This three-day intensive course is designed to equip software engineers, project managers, and technical leads with the tools and insights needed to leverage artificial intelligence (AI) effectively. By focusing on AI concepts, practical implementations, and ethical considerations, participants will enhance their ability to integrate AI into modern software projects. AI for Software Engineers: Concepts and Techniques Benefits Course Benefits Foundation: Understand core AI concepts and their integration with software engineering. Practical Tools: Use AI tools for testing, debugging, and project management. Ethics: Explore responsible and ethical AI practices. Hands-On: Apply concepts through practical labs addressing real-world challenges Prerequisites: Proficiency in Python Familiarity with SDLC fundamentals (version control, CI/CD, agile methodology) AI for Software Engineers: Concepts & Techniques Training Outline Learning Objectives Day 1: AI Foundations and Basic ML Concepts Module 1: Introduction to AI in Software Development AI vs. Conventional Systems Narrow, General, and Super AI AI hardware (GPUs, TPUs) and popular frameworks AI in the SDLC Benefits (automation, predictive insights) and risks (maintenance, data quality) AI as a Service (AIaaS) and service contracts Module 2: Quality Characteristics and Ethics in AI Key Quality Factors Flexibility, Adaptability, Autonomy Transparency & Explainability Ethical & Regulatory Bias, Reward Hacking, and compliance (e.g., GDPR) Risk Management Identifying and mitigating biases Documentation of AI components Module 3: Machine Learning Overview ML Types Supervised, Unsupervised, Reinforcement Learning ML Workflow Data collection, preprocessing, model training, evaluation, deployment Overfitting & Underfitting Causes, detection, and mitigation (e.g., regularization) Lab 1: Overfitting & Underfitting (Titanic Dataset) Scenario: Predict passenger survival on the Titanic. Goal: Demonstrate how model complexity influences performance. Lab Steps: Load and preprocess Titanic data. Train multiple classifiers (e.g., logistic regression vs. random forest). Observe overfitting/underfitting effects on validation accuracy. Day 2: Data Handling, Regression, and Prompt Engineering Module 4: Data Preparation & Handling Data Quality Cleaning, handling missing values, outliers, categorical features Train/validation/test splits Common Pitfalls Imbalanced classes, mislabeled data, domain knowledge gaps Lab 2: Data Preparation (NYC Taxi Dataset) Scenario: Forecast taxi fares in NYC (regression). Goal: Clean a real-world dataset and create meaningful features. Lab Steps: Load NYC Yellow Cab data (pickup/dropoff times, distances). Handle missing data and detect outliers. Engineer features (e.g., time-of-day, trip distance). Module 5: Model Evaluation Metrics Regression Metrics MSE, RMSE, MAE, R² Classification Recap Accuracy, precision, recall, F1-score, confusion matrix Choosing the Right Metric Contextual needs (business value, safety-critical) Lab 3: Regression Modeling (NYC Taxi Fares) Scenario: Build and evaluate models to predict fare amounts. Goal: Compare linear regression vs. gradient boosting to measure error rates. Lab Steps: Train at least two regression models on NYC Taxi data. Compute MSE, RMSE, and MAE on the validation set. Discuss feature importance and next steps. Module 6: Prompt Engineering for Generative AI Prompting Best Practices Role-based prompting, zero-shot vs. few-shot, chain-of-thought reasoning Structuring prompts for clarity, constraints, and context Iterative refinement (synonyms, repeated keywords, output format) Lab 4: Designing a Sophisticated Prompt for Software Engineering Scenario: Generate detailed, actionable advice on software architecture, testing, or refactoring in a microservices environment. Goal: Apply advanced prompting techniques (role prompting, constraints, few-shot examples) to create a high-quality prompt that yields expert-level recommendations. Lab Steps: Define Context & Role (e.g., “You are a principal software architect…”). Provide Examples (show how you want the answer structured or styled). Add Constraints (limit response length, include specific bullet points). Iterate & Refine (test and adjust wording for clarity & precision). Day 3: Neural Networks, Explainability, and Responsible AI Module 7: Neural Networks Introduction NN Basics Perceptrons, hidden layers, activation functions NN Use Cases Images, text, speech; large-scale data Testing Neural Networks Special considerations, coverage measures Lab 5: Neural Network Classification (MNIST) Scenario: Classify handwritten digits from MNIST. Goal: Implement and train a feed-forward neural network. Lab Steps: Load MNIST images (28x28). Build a simple network (e.g., feed-forward). Evaluate accuracy and discuss improvements (layers, dropout, etc.). Module 8: Testing & Model Explainability Levels of Testing Input data testing, model testing, system & acceptance testing Adversarial Attacks & Data Poisoning Defenses, monitoring strategies Explainability Methods LIME, SHAP, local vs. global interpretation Lab 6: Model Explainability (U.S. Housing with LIME) Scenario: Stakeholders want insights into house pricing predictions. Goal: Use LIME to explain predictions of a regression model. Lab Steps: Train a regression model on a U.S. housing dataset (e.g., Ames Housing). Apply LIME to interpret specific predictions. Identify potential biases or anomalies in model behavior. Module 9: Responsible AI & Wrap-Up Governance & Compliance Privacy, fairness, disclaimers, accountability Future Trends Large Language Models (LLMs), multi-modal AI, MLOps Key Takeaways Data and model versioning, transparency, bias mitigation, robust QA Summary of Labs Lab 1: Overfitting & Underfitting (Titanic) Lab 2: Data Preparation (NYC Taxi) Lab 3: Regression Modeling (NYC Taxi Fares) Lab 4: Designing a Sophisticated Prompt for Software Engineering (GenAI) Lab 5: Neural Network Classification (MNIST) Lab 6: Model Explainability (U.S. Housing with LIME)
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