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Handbook
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  • TOC {:toc}

Introduction

In the initial stages of this course we will be broadly covering four topics namely Linear Algebra, Multivariable Calculus, Data Analysis and Statistics.

Learning Outcomes

We focus on a skill-based learning approach that fosters a deep and intuitive understanding of AI, moving away from rote memorization to emphasize real comprehension and application.

Here are the key learning outcomes you can expect from our program:

  1. Deep Understanding of some core AI Concepts including basic concepts like machine learning algorithms, neural networks, and data preprocessing, and advanced ones such as deep learning architectures, reinforcement learning, etc.
  2. Learning core mathematical concepts like Linear Algebra, Probability, Statistics, and Calculus in the visualization and intuitive mode.
  3. Getting Hands-on experience in developing Practical Problem-Solving Skills.
  4. Benefit from a structured environment that emphasizes continuous practice and active application of learned skills to solidify your knowledge and expertise in AI.
  5. Building Confidence and Proficiency in AI.

We believe that your active involvement is key to leveraging the full potential of this program.

Expectations from the Participants

We encourage active participation in lectures, workshops, and group discussions, fostering curiosity and enthusiasm for AI technologies. While prior AI knowledge isn't mandatory, a basic understanding of programming and math concepts is beneficial. Collaboration and teamwork are key as students work on projects together and share ideas. Creativity is encouraged to approach AI problems innovatively, alongside critical thinking to analyze algorithms critically. We promote a respectful, inclusive environment where everyone's contributions are valued. Students are expected to take responsibility for their learning and be open to feedback for growth. Expect hands-on learning through coding exercises and real-world AI applications. Join us for a rewarding AI journey at IIT Ropar!

Code of Conduct

  • Maintain cleanliness throughout the campus.
  • No personal vehicles allowed on the campus.
  • Please refrain from doing any kind of substance misuse in the college, including consuming alcohol, smoking, drugs, etc.
  • No sexual misconduct should happen in the college. Legal action will taken in that case.
  • No damage should be done to the college property.
  • Do not involve in any activity that disrupts the overall integrtiy of the institute, any individual or the the Department.
  • Refrain from bringing any unauthorised individuals inside the campus.
  • Do not bring any high power electrical equipments in the hostel, i.e., kettle, iron, cooler, Table Fan etc.
  • If disagreements or conflicts arise, participants should address issues calmly and respectfully with each other. Volunteers are available to help mediate if necessary.
  • If you are leaving the campus for any reason, please inform us in advance through email at aivs@iitrpr.ac.in. Include:
    • Departure and Return times
    • Purpose
    • Companions (if any)
    • hostel and Room number
    • Mode of Travel
    • Contact Info and Emergency Contact Number
  • Do not damage any property of the hostel, use the washrooms responsibly.
  • Violation of any of the above will lead to penalties according to the rules of the institute and the department. If there is severe misconduct, then legal action may be taken, accompanying expulsion from the program and blacklisting from the college.

Schedule

Monday to Friday:

| 7:30 am - 8:30 am | Breakfast | | 8:30 am - 10:40 am | Class (with 10 min Break) | | 10:40 am – 12:40 pm | Roundtable Discussion with peers | | 1:00 pm – 2:30 pm | Lunch | | 4:30 pm – 6:40 pm | Class (with 10min break) | | 7:30 pm – 9:15 pm | Dinner                  |

Saturday and Sunday:

On the weekends, we will be having some fun activities like sports activities, Music and Dance, Movie Sessions, small trips, etc.

Grading

Evaluation of students will be a holistic evaluation without any strict marking scheme and graded assignments. Students will be judged on the basis of contributive and collaborative skills expressed by them during the course.

Project

The student will be asked to pick a project from a list of topics. The projects will mostly be explorative in nature. Every project will be executed by a team of at most 2 people. Evaluation of the project will be based on the report and presentation.