Courses & Requirements

Requirements for the Foundations of Artificial Intelligence Minor

The minor requires a minimum of five courses (20 credit hours): two core courses and at least three elective courses.  The three elective courses must come from at least two of the three categorical groups.  At least three courses (12 credit hours) for the minor must be taken at Agnes Scott.

Required Core Courses

PHI-110: Introduction to Artificial Intelligence (4.00)

INTRODUCTION TO ARTIFICIAL INTELLIGENCE--This course introduces students to the central concepts of computer science and artificial intelligence. We will examine questions such as "What is a computer?", "What makes a function or number computable?", "What are algorithms and how do they differ from programs and heuristics?", "What does it mean to implement a program?". Students will learn, e.g., the difference between formal systems, finite state automata, and Turing machines. We will consider fundamental issues in AI such as how programs relate to the world, what makes a system intelligent, and whether computers can have minds. Students will also become acquainted with narrower topics in AI such as knowledge representation, machine learning, artificial neural networks, natural language processing, and robotic perception. Finally, we will explore some of the ethical challenges that face AI such as whether intelligent artificial systems deserve rights, whether they should be relied upon to make life-or-death decisions, and whether we should create such systems in the first place. While the course will not require students to learn any particular programming language, it will introduce them to basics of such languages and will train them in a notation resembling a simplified programming language---what is known as pseudocode. Assignments will include program-design projects using pseudocode, position papers, and a final exam.

MAT-131: Introduction to Computer Programming (4.00)

This introduction to computer science, developed by Google and their academic computer science partners, emphasizes problem solving and data analysis skills along with computer programming skills. Using Python, you will learn design, implementation, testing, and analysis of algorithms and programs. And within the context of programming, you will learn to formulate problems, think creatively about solutions, and express those solutions clearly and accurately. Problems will be chosen from real-world examples such as graphics, image processing, cryptography, data analysis, astronomy, video games, and environmental simulation. You'll get instruction from a World-class computer science professor, delivered remotely through video and interactive media. Then you will attend class for collaborative team projects to solve real-life problems, similar to those a team at Google might face. Prior programming experience is not a requirement for this course. Cross-listed with PHY-131.

Elective Courses (must come from at least 2 groups)

Group I: Programming

MAT-231: How to Think Like a Data Scientist (4.00)

This course introduces students to the importance of gathering, cleaning, normalizing, visualizing, and analyzing data to drive informed decision-making, no matter the field of study. Students will learn to use a combination of tools and techniques, including spreadsheets, SQL, and Python to work on real world datasets using a combination of procedural and basic machine learning algorithms. They will also learn to ask good, exploratory questions and develop metrics to come up with a well thought-out analysis. Presenting and discussing an analysis of datasets chosen by the students will be an important part of the course. Like PHY/MAT-131, this course will be "flipped," with content learned outside of class and classroom time focused on hands-on, collaborative projects. Cross-listed with PHY-231.

Course requisites: PHY/MAT-131 (or permission)

MAT-325: Mathematical Models and Applications (4.00)

Development of techniques of model building. Applications to illustrate the techniques drawn principally from the natural and social sciences. Offered alternate years.

Course requisites: 206 or 220 with a grade of C- or better

Group II: Logic

PHI-103: Logic (4.00)

An introduction to the rudiments of critical thinking, with emphasis on analysis of ordinary discourse into formal symbolism, and to the properties of formal systems.

PHI-303: Intermediate Logic (4.00)

This course introduces students to logical meta-theory. After reviewing the semantics and proof theory for First-Order Logic (FOL) and Classical Propositional Logic (CPL) as well as some basic set-theoretic concepts, we proceed to investigate the various meta-logical properties of FOL and CPL, such as soundness, completeness, and decidability. We will also explore the concept of computability via Finite State Automata and Turing Machines. From there, we turn to the meta-theory of nonclassical logics such as Modal Logic, Intuitionistic Logic, Relevant Logic(s), Fuzzy Logic, Deontic Logic(s) and Nonmonotonic Logic(s). Students will also be trained to use the typesetting markup language LaTex.

Course requisites: PHI-103 or MAT-204

MAT-204: The Art of Mathematical Thinking (4.00)

An introduction to the study of the role of proof in mathematics, mathematical writing and grammar and abstraction and critical thinking, using topics from areas such as set theory, logic, discrete mathematics and number theory.

Course requisites: 119 with a grade of C- or better.

Group III: Neuroscience

BIO-350: Foundations of Neuroscience I (3.00)

This course requires students to understand the basics of the nervous system at the cellular and subcellular level as well as equip students with scientific tools such as critical analysis of primary literature, development of an inquiry based project, and presentation of scientific research. Cross-listed with PSY-350.

Course requisites: BIO-350 & BIO-350L must be taken concurrently.

BIO-350L: Inquiry Based Research Neuroscience Lab (1.00)

INQUIRY BASED RESEARCH IN NEUROSCIENCE LAB--In this laboratory co-requisite course to BIO-350, students are given background material and generate their own line of scientific inquiry with tools and specific techniques explained and taught. Based on their questions and the techniques available, they will design experiments and analyze the results.

Course requisites: BIO-350 & 350L must be taken concurrently.

BIO-351: Foundations of Neuroscience II (3.00)

This course requires students to understand the basics of the nervous system at the systems level and equips students with scientific tools such as critical analysis of primary literature, development of an inquiry based project, and presentation of scientific research. Cross-listed with PSY-351.

Course requisites: BIO-351 & BIO-351L must be taken concurrently.

BIO-351L: Inquiry Based Research Neurosci II Lab (1.00)

INQUIRY BASED RESEARCH IN NEUROSCIENCE II LAB--In this laboratory co-requisite course to BIO-351, students are given background material and generate their own line of scientific inquiry with tools and specific techniques explained and taught. Based on their questions and the techniques available, they will design experiments and analyze the results.

Course requisites: BIO-351 & 351L must be taken concurrently.

PSY-311: Animal and Human Learning (4.00)

Principles of learning, behavioral change, and motivation in humans and other animals. Emphasis on conceptual, methodological, and theoretical findings in classical, operant, and observational learning, with a focus on application in a variety of settings.

Course requisites: PSY-101 and PSY-207

PSY-315: Cognitive Neuroscience (4.00)

Human cognition and perception and their neurophysiological correlates as revealed by functional imaging techniques and clinical populations. Selected topics include basic neuroanatomy and brain imaging techniques and their application to the study of attention, memory imagery, concept formation, language, problem solving, creative thinking, and intelligence.

Course requisites: PSY-101 and PSY-207

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