Introduction to Machine Learning

Catalog description

3 Lecture Hour(s) The course objective is to equip students with the knowledge and skills to learn machine learning models using contemporary open-source toolsets. Students will apply these skills to various related STEM applications. Fundamental machine learning concepts and models including supervised and unsupervised machine learning, reinforcement machine learning and loss calcuations will be covered. Upon successful completion of this course, students will acquire the following skills: Apply various technologies for ML development, including Jupyter Notebooks, NumPy, statsmodels, pandas, scikit-learn, Keras, TensorFlow, and Matplotlib. Apply linear algebra concepts to machine learning. Design and analyze neural networks. Apply evolutionary computation concepts to practical applications. Utilize programming skills to read, comprehend, and develop machine learning models.