Philosophy

Artificial Intelligence (AI) is an area that spans multiple disciplines and impacts almost every sphere of life. It is widely agreed that AI has the potential to have a large impact on “the human condition”, making it imperative that engineers of tomorrow are trained in the scientific, engineering, as well as sociological aspects of AI. AI, by its nature, is a multidisciplinary subject, both in terms of the set of techniques needed to develop it as well as the set of fields it has interactions with. Broadly speaking, we can understand it as the study of strategies that harness data collection and computation in order to enhance and empower human capabilities. While statistical and computational techniques form the core of any AI subfield, the importance of domain knowledge cannot be underestimated. Our designed curriculum is based on the following principles:

1. It provides a solid foundation for the basics in AI, from both the computational as well as statistical perspective. While students do not need a view into all aspects of computer science, they need enough exposure to identify, e.g. what are the hard problems in AI and why, and what engineering is involved in an implementation of AI algorithms.
2. The curriculum is designed to be flexible to adapt to a rapidly changing field.
3. The curriculum is designed to give students enough leeway to specialize in any of the subfields of AI, i.e. students should be able to train themselves in the relevant AI techniques, choose a domain, and then use the AI techniques to synthesize novel questions and applications in their domain of interest.

Over the last few years, the number of faculty in different disciplines working on applications of AI in their respective domains has significantly increased. Familiarity with AI concepts, to various extents, is in high demand both in the industry as well in the government sector. It is therefore proposed that a major in Artificial Intelligence be established at the Institute.
Constraints:
1. Discipline Core Courses - 44 credits
2. Discipline Elective Courses - 20 credits. This consists of the following baskets.
    a. Advanced AI basket,
    b. AI application basket,
    c. CSE elective basket.

AI techniques to synthesize

Core Courses

The discipline-specific curriculum consists of the following core courses.

Course code	Name	Credits
ES 242	Data Structures and Algorithms -1	4
ES 203	Digital Systems	4
CS 301	Theory of Computing	4
ES 215	Computer Organization & Architecture	4
CS 2xx (to be proposed)	AI Software Tools and Techniques	4
AI/CS 2xx (to be proposed)	Mathematical Foundations of AI	4
ES 654	Machine Learning	4
To be proposed	Signals, systems and random processes	4
EE xxx	(Electrical) Control Systems	4
CS 328	Introduction to Data Science	4
To be proposed	Foundations of Artificial Intelligence	4
Total	44

Elective structure

Electives are divided into the following three baskets. 20 credits of electives must be taken. The "advanced AI" and "AI cluster" baskets are just a guiding structure. There is no constraint on the minimum number of credits from these two baskets.

Advanced AI: These courses cover advanced AI topics that are applicable to a variety of domains. No minimum credit limit.
AI applications: These courses cover various AI application clusters, such as language, speech, vision, etc. No minimum credit limit.
CSE Electives (at least 4 credits have to be taken) These courses offer students background in core EECS topics that can help students apply AI for X or use X for AI. For example, if X = Software Engineering, then students wishing to study software engineering for AI or AI for software engineering will find such a course useful.

The following table lists the electives in each basket. This list of courses is expected to be dynamic. When any course gets approved, we should identify the elective basket for that course. In general, listing a course in one of these buckets as an elective will involve going through its curriculum in a discipline meeting.

Selected online courses and CS 299/399/499 courses can contribute at most 8 credits towards the discipline electives.

Basket	Course code	Course Name	Credits
CSE Electives	ES 214	Discrete Math	4
CSE Electives	CS 327	Compilers	4
CSE Electives	CS 301	Operating Systems	4
CSE Electives	CS 433	Computer Networks	4
CSE Electives	EE 411	Digital Signal Processing	4
CSE Electives	CS 434	Software Engineering and Testing	4
CSE Electives	CS 432	Databases	4
AI advanced	AI 4xx	Reinforcement Learning	4
AI advanced	AI 4xx	Game Theory	4
AI advanced	ES 413	Deep learning	4
AI advanced	ES 645	Optimization methods in ML	4
AI advanced	ES 661	Probabilistic ML	4
AI advanced	AI 4xx	Knowledge representation and reasoning	4
AI advanced	CS 4xx (to be proposed)	AI and Ethics	4
AI applications	CS 613	Natural language processing	4
AI applications	EE 645	3D Computer Vision	4
AI applications	ES 659	Computer Graphics	4
AI applications	AI 4xx	Speech Technology	4
AI applications	ES 615	Nature Inspired Computing	4
AI applications	ES 4xx	Robotics	4
AI applications	AI 4xx	AI for Sustainability	4
AI applications	AI 4xx	AI for Earth Sciences	4
AI applications	AI 4xx	AI for Biological engineering	4