Introductory AI for Both Computer Science and Neuroscience Students

AAAI Conferences

Macalester College offers a single undergraduate elective in artificial intelligence. This course is cross-listed between Computer Science and Cognitive and Neuroscience Studies, and includes majors from both disciplines. Computer science students bring strong technical knowledge and skills but little awareness of the deeper issues of cognition and intelligence, while neuroscience students bring knowledge about biological and psychological models for human and animal behavior, but much less technical knowledge and experience. To meet the needs of these very different student populations, the course was redesigned from the ground up: choice of topics, classroom activities, kinds of assigned work. The resulting course retains strong technical content, and provides opportunities for all students to pursue topics related to their interests. It has maintained strong enrollment by computer science majors, even as the number of majors has declined, and has seen an increasing number of neuroscience majors each time it is offered.

Using Science Fiction in Teaching Artificial Intelligence

AAAI Conferences

Many factors are blamed for the decreasing enrollments in computer science and engineering programs in the U.S., including the dot-com economic bust and the increase in the use of "offshore" programming labor. One major factor is also the lack of bold new vision and excitement about computer science, which thus results in a view of computer science as a field wedded to routine programming. To address this concern, we have focused on science fiction as a means to generate excitement about Artificial Intelligence, and thus in turn in Computer Science and Engineering. In particular, since the Fall of 2006, we have used science fiction in teaching Artificial Intelligence to undergraduate students at the University of Southern California (USC), in teaching activities ranging from an undergraduate upper division class in computer science to a semester-long freshman seminar for nonengineering students to micro-seminars during the welcome week. As an interdisciplinary team of scholar/instructors, our goal has been to use science fiction not only in motivating students to learn about AI, but also to use science fiction in understanding fundamental issues that arise at the intersection of technology and culture, as well as to provide students with a more creative and well-rounded course that provided a big picture view of computer science. This paper outlines the courses taught using this theme, provides an overview of our classroom teaching techniques in using science fiction, and discusses some of the lectures in more detail as exemplars. We conclude with feedback received, lessons learned and impact on both the computer science students and noncomputer-science (and non-engineering) students. "Science fiction like Star Trek is not only good fun, but serves a serious purpose, that of expanding human imagination" Physicist Stephen Hawking (from (Krauss 1995))

Machine Learning

AITopics Original Links

The course will give the student the basic ideas and intuition behind modern machine learning methods as well as a bit more formal understanding of how, why, and when they work. The underlying theme in the course is statistical inference as it provides the foundation for most of the methods covered.

Mathematics for Machine Learning: Multivariate Calculus Coursera


About this course: This course offers a brief introduction to the multivariate calculus required to build many common machine learning techniques. We start at the very beginning with a refresher on the "rise over run" formulation of a slope, before converting this to the formal definition of the gradient of a function. We then start to build up a set of tools for making calculus easier and faster. Next, we learn how to calculate vectors that point up hill on multidimensional surfaces and even put this into action using an interactive game. We take a look at how we can use calculus to build approximations to functions, as well as helping us to quantify how accurate we should expect those approximations to be.

Complete Data Science guide -Keras library for deep learning


Keras is an open source neural network library written in Python. It is capable of running on top of MXNet, Deep learning Tensorflow, CNTK, or Theano. Designed to enable fast experimentation with deep neural networks, it focuses on being minimal, modular, and extensible. This course provides a comprehensive expert level details in deep learning(Keras). We start by a brief recap of the most common concepts found in machine learning.