The subject matter of this undergraduate-level introductory course is to provide students a broad overview of many concepts and algorithms in Machine Learning (ML) and equip students with the skills to apply these concepts to real world problems. After completing the course, the students, learn about the basic concepts in main machine learning. Topics will include Nearest Neighbor Classifier, Linear Regression, Least Squares, Learning Theory, Statistical Estimation: MLE, MAP, Naive Bayes Classifier, Linear Classification Models: Logistic Regression, Linear Discriminant Functions, Support Vector Machines, Decision Tree Learning, Ensemble Methods: Bagging, Boosting, Clustering, Ethics in Machine Learning, Feature Engineering: Extraction and Selection.

The aim of this course is to explore the concepts and algorithms at the foundation of modern artificial intelligence, diving into the ideas that give rise to technologies like game-playing engines, handwriting recognition, and machine translation. Through hands-on projects, students gain exposure to the theory behind graph search algorithms, classification, optimization, reinforcement learning, and other topics in artificial intelligence and machine learning as they incorporate them into their own Python programs. By the end of the course, students emerge with experience in libraries for machine learning as well as knowledge of artificial intelligence principles that enable them to design intelligent systems of their own.

This course is designed to provide students with a general understanding of the basic concepts and core techniques of data science. Students explore the computational methods and statistical tools to analyze and make sense of data. Upon completing the course, students will be able to: utilize tools to collect, clean and visualize data; employ data management techniques to effectively access, manipulate and store data; apply statistical methods to make predictions based on data; communicate their results through descriptive summaries and visualizations. Topics will include: Data collection and data management, visualization and basic statistics, hypothesis testing and causality, similarity, neighbors and clusters, large scale data analysis, collaborative filtering.

Introduces concepts of Artificial Intelligence. Presents tools to form well-defined Artificial Intelligence problem formulations. Studies tools and structures to design intelligent agent systems. Presents tools to solve problems based on the structure of the problem and the search space.