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Introduction_to_Machine_Learning_Presentation.pdf
- 1. Image Introduction to MachineLearning Concepts, Methods, and Applications Data Science Department University of Advanced Technology February 9, 2026 ML Team (University) Introduction to Machine Learning February 9, 2026 1 / 14
- 2. Image Outline 1 Introduction toMachine Learning 2 Types of Machine Learning 3 Key Algorithms 4 ML Workflow 5 Applications 6 Challenges and Future 7 Conclusion ML Team (University) Introduction to Machine Learning February 9, 2026 2 / 14
- 3. Image What is MachineLearning? Definition Machine Learning is a subset of artificial intelligence that enables computers to learn patterns from data without being explicitly programmed. Key Idea Instead of programming specific rules, we create algorithms that can learn from examples. ML Team (University) Introduction to Machine Learning February 9, 2026 3 / 14
- 4. Image What is MachineLearning? Definition Machine Learning is a subset of artificial intelligence that enables computers to learn patterns from data without being explicitly programmed. Key Idea Instead of programming specific rules, we create algorithms that can learn from examples. Learns from experience (data) ML Team (University) Introduction to Machine Learning February 9, 2026 3 / 14
- 5. Image What is MachineLearning? Definition Machine Learning is a subset of artificial intelligence that enables computers to learn patterns from data without being explicitly programmed. Key Idea Instead of programming specific rules, we create algorithms that can learn from examples. Learns from experience (data) Improves performance over time ML Team (University) Introduction to Machine Learning February 9, 2026 3 / 14
- 6. Image What is MachineLearning? Definition Machine Learning is a subset of artificial intelligence that enables computers to learn patterns from data without being explicitly programmed. Key Idea Instead of programming specific rules, we create algorithms that can learn from examples. Learns from experience (data) Improves performance over time Makes predictions or decisions ML Team (University) Introduction to Machine Learning February 9, 2026 3 / 14
- 7. Image History and Evolution Time Complexity 1950s Perceptron 1980s NeuralNets 1990s SVM 2010s Deep Learning 1950s: Early neural networks 1980s: Backpropagation algorithm 1990s: Support Vector Machines 2000s: Ensemble methods 2010s+: Deep Learning revolution ML Team (University) Introduction to Machine Learning February 9, 2026 4 / 14
- 8. Image Machine Learning Categories SupervisedLearning A Labeled data Classification Regression Unsupervised Learning B Unlabeled data Clustering Dimensionality reduction Reinforcement Learning C Agent-environment Rewards Sequential decisions ML Team (University) Introduction to Machine Learning February 9, 2026 5 / 14
- 9. Image Supervised Learning Examples AlgorithmType Use Case Complexity Linear Regression Regression House prices Low Logistic Regression Classification Spam detection Low Decision Trees Both Medical diagnosis Medium SVM Classification Image recognition High Neural Networks Both Complex patterns Very High Table: Common supervised learning algorithms Mathematical Formulation For regression: y = f (X) + ϵ where f is learned from {(xi , yi )}n i=1 ML Team (University) Introduction to Machine Learning February 9, 2026 6 / 14
- 10. Image Popular ML Algorithms LinearModels Linear Regression: y = β0 + β1x1 + · · · + βpxp + ϵ Logistic Regression: P(y = 1|x) = 1 1 + e−(β0+βT x) Decision Trees Recursive partitioning Gini impurity or entropy Interpretable models Support Vector Machines Find hyperplane that maximizes margin: min w,b 1 2 ∥w∥2 s.t. yi (wT xi + b) ≥ 1 Neural Networks Multiple layers Backpropagation Universal approximators ML Team (University) Introduction to Machine Learning February 9, 2026 7 / 14
- 11. Image Typical ML Pipeline DataCollection Preprocessing Train/Test Split Model Training Evaluation Deployment ML Team (University) Introduction to Machine Learning February 9, 2026 8 / 14
- 12. Image Model Evaluation Metrics Classification Accuracy:TP+TN TP+TN+FP+FN Precision: TP TP+FP Recall: TP TP+FN F1-score: 2 × Precision×Recall Precision+Recall ROC-AUC Regression MSE: 1 n P (yi − ŷi )2 MAE: 1 n P |yi − ŷi | R2 : 1 − SSres SStot RMSE: √ MSE Important Note Always use cross-validation to avoid overfitting! ML Team (University) Introduction to Machine Learning February 9, 2026 9 / 14
- 13. Image Real-World Applications Healthcare Disease diagnosis Drugdiscovery Medical imaging Personalized treatment Finance Fraud detection Algorithmic trading Credit scoring Risk assessment Technology Natural language processing Computer vision Recommendation systems Autonomous vehicles Business Customer segmentation Sales forecasting Churn prediction Supply chain optimization ML Team (University) Introduction to Machine Learning February 9, 2026 10 / 14
- 14. Image Challenges in ML TechnicalChallenges Data quality issues Overfitting/Underfitting Computational complexity Model interpretability Scalability problems Ethical Considerations Bias and fairness Privacy concerns Transparency needs Accountability Job displacement Future Directions Automated Machine Learning (AutoML) Federated Learning Explainable AI (XAI) Quantum Machine Learning Edge AI deployment ML Team (University) Introduction to Machine Learning February 9, 2026 11 / 14
- 15. Image Summary Machine Learning enablescomputers to learn from data Image Figure: ML Impact Growth ML Team (University) Introduction to Machine Learning February 9, 2026 12 / 14
- 16. Image Summary Machine Learning enablescomputers to learn from data Three main types: Supervised, Unsupervised, Reinforcement Image Figure: ML Impact Growth ML Team (University) Introduction to Machine Learning February 9, 2026 12 / 14
- 17. Image Summary Machine Learning enablescomputers to learn from data Three main types: Supervised, Unsupervised, Reinforcement Proper workflow is crucial for success Image Figure: ML Impact Growth ML Team (University) Introduction to Machine Learning February 9, 2026 12 / 14
- 18. Image Summary Machine Learning enablescomputers to learn from data Three main types: Supervised, Unsupervised, Reinforcement Proper workflow is crucial for success Numerous applications across industries Image Figure: ML Impact Growth ML Team (University) Introduction to Machine Learning February 9, 2026 12 / 14
- 19. Image Summary Machine Learning enablescomputers to learn from data Three main types: Supervised, Unsupervised, Reinforcement Proper workflow is crucial for success Numerous applications across industries Important challenges need addressing Image Figure: ML Impact Growth ML Team (University) Introduction to Machine Learning February 9, 2026 12 / 14
- 20. Image Summary Machine Learning enablescomputers to learn from data Three main types: Supervised, Unsupervised, Reinforcement Proper workflow is crucial for success Numerous applications across industries Important challenges need addressing Rapidly evolving field with exciting future Image Figure: ML Impact Growth ML Team (University) Introduction to Machine Learning February 9, 2026 12 / 14
- 21. Image Thank You! Questions? Email: datascience@university.edu Website:www.university.edu/ml GitHub: github.com/ml-course ML Team (University) Introduction to Machine Learning February 9, 2026 13 / 14
- 22. Image References Mitchell, T. M.(1997). Machine Learning. McGraw-Hill. Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep Learning. MIT Press. Hastie, T., Tibshirani, R., & Friedman, J. (2009). The Elements of Statistical Learning. Springer. Chollet, F. (2021). Deep Learning with Python. Manning Publications. Further Reading Coursera: Machine Learning by Andrew Ng Kaggle: Competitions and datasets ArXiv: Latest research papers ML Team (University) Introduction to Machine Learning February 9, 2026 14 / 14