MACHINE LEARNING

This course introduces students to machine learning algorithms, data preprocessing techniques, and practical applications. Students will gain a hands-on understanding of how to build machine learning models, evaluate their performance, and apply them to real-world datasets. By the end of the course, students will be able to design, implement, and evaluate machine learning models using popular Python libraries such as scikit-learn and TensorFlow.

Machine Learning (ML) studies how computers can be made to behave intelligently. In this course we will cover theoretical and practical approaches to ML, with topics to include search, logic, knowledge representation, uncertainty, and different aspects of the performance of ML techniques.

1. Linear Algebra
2. Statistics
3. Programming

1. Students will be able to Understand and Explain Key Machine Learning Concepts: Define the types of machine learning (supervised, unsupervised, reinforcement learning) and the types of problems they solve. Articulate the fundamental concepts of training, testing, validation, overfitting, underfitting, and cross-validation.
2. Students will be able to Preprocess and Prepare Data for Machine Learning Models: Clean, preprocess, and manipulate real-world datasets to ensure they are ready for machine learning applications (handling missing values, normalizing features, encoding categorical variables).
3. Students will be able to use both Implement and Evaluate Supervised and Unsupervised Learning Models.
4. Students will be able to work Independently and Collaboratively on Machine Learning Projects (Develop, implement, and present a comprehensive machine learning project, demonstrating the ability to apply all aspects of the course to a real-world dataset or problem. Collaborate effectively with peers in group assignments and class discussions, sharing knowledge and insights on machine learning topics.)

Géron, A. (2019). Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow (2nd ed.). O'Reilly Media.