CS418

CS418 – Machine Learning

CS418 – Machine Learning is an advanced-level course in the Computer Science curriculum at Virtual University, designed to introduce students to the foundational principles, algorithms, and practical applications of machine learning (ML). This course aims to equip students with the ability to build intelligent systems that learn from data and improve performance over time without being explicitly programmed. It combines theory with hands-on implementation, offering insights into both mathematical models and real-world problem solving. The course begins by covering the basics of supervised and unsupervised learning, including important concepts such as hypothesis space, bias-variance tradeoff, overfitting, and underfitting. Students explore popular supervised learning algorithms such as Linear Regression, Logistic Regression, Decision Trees, Naive Bayes, Support Vector Machines (SVM), and K-Nearest Neighbors (KNN). The course also delves into unsupervised techniques like K-Means Clustering and Hierarchical Clustering, which are useful for grouping data without predefined labels. A significant portion of CS418 is focused on model evaluation, using techniques such as cross-validation, confusion matrices, and performance metrics like accuracy, precision, recall, and F1-score. Students also learn how to preprocess data, including normalization, feature scaling, dimensionality reduction (e.g., PCA), and handling missing values — all crucial steps before feeding data into any learning algorithm. In the latter part of the course, students are introduced to neural networks and deep learning, laying the groundwork for modern AI applications. Libraries such as scikit-learn, TensorFlow, or PyTorch are often used for practical implementation. Assignments and projects help students develop real-life ML systems such as classification engines, spam filters, recommendation systems, and even predictive analytics tools. Overall, CS418 prepares students for careers in data science, artificial intelligence, and analytics, as well as for further study in specialized ML and AI courses. It fosters critical thinking and problem-solving skills, emphasizing both algorithmic knowledge and the ability to apply ML techniques to diverse datasets across various domains.