Course Outline

Description

How can we create machines that think, learn, and solve problems? This course explores the fascinating field of artificial intelligence (AI), introducing the fundamental concepts, techniques, and ethical considerations that drive this rapidly evolving discipline.

Building upon your programming knowledge, you will explore key AI paradigms including search algorithms, evolutionary algorithms, swarm intelligence, and machine learning. You will implement AI solutions to real-world problems, and gain an understanding of how to think about contemporary AI development.

This course balances theoretical foundations with practical applications, and encourages critical thinking about both the capabilities and limitations of artificial intelligence. You will develop technical skills valuable across a variety of domains. By the end of the course, you will understand AI’s core principles and possess the ability to design, implement, and evaluate basic AI systems.

Learning Outcomes

1. Apply fundamental AI search algorithms to develop solutions for complex problem domains.
2. Implement and evaluate evolutionary algorithms and swarm intelligence techniques to solve optimization problems.
3. Develop machine learning models to address real-world classification and pattern recognition tasks.
4. Compare and evaluate different AI paradigms to determine appropriate approaches for specific problem domains.

Readings

• Hurbans, Rishal. Grokking Artificial Intelligence Algorithms. Manning, 2020.

Evaluation

• Midterm and Final Examination: Both tests will cover everything we’ve discussed in class and read about. You’ll see conceptual questions, complexity analysis problems, and algorithm design challenges.

• Engagement: Your engagement grade comes from completing exercises on time, active participation, and showing up regularly. The exercises are based on our readings, and are listed on the class schedule.

External Links

1. Class Schedule: Current schedule of readings and exercises
2. Course Repository: Worksheets and other documents, including solutions to problems
3. Electronic Whiteboard: For information sharing during class
4. Etherpad: For code sharing during class
5. DrRacket: Our development environment, to be installed on your machine
6. How to Design Programs (Prologue): Quickly get some experience with Racket
7. Simply Scheme: Introducing Computer Science: Learning Scheme step-by-step
8. The Structure and Interpretation of Computer Programs: Learning Scheme in large chunks
9. Fred Overflow solves Fizzbuzz: Using REPL-driven development
10. Algorithms using IKEA Instructions: Happy and confused faces
11. Beating the Averages: The Blub Paradox

Racket

We use Racket because its clean presentation lets you focus on algorithms instead of fighting with syntax. More importantly, functional programming forces you to think recursively, which is exactly how many of the most powerful algorithms work. Tree traversals, graph searches, divide-and-conquer algorithms: they all become clearer when you’re not constantly mutating variables and tracking state.

Racket stands in the tradition of languages like Lisp and Scheme: languages created to explore the deepest ideas of computer science, not just to follow industry trends. Dijkstra once said that Lisp has been called “the most intelligent way to misuse a computer,” and he meant it as a compliment because “it has assisted a number of our most gifted fellow humans in thinking previously impossible thoughts.” That’s what we’re after here. Using Racket will change how you think about algorithmic problems, often revealing solutions that mirror the logical structure of the problems themselves.

1. Download Racket from here. There are versions for all modern Macs, Linux machines, and Windows. The file name will look something like:

   • racket-9.x-aarch64-macosx-cs.dmg on an Apple Silicon Mac
   • racket-9.x-x86_64-macosx-cs.dmg on an Intel Mac
   • racket-9.x-x86_64-win32-cs.exe on a PC
   • racket-9.x-x86_64-linux-cs.sh on Linux

2. Install Racket: On a Mac, open the .dmg file and drag the Racket folder to Applications. On a PC, run the .exe installer and follow the prompts. On Linux, open a terminal and run: sh racket-9.x-x86_64-linux-cs.sh.

3. Start DrRacket: On a Mac: this is in the Racket folder of your Applications list. If your computer complains that DrRacket is not from a “trusted developer,” go into the Finder, then Applications, right-click DrRacket, and click “Open” to bypass this. You only need to do this once. On a PC: this is in the Racket folder of your Applications list. On Linux: after installation, you can usually start DrRacket from your Applications menu. If not, open a terminal and run: drracket &

Lab computers in Dickinson have DrRacket pre-installed, if you prefer not to install it on your personal machine. You can also use them as a backup if your installation runs into problems.

Course Policies

1. Outline: This outline is subject to arbitrary change. I shall announce any changes in class; if you are not present, you are still responsible for finding out what I announce.
2. Late exercises: Exercises will not be accepted late without an acceptable medical or compassionate reason. All exercises are due at the beginning of class for which they are assigned.
3. Attendance: Come to class. Three unexcused absences might mean a marginal pass; four could mean a failure, even if your other work is fine. Excused absences don’t count.
4. Laptops and Cell Phones: Don’t use mobile technology in class unless I specifically permit it. Take notes with pencil and paper (unless you have special accommodations).
5. AI Tools: Don’t use ChatGPT or similar tools for assignments unless I explicitly allow it. If I do allow it, you must cite exactly how you used it according to Chicago style.
6. Office hours: Office hours are first-come-first-serve, unless you have an appointment. If you want to schedule something, email me a few times that work for you.
7. College Policies: Be familiar with the college rules on class attendance, as well as academic and artistic ethics.
8. Grading: If you opt for a letter-grade, your mark in the course will be translated according to the following scale: A+ (90–100), A (85–89), A– (80–84), B+ (77–79), B (73–76), B– (70–72), C+ (67–69), C (63–66), C– (60–62), D (50–59), F (0–49). If you do not opt for a letter-grade, the scale is as follows: Pass (65–100), Marginal Pass (50–62), Fail (0-49).

A Note about AI within Computer Science

Artificial intelligence can generate code, but it cannot decide what problems are worth solving, nor judge whether a solution is effective, ethical, or just. Computer science is less about mastering tools than about cultivating the habits of mind that let us interpret, understand, and evaluate. It asks how abstractions shape systems, how systems shape society, and how we might imagine technology serving human flourishing rather than undermining it. To study CS is therefore not only to learn how machines work, but to engage in broader questions about seeing clearly, reasoning correctly, and imagining responsibly in a world increasingly shaped by computation.

A Note on Liberal Arts Learning

An overarching objective of this course is to help you develop as a student of the liberal arts. True students of the liberal arts are able to reflect on the context in which they live, and reason about what it means to live a meaningful and happy life. Thus, they are able to be more than just children of their own time. But this means we must be willing to put our ideas to the test, see our own errors, and develop intellectual courage and humility. It also helps not to take ourselves too seriously.