Another section (CS-1102-2) of this course will be offered by
Debayan Gupta. Please find more details on AMS.

Logistics:

Credits:	4
Classes:	Tuesdays and Fridays, 8:30am to 10:00am
Classsroom:	AC-03-LR-003
Teaching Fellows:	Adwitee Roy, Aritra Das
Instructor Office Hours:	Fridays, 10:00am to noon at AC04-705 or by appointment

Links:

Course Outline and Lectures

Resources and Reading Materials

Discussion Sessions (Coming Soon)

Lab Sessions (Coming Soon)

Frequently Asked Questions

Books and Movies in Context

Course Description: This introductory course provides a foundational understanding of computational thinking as a problem-solving strategy, explores the core principles of computation, and introduces students to various subdisciplines within the field. Students will learn how to approach and solve problems both with and without code, with a strong emphasis on correctness, design, and style. The course is structured around two key computational models: the functional model, which views computation as the evaluation of mathematical functions, and the imperative model, which focuses on sequences of instructions that manipulate program state. By exploring these two perspectives, students will develop a deeper understanding of how different programming paradigms influence problem-solving and algorithm design. The course concludes with a series of lectures that offer disciplinary breadth through topics such as information theory, cryptography, compiler design, databases, numerical algorithms, quantitative modeling, and machine learning. The objectives of this course are to:

1. Algorithmic Thinking: Develop the ability to analyze and design algorithms as systematic problem-solving approaches, with an emphasis on precision and efficiency.
2. Program Correctness and Design: Understand the theoretical principles of program correctness, as well as design principles that emphasize clarity, structure, and style in solutions.
3. Computational Models: Explore computer science and computation through two core paradigms: the functional model (evaluation of mathematical functions) and the imperative model (state manipulation via sequences of instructions).
4. Practical Coding Skills: Gain hands-on experience coding in OCaml and Python, with opportunities to explore other languages such as C++, Rust, Java, Datalog, and SQL.
5. Foundational Disciplinary Topics: Acquire an introductory understanding of subfields such as information theory, cryptography, compiler design, databases, numerical algorithms, quantitative modeling, and machine learning.

Prerequisites: The course will require a certain amount of mathematical maturity at least at the level of mathematics at the grade XII level, FC-0306, or MAT-1000.

Grading: A serious student of a subject is not an idle spectator to a variety show but learns best by active involvement. This is particularly true of mathematical subjects. This course is structured to incentivise involvement through the following:

1. Assignments: The course includes 10 problem sets. These are designed to reinforce lecture material and develop practical skills in applying theoretical concepts. They constitute 50% of the grade.
2. Examination: Two in-class, closed-book, 1.5 hour examinations carry the remaining 50% of the grade. Students are permitted to bring a single, two-sided "cheat sheet" of their own creation.
3. Extra-credit assignments: Extra-credit assignments are included to make this course fun and customized to student needs. They include a meme contest, expository or argumentative writing, poetry competition, puzzle weekend, hackathon, leetcode Saturdays, and web development. They carry 10% of the grade.

Policies: From Ashoka's Academic Integrity Policy (MyAshoka → Information and Documents → Office of Academic Affairs): plagiarism—which is a matter of producing academic work that borrows, without acknowledging, from another person's work—is a serious academic offense. All violations of Academic Integrity Policy (including but not limited to plagiarism) will be reported to the Academic Integrity Committee. Please familiarize yourself with the policies and sanctions.

Attendance: A minimum attendance of 85% is mandatory, allowing a maximum of 3 unexcused absences. Any additional absences will result in penalties.

Submissions: Assignments are due on Mondays by 9 AM, as outlined in the calendar. A late submission will incur a 15% penalty per day for the first two days. Submissions delayed beyond two days will receive a zero.

Support: Students are encouraged to reach out to University offices such as the Office of Learning Support, Ashoka Center for Well-Being, and Center for Writing and Communication for additional support.

Calendar:

Sunday	Monday	Tuesday	Wednesday	Thursday	Friday	Saturday
Jan 19	Jan 20	Jan 21 Lecture 1 Office Hours	Jan 22	Jan 23	Jan 24 Lecture 2	Jan 25
Jan 26	Jan 27	Jan 28 Lecture 3 Assignment 1 Office Hours	Jan 29	Jan 30	Jan 31 Lecture 4	Feb 1
Feb 2	Feb 3 Assignment 1 Due	Feb 4 Lecture 5 Assignment 2 Office Hours	Feb 5	Feb 6	Feb 7 Lecture 6	Feb 8
Feb 9	Feb 10 Assignment 2 Due	Feb 11 Lecture 7 Assignment 3 Office Hours	Feb 12	Feb 13	Feb 14 Lecture 8	Feb 15
Feb 16	Feb 17 Assignment 3 Due	Feb 18 Lecture 9 Assignment 4 Office Hours	Feb 19	Feb 20	Feb 21 Lecture 10	Feb 22
Feb 23	Feb 24 Assignment 4 Due	Feb 25 Lecture 11 Assignment 5 Office Hours	Feb 26	Feb 27	Feb 28 Lecture 12	Mar 1
Mar 2	Mar 3 Assignment 5 Due	Mar 4 Lecture 13 Office Hours	Mar 5	Mar 6 Mid-term Exam	Mar 7 Lecture 14	Mar 8
Mar 9	Mar 10	Mar 11	Mar 12	Mar 13	Mar 14	Mar 15
Mar 16	Mar 17	Mar 18 Lecture 15 Assignment 6 Office Hours	Mar 19	Mar 20	Mar 21 Lecture 16	Mar 22
Mar 23	Mar 24 Assignment 6 Due	Mar 25 Lecture 17 Assignment 7 Office Hours	Mar 26	Mar 27	Mar 28 Lecture 18‡	Mar 29 Meme Contest
Mar 30	Mar 31 Assignment 7 Due†	Apr 1 Lecture 19 Assignment 8 Office Hours	Apr 2	Apr 3	Apr 4 Lecture 20	Apr 5 Puzzle Weekend
Apr 6 Puzzle Weekend	Apr 7 Assignment 8 Due	Apr 8 Lecture 21 Assignment 9 Office Hours	Apr 9	Apr 10	Apr 11 Lecture 22	Apr 12 Hackathon
Apr 13	Apr 14 Assignment 9 Due	Apr 15 Lecture 23 Assignment 10 Office Hours	Apr 16	Apr 17	Apr 18 No make-up lecture scheduled.	Apr 19
Apr 20	Apr 21 Assignment 10 Due	Apr 22 Lecture 24 Office Hours	Apr 23	Apr 24	Apr 25 Lecture 25	Apr 26
Apr 27	Apr 28	Apr 29	Apr 30 Reading Week	May 1	May 2	May 3
May 4	May 5	May 6	May 7 Exam Week	May 8	May 9	May 10

^† Due date may be extended by one day.
^‡ Lecture may be repeated upon request.