DATA SCIENCE: MACHINE LEARNING (2/1 Credits)

Learning Outcomes :

On successful completion of this course, student will be able to: Explain fundamental machine learning concepts and differentiate between AI, Machine Learning, and Deep Learning, perform data pre-processing data using Python, including feature engineering; apply the best machine learning model to solve problem(s) using Python; apply a variety of machine learning techniques in software engineering contexts

Topics :

Introduction to AI in Software Engineering, Data Preparation, Regression, Classification, Tree-based Methods, Clustering, Quiz, Fundamentals of Statistical Machine Learning, Machine Learning Process, Supervised Learning, Resampling Methods, Model Selection and Regularization Techniques, Tree-based Methods, Support Vector Machines (SVM), Unsupervised Learning, Deep Learning and Neural Networks, Multiple Testing, Ethical Considerations and Potential Biases in Machine Learning Applications, Project Presentation