CSC 462 Machine Learning

This course gives an overview of machine learning concepts, techniques, and algorithms. Topics include Linear Regression, Logistic Regression, Support Vector Machine, Decision Tree Learning, k-Nearest Neighbors, Gradient Descent, Feature Engineering, Cross Validation, Underfitting and Overfitting, Regularization, Model Performance Assessment, Neural Networks, Backpropagation, Convolutional Neural Networks, Ensemble Learning, K-Means & DBSCAN Clustering, and practical examples.

After completing this course successfully, student should have the following capabilities:

1. Understand the fundamentals of machine learning algorithms, including supervised and some unsupervised learning techniques.

2. Learn how to prepare data for machine learning models utilizing techniques such as feature engineering.

3. Develop critical thinking and problem-solving skills to identify and address common challenges in machine learning, such as overfitting, underfitting, and biased data.

4. Know how to evaluate the performance of machine learning models using appropriate metrics and techniques.

5. Gain practical experience implementing machine learning algorithms using popular machine learning libraries and frameworks, such as scikit-learn and TensorFlow.