driving
AlphaGo and gaming AI
GPT models and the language revolution
16.3 Interactive Q&A and Scenarios
Examples specific to the participants' industry
AI solutions to real problems
CHAPTER 17: RESOURCES AND ADVANCED LEARNING
17.1 Recommended Resources
Online courses (Coursera, edX, Udacity)
Books (Pattern Recognition, Deep Learning Book)
Research resources (arXiv, Papers with Code)
Podcasts and YouTube channels
17.2 Communities
Kaggle
GitHub
Reddit ML community
Local meetups
17.3 Certifications
Google TensorFlow Certificate
AWS ML Specialty
Microsoft Azure AI Engineer
BONUS: AI DICTIONARY
Turkish-English equivalents and explanations of all technical terms used throughout the training
Presentation Format Suggestions:
Total Duration: 1-day basic, intensive training or 2-day detailed, hands-on program
Audience-Based Adaptation:
For Managers: Chapters 1, 10, 11, 12, 15 emphasis
For technical teams: Emphasis on Divisions 2, 3, 4, 5, 13, 14
For mixed groups: Balanced selection from all divisions
Visual Material:
Lots of infographics in each chapter
Real world examples and videos
Interactive demonstrations
Live coding examples
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