Overview
This course covers the fundamental concepts of machine learning, providing a solid foundation in supervised, unsupervised, and reinforcement learning. Students will gain hands-on experience with real-world datasets and problem-solving techniques.
What You’ll Learn
– Key machine learning algorithms and their applications.
– Techniques for data preprocessing and model evaluation.
– Practical approaches to optimizing machine learning models.
Skills You’ll Gain
– Data analysis and pattern recognition.
– Proficiency in building and tuning machine learning models.
– Understanding of evaluation metrics and performance reporting.
| Prerequisite: | None |
| Course Contents: | |
| Introduction to machine learning and statistical pattern recognition. Supervised learning: Part I (Graphical models (full Bayes, Naïve Bayes), Decision trees for classification & regression for both categorical & numerical data, Ensemble methods, Random forests, Boosting (Adaboost and Xgboost), Stacking; Part II (Four Components of Machine Learning Algorithm (Hypothesis, Loss Functions, Derivatives and Optimization Algorithms), Gradient Descent, Stochastic Gradient Descent, Linear Regression, Nonlinear Regression, Perceptron, Support vector machines, Kernel Methods, Logistic Regression, Softmax, Neural networks); Unsupervised learning: K-means, Density Based Clustering Methods (DBSCAN, etc.), Gaussian mixture models, EM algorithm, etc.; Reinforcement learning; Tuning model complexity; Bias-Variance Tradeoff; Grid Search, Random Search; Evaluation Metrics; Reporting predictive performance | |
| Teaching Methodology: | |
| Lectures, Problem-based learning, open problem discussion. | |
| Reference Material: | |
| 1. Elements of Statistical Learning
2. Pattern Recognition & Machine Learning, 1st Edition, Chris Bishop 3. Machine Learning: A Probabilistic Perspective, 1st Edition, Kevin R Murphy 4. Applied Machine Learning, online Edition, David Forsyth, http://luthuli.cs.uiuc.edu/~daf/courses/LearningCourse17/learning-book-6-April-nn-revision.pdf |
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