The course provides an introduction to machine learning algorithms and applications. Machine learning algorithms answer the question: 'How can a computer improve its performance based on data and from its own experience?' The course is roughly divided into thirds: supervised learning (learning from labeled data), reinforcement learning (learning via trial and error), and real-world considerations like ethics, safety, and fairness.

The purpose of this course is to provide students with an understanding of the major historical and contemporary theories of human development and learning and their educational implications.

In this honors colloquium, in addition to the requirements of the base course, students will need to complete additional work, research, and/or writing as directed by the instructor. Students will perform analysis of some type of data related to the course, and will learn how to create an annotated bibliography or literature review. Additionally, they will display their findings in either a paper or a presentation. Students will learn more in-depth how Sociologists do their research, how to locate and identify peer-reviewed academic work, and to explore a topic of interest to them.

This is a writing-intensive course that fulfills the University's Junior Writing requirement. Each section focuses on a particular aspect of current issues in psychology. The topic is selected based on the expertise of the teaching staff. All sections share similar writing assignments, ranging from in-class short writing assignments to lengthy papers that include literature review. Classes emphasize discussion and extensive peer review of written work. Topics for individual sections will not be available until shortly before the start of the semester.

The course will teach students algorithms that intelligently process, analyze and generate visual data. The course will start by covering 2D image and 3D shape representations, classification and regression techniques, and the fundamentals of deep learning. The course will then provide an in-depth background on analysis and synthesis of images and shapes with deep learning, in particular convolutional neural networks, recurrent neural networks, memory networks, auto-encoders, adversarial networks, reinforcement learning methods, and probabilistic graphical models.

The course will teach students algorithms that intelligently process, analyze and generate visual data. The course will start by covering the most commonly used image and shape descriptors. It will proceed with statistical models for representing 2D images, textures, 3D shapes and scenes. The course will then provide an in-depth background on topics of shape and image analysis and co-analysis. Particular emphasis will be given on topics of automatically inferring function from shapes, as well as their contextual relationships with other shapes in scenes and human poses.