Carnegie Mellon Launches Undergraduate Degree in Artificial Intelligence

I'm starting with gathering the book requirements for some of the electives which might be of interest to me...

For 85-712 COGNITIVE MODELING:

How Can the Human Mind Occur in the Physical Universe? 2009 Author: Anderson, John

ANSI Common Lisp 1996 Author: Graham, Paul

For 85-211 COGNITIVE PSYCH:

Cognitive Psychology and Its Implications 7TH 10 Author: Anderson, John

For 85-814 COGNITIVE NEUROSCIENCE:

No books listed.

For 85-421 LANGUAGE AND THOUGHT:

Language in Mind: An Introduction to Psycholinguistics 2014 Author: Sedivy, Julie

I'll update this with more books shortly.

For 15-386 Neural Computation:

From course website: http://www.cnbc.cmu.edu/~tai/nc17.html Trappenberg T.P. (TTP) Fundamentals of computational neuroscience, 2nd edition, Oxford University Press 2009 (required/recommended). Hertz J, Krogh A, Palmer RG (HKP) Introduction to the theory of neural computation., Addison Wesley 1991 (reference).

For 15-150: Principles of Functional Computation:

From course website http://www.cs.cmu.edu/~15150/ There is no required textbook for the course. All material we expect you to be familiar with will be covered in sufficient detail in the lectures and lecture notes. There is an optional (and free!) text which some students find useful, called Programming In Standard ML (PSML). This book is based on the lecture notes for the predecessor to this course, 15-212.

For CS 15-122: Principles of Imperative Computation:

http://www.cs.cmu.edu/~15122/syllabus.shtml No textbook, but uses C, Emacs, Linux.

For 15-381: Introduction to AI Representation and Problem Solving:

Artificial Intelligence: A Modern Approach, Third Edition (Typical at most schools for teaching Intro to AI/ML.)

For 10-401: Introduction to Machine Learning

Machine Learning, Tom Mitchell. (optional) Pattern Recognition and Machine Learning, Christopher Bishop. (optional) Machine Learning: A Probabilistic Perspective, Kevin P. Murphy, available online, (optional)

Oh cool, what year are you in?

If it's 3rd or 4th I was one of the judges at your systems design practical.

I think its interesting that Deep Learning is just one course in a cluster of electives. To read recent press you might think that's all current AI is.

We never had a differentiated "AI" undergraduate major before. A student who wanted to concentrate on AI/ML would have used their electives to synthesize something they liked. The CS curriculum is:

https://csd.cs.cmu.edu/academic/undergraduate/bachelors-curr...

So that student would pick AI-ish classes using their applications elective and two CS electives.

In contrast, the AI curriculum:

https://www.cs.cmu.edu/bs-in-artificial-intelligence/curricu...

Removes several of the required courses from the CS curriculum (such as the upper-division systems requirement - OS/networking/distributed systems and the logic & languages requirement), adds another required math course (modern regression), and then uses the space from those freed-up courses to add a bit more depth in the AI core. It also shifts the set of available electives towards a more stats/ML/AI-centric group.

It's not a huge change from our CS curriculum, but it's one that lets AI/ML-interested undergrads create something that's more stats-heavy and deeper in AI than they would have been able to with the CS version. Keep in mind this is all still within the school of computer science.

This doesn't change things at the masters and Ph.D. level.

If you are an undergraduate in computer science these days, it is very hard to get into advanced AI/ML classes, which are typically reserved for graduate students. Back before the ML goldrush, a strong CS undergrad interested in AI could elect to take advanced coursework beyond the introductory AI class. Nowadays, good luck getting off the waitlist! Having an official "AI major" does at least tell students, "Hey, we are making it a priority that undergraduates have access to our rich AI curriculum."