This course is a jump-start on machine learning for data science with the purpose of providing the students a balance between theory and practice of machine learning. In this class, you will learn the nuts and bolts through exploring the major topics to learn from your data. These include classification, clustering, regression, dimensionality reduction and frequent pattern mining. You will also learn the Do's and Don'ts in preparing your data, running your experiments and evaluating your models. You will gain machine learning practice through several experiments on different kinds of datasets.
Specifically, you will learn:
(1) how to explore your data, and prepare it to be fed to machine learning algorithms,
(2) how to choose the right algorithm to model the dynamics driving a phenomenon or solve a problem of interest, and
(3) how to measure the performance of your models.
Wishing you all an excellent machine learning journey!
Instructor: Ansaf Salleb-Aouissi, PhD.
Ansaf Salleb-Aouissi is a computer scientist with a PhD in Machine Learning. She has over a decade of machine learning experience with applications in fields ranging from the power grid, geology and medical informatics. She published several peer-reviewed papers in high quality journals, conferences and books including TPAMI, JMLR, ECML, PKDD, COLT, IJCAI, ECAI and AISTAT. More here.
Meeting times and locations
Lectures: Monday and Wednesday 7:40pm.8:55pm. Room: 407 MATH.