Study guide: AI in Economics and Data Science

📋 Course Outline

1. AI for Economic Decision-Making
2. History and Evolution of AI
3. AI Definitions and Applications
4. Supervised Learning and Naive Bayes
5. Unsupervised Learning and K-Means
6. Hybrid Recommendation Systems
7. Data Collection Methods
8. Data Analysis and Visualization
9. Advanced Data Analysis Techniques

📖 1. AI for Economic Decision-Making

🔑 Key Concepts & Definitions

• Economic decision-making: economic decision-making is the process of choosing actions based on information to improve outcomes in markets, businesses, or public administration.
• Artificial intelligence: Artificial intelligence is the ability of computers to perform tasks resembling human cognition and behavior using software and hardware methods.
• Machine learning: Machine learning is an AI approach that uses large datasets to make predictions or decisions rather than relying on fixed instructions.
• Predictive economic modeling: Predictive economic modeling uses AI methods to forecast market trends, consumer behavior, and economic impacts.

📝 Essential Points

• AI education targets applying AI to solve economic problems, improve decisions, and optimize economic processes.
• Students practice analyzing large economic datasets with AI tools to support decision-making.
• AI is presented as enabling forecasts of market trends, consumer behavior, and economic impacts.
• In economics, AI supports strategic scenarios and demand prediction through predictive analytics and simulation.
• AI systems are linked to practical dashboards that support performance management using analyzed data.

💡 Memory Hook

AI for economics: Data → predictions (markets/consumers) → decisions (scenarios/dashboards).

📖 2. History and Evolution of AI

🔑 Key Concepts & Definitions

• Artificial intelligence as a discipline: Artificial intelligence is treated as an academic discipline whose origins are traced back to the 1950s.
• Turing Test: The Turing Test is a proposal by Alan Turing that assesses whether a machine shows intelligence through interaction with a human.
• Artificial intelligence term (1956): In 1956, John McCarthy coined the phrase artificial intelligence at a conference he organized.
• Logic Theorist: Logic Theorist is an early 1956 AI program focused on proving mathematical theorems.
• ELIZA: ELIZA is a 1967 program created by Joseph Weizenbaum to stimulate therapist–patient style interactions.

📝 Essential Points

• AI research between 1956 and 1966 was mostly theoretical, aiming to model human thought and imitate human behavior in machines.
• Sad Sam (1960) was an AI program by Robert K. Lindsay that generated conclusions in English from simple sentences.
• ELIZA was developed in 1967 by Joseph Weizenbaum to simulate interactions with patients in a therapeutic setting.

💡 Memory Hook

Turing (test), McCarthy (name), then programs: Logic Theorist (prove), Sad Sam (say), ELIZA (therapy chat).

📖 3. AI Definitions and Applications

🔑 Key Concepts & Definitions

• Contract classification: Contract classification is an AI justice application that identifies and organizes contract content for automated legal analysis tasks.
• Adaptive traffic lights: Adaptive traffic lights are a transport AI use where signal control adjusts to reduce road congestion.
• HR chatbot: An HR chatbot is an AI assistant that answers employee questions within recruitment and human-resources management workflows.

📝 Essential Points

• Machine learning differs from traditional programming because it relies on learning from data to produce predictions or decisions rather than executing explicit instructions.
• AI is used to predict the spread of epidemics and support medical chatbots that answer health questions.
• In transport, AI supports autonomous vehicles, adaptive traffic-light control to reduce congestion, and route/time planning based on actual demand.
• In public administrations, AI analyzes large public databases for effective decision-making and helps monitor transactions to detect fraud and anomalies.
• In business management, AI supports automatic CV filtering, candidate–role matching, KPI analysis for team productivity, and predictive analytics such as market-demand forecasting plus scenario simulation.

💡 Memory Hook

AI vs traditional programming: rules-first instructions vs data-first learning for predictions and decisions.

📖 4. Supervised Learning and Naive Bayes

🔑 Key Concepts & Definitions

• Supervised learning: A supervised learning approach trains models on labeled data so the algorithm can learn to map inputs to the correct class or output.
• Support Vector Machine: A Support Vector Machine is a supervised classifier that separates classes by finding a hyperplane using support vectors near the decision boundary.
• Support vectors: Support vectors are the data points closest to the decision limit that determine the position of the SVM separating hyperplane.
• Naive Bayes: Naive Bayes is a supervised probabilistic classifier built on Bayes’ theorem and a simplifying assumption that features are independent given the class.

📝 Essential Points

• A Support Vector Machine searches for a hyperplane that separates classes while maximizing separation using support vectors closest to the decision limit.
• Naive Bayes assumes a class y makes the observed characteristics in X independent of one another.
• Naive Bayes computes the posterior class probability using $P(y\mid X)=\dfrac{P(X\mid y)\,P(y)}{P(X)}$.

💡 Memory Hook

SVM: “support vectors set the border”; Naive Bayes: “Bayes + feature independence” leads to $P(y\mid X)$.

📖 5. Unsupervised Learning and K-Means

🔑 Key Concepts & Definitions

• Unsupervised learning: Unsupervised learning is a machine learning approach that uses algorithms to find patterns in data without using labelled answers.
• K-means: K-means is a machine learning algorithm used within unsupervised learning to group data into clusters.

📖 6. Hybrid Recommendation Systems

🔑 Key Concepts & Definitions

• Purchasing behaviour: Purchasing behaviour refers to how customers buy over time, producing signals that can be used to infer preferences or group them.
• Customer category: A customer category is a grouping assigned to a customer based on their purchasing behaviour patterns.

📖 7. Data Collection Methods

🔑 Key Concepts & Definitions

• Data collection: Data collection is the process of gathering relevant information so you can identify economic behaviour patterns and train an AI predictive model.
• Web scraping extraction: Web scraping extraction is an automated way to collect data from websites for later use in training predictive models.
• Consumer surveys and forms: Consumer surveys and forms are questionnaires used to collect responses that reveal patterns in consumer behaviour.
• Real-time sensor data: Real-time sensor data are measurements collected from sensors to capture conditions as they change over time.
• Interaction logs: Interaction logs are records of user actions from website or mobile app activity that support analysis for predictive modelling.

📝 Essential Points

• Data collection methods include manual or automated extraction via web scraping, surveys/forms on consumer behaviour, sensors for real-time data, and website/mobile app interaction logs.
• The goal of data collection is to obtain data patterns of economic behaviour that can be used to train a predictive model.
• Employee-role prediction can use data collection items such as questionnaire, HR interview, CV, LinkedIn profile, tests, and direct observation.
• Product-selection prediction can use data collection items such as online questionnaires, ratings, and analysis of comments using NLP plus product standards.

💡 Memory Hook

Web→Talk→Sensors→Click logs (web scraping, surveys, sensors, interaction logs) to get training data for predictions.

📖 8. Data Analysis and Visualization

🔑 Key Concepts & Definitions

• Model accuracy: Model accuracy measures the fraction of predictions a model gets correct out of all predictions it makes.
• True positives (TP): True positives are positive cases that the model predicts as positive.
• False negatives (FN): False negatives are positive cases that the model predicts as negative.

📝 Essential Points

• Accuracy can be computed as $\frac{\text{NB of correct predictions}}{\text{total number of predictions}}$.
• Accuracy can also be computed as $\frac{TP+TN}{TP+TN+FP+FN}$.
• True negatives (TN) are negative cases predicted as negative, while false positives (FP) are negative cases predicted as positive errors.
• In this setup, false negatives (FN) are positive cases predicted as negative errors.

💡 Memory Hook

Accuracy = (TP + TN) ÷ all outcomes; keep TP/TN as the “right calls.”

📖 9. Advanced Data Analysis Techniques

🔑 Key Concepts & Definitions

• Predictive analytics: Predictive analytics is an approach that uses data patterns to anticipate future events, such as machine failures, before they occur.
• Sensor data monitoring: Sensor data monitoring is the analysis of readings from equipment to detect signals indicating abnormal behaviour and likely breakdowns.
• Real-time trend detection: Real-time trend detection is the analysis of incoming data streams to spot trends or anomalies as they happen.
• Algorithmic auditing: Algorithmic auditing is a structured examination of AI systems to check compliance with requirements and best practices.

📝 Essential Points

• Predictive analytics based on sensor data can anticipate failures and trigger preventive maintenance planning to improve machine reliability.
• Real-time analysis of large economic or financial datasets helps identify trends or anomalies.
• Public policy can require algorithmic auditing mechanisms for AI systems.
• Public policy can introduce labels or certifications for AI systems that comply with best practice.
• Public policy can monitor societal, economic, and environmental impacts of AI to limit risks.

💡 Memory Hook

Failures: sensors → predictive analytics → preventive maintenance.

📅 Key Dates

1. 1950sAI considered as an academic discipline
2. 1956John McCarthy coined the term “artificial intelligence”
3. 1960“Sad Sam” created by Robert K. Lindsay
4. 1967ELIZA developed by Joseph Weizenbaum
5. 1988V. Daniel Hunt defined AI
6. 2013Cited work on applicability of AI in different fields of life
7. 2018Cited work on AI in medical education (Academic Medicine)
8. 1975Charles Darwin’s theory–inspired genetic algorithm developed by Holland
9. 2019“Al Khawarizmi” call for projects launched
10. 2022Cited work on hybrid approach recommending adaptive remediation activity

📊 Synthesis Tables

Data collection methods: comparisons

Deep learning vs machine learning

⚠️ Common Pitfalls & Confusions

1. Confusing supervised learning (labelled data with a learned mapping to outputs) with unsupervised learning (unlabelled data to discover structure/clusters).
2. Assuming Naive Bayes removes the need for probabilities; it still computes P(y|X) using Bayes’ theorem and the independence assumption.
3. Mixing up SVM’s goal (separating classes with a hyperplane using support vectors near the decision limit) with k-means clustering (partitioning data into K clusters by distance).
4. Treating data collection as the model itself; the course frames predictive modelling as a pipeline: data collection → analysis/visualisation → prediction → testing/evaluation.
5. Using only accuracy = (NB correct predictions)/total without knowing the alternative TP+TN formula and what TP/TN/FN/FP represent in this setup.
6. Believing collaborative filtering recommendations use item content alone; it predicts user preferences by similarity between users and their ratings.
7. Thinking “algorithmic auditing” is just general model testing; the course presents it as a structured examination to check compliance with requirements and best practices.

✅ Exam Checklist

1. Define economic decision-making and explain how AI supports it in economic contexts (forecasts, scenarios, dashboards).
2. List core AI fundamentals introduced in the course: AI as an academic discipline (1950s), Turing Test, and the 1956 McCarthy naming context.
3. Describe the early AI programs and their focus: Logic Theorist (1956), Sad Sam (1960), and ELIZA (1967).
4. Differentiate machine learning from traditional programming by stating data-first learning vs fixed instructions.
5. Explain supervised learning and what “labelled data” enables, including classification vs regression.
6. For SVM: state the hyperplane objective and the role of support vectors closest to the decision limit.
7. For Naive Bayes: state the Bayes theorem formula P(y|X)=P(X|y)P(y)/P(X) and the independence assumption.
8. Explain unsupervised learning goals and the clustering idea, then describe k-means (non-hierarchical partitioning) including the meaning of K and the centroid-distance assignments.
9. For reinforcement learning: state the reward/penalty mechanism and that it learns an agent’s best decisions autonomously in an environment.
10. Explain deep learning vs machine learning using the course distinctions (depth of neural networks; dependence on structured/labelled vs raw/unstructured data).
11. Reproduce the k-means procedure steps (initialise K centroids → assign to nearest centre → recompute centroids → check validity/iterate).
12. For predictive models: describe the full pipeline (data collection methods; analysis/visualisation methods such as PCA/MCA/time series/discriminant; prediction; testing/evaluation with accuracy formulas and TP/TN/FP/FN).