Course

Unit 1

Unit ISupervised Learning AlgorithmsRegression and Classification Models; Learning Paradigms in Machine Learning; Decision Trees, Random Forest, Support Vector Machines, Naive Bayes, and k-Nearest Neighbors; Metrics: confusion matrix, ROC-AUC, precision-recall curves [15 hrs]

Unit 2

Unit IIUnsupervised Learning and Pattern Discovery Hierarchical clustering, DBSCAN; Dimensionality reduction: PCA, t-SNE; Topic modeling (LDA) revisited; Evaluation metrics [8 hrs]

Unit 3

Unit IIIData Mining: Techniques and Applications Useful for discovering co-occurrence or emerging needs in schemes, complaint analysis, and monitoring systems: Association Rule Mining (Apriori algorithm), Market- basket style analysis of citizen complaints; Orange tool [8 hrs]

Unit 4

Unit VIArtificial Intelligence and Sub-domains AI vs ML vs Data Science; Overview of Major Sub- domains: Machine Learning, Natural Language Processing (NLP), Computer Vision, Expert Systems, Social Network Analysis (SNA), Planning & Optimization, Knowledge Representation & Reasoning, Ethics and Responsible AI, Generative AI; Applications relevant to policy [6 hrs]

Unit 5

Unit VAdvances of LLMsIntroduction to neural networks and deep learning (conceptual); Transformer models, transfer learning; Prompt engineering; LLM fine-tuning: Use cases in policy [6 hrs]

Unit 6

Unit VIEthical AI and InterpretabilityBlack-box risk and transparency; Explainability techniques: SHAP, LIME (conceptual); Bias, fairness, and accountability in social algorithms; Global frameworks; [10 hrs]

Text Books / References

Textbooks and Papers:

• Cioffi-Revilla, C. (2014). Introduction to computational social science. Springer Verlag London Limited. https://link.springer.com/content/pdf/10.1007/978-3-319-50131-4.pdf
• James, G., Witten, D., Hastie, T., & Tibshirani, R. (2013). An introduction to statistical learning (Vol. 112, No. 1).
• New York: springer. https://link.springer.com/book/10.1007/978-3-031-38747-0
• Gron, Aurlien. Hands-on machine learning with Scikit-Learn, Keras, and TensorFlow: Concepts, tools, and techniques to build intelligent systems. ” O’Reilly Media, Inc.”, 2022.
• Rajaraman, Anand, and Jeffrey D. Ullman. Mining of massive datasets. Autoedicion, 2011.
• https://biblioteca.unisced.edu.mz/handle/123456789/2674
• Russell, S. J., & Norvig, P. (2016). Artificial intelligence: a modern approach. Pearson. https://people.engr.tamu.edu/guni/csce625/slides/AI.pdf

References:

• Ziems, Caleb, et al. “Can large language models transform computational social science?.” Computational Linguistics
• 50.1 (2024): 237-291.
• Chang, Ray M., Robert J. Kauffman, and YoungOk Kwon. “Understanding the paradigm shift to computational social science in the presence of big data.” Decision support systems 63 (2014): 67-80.
• Chandrasekharan, Eshwar, Umashanthi Pavalanathan, Anirudh Srinivasan, Adam Glynn, Jacob Eisenstein, and Eric Gilbert. “You can’t stay here: The efficacy of reddit’s 2015 ban examined through hate speech.” Proceedings of the ACM on human-computer interaction 1, no. CSCW (2017): 1-22.

Prerequisite: Programming for Social Data Science I & II, Research Methods for Policy Studies I & II. Summary: This course introduces students to the foundational concepts, models, and systems of modern artificial intelligence with a particular focus on machine learning and data mining techniques relevant to social science and public policy. Students will explore a range of learning paradigms (supervised, unsupervised), understand how machines identify patterns in data, and gain exposure to contemporary advances in AI including large language models and responsible AI frameworks. Emphasis is placed on the application of algorithms to policy-relevant problems such as citizen feedback analysis, social clustering, fraud detection, and knowledge discovery. The course integrates ethical reasoning and explainability throughout, ensuring that students not only understand how models function but also how to evaluate and interpret their impact in socially responsible ways.

Course Objectives:

1. Understand the fundamentals of machine learning methods.
2. Describe the statistical theory behind widely used supervised and unsupervised machine learning methods.
3. Explain the variety of machine learning methods available for social science research.
4. Identify appropriate machine learning methods to address a variety of research questions.
5. Learn how to design, train, and deploy machine-learning models to produce insights relevant for addressing societal challenges.

Course Outcomes:

CO1: Explain foundational concepts and subdomains of learning paradigms and Artificial Intelligence (AI) with relevance to social science and policy applications.

CO2: Apply supervised and unsupervised learning algorithms to analyze structured datasets and derive meaningful policy insights.

CO3: Implement data mining techniques to discover associations, patterns, or anomalies in real-world social or administrative data.

CO4: Interpret the structure and conceptual logic of advanced models like neural networks, transformer models, and large language models.

CO5: Evaluate model performance using standard metrics and justify the choice of modeling techniques in context-specific scenarios.

CO6: Assess the ethical, legal, and societal implications of AI systems, particularly regarding bias, transparency, and accountability.

Skills:

• Data-driven decision-making: through practical application of machine learning techniques, students will acquire the skill to leverage data effectively for evidence-based decision-making in social research and policy formulation, enhancing their capacity to address complex societal challenges.

• Ethical reasoning: students will develop ethical reasoning skills, enabling them to navigate and address ethical dilemmas inherent in the use of machine learning algorithms within social research, thus promoting responsible and ethical use of data-driven methodologies for societal benefit.

Program outcome PO – Course Outcomes CO Mapping

Program Specific Outcomes PSO – Course Objectives – Mapping

*CA – Can be Quizzes, Assignment, Projects, and Reports, and Seminar