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    <h2>Information </h2>
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      <h5>Instructors: R. Venkatesh Babu </h5>
      <h5>Teaching Assistant: Badrinath Singhal, Priyam Dey and Varun Varma Thozhiyoor </h5>
      <h5>Classroom timings & venue: <span style="color: red;"> Tue/Thu 4PM - 5:30PM at CDS 102 </span></h5>
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      <h5>Tutorial timings & venue: Fri 11:30AM - 1:00PM at CDS 102</h5>
      <h5>Note: Tutorials will generally be held depending on the course requirements or requests from the students.</h5>
      <!-- <h5>First Class: Wednesday, 7 Jan 2026, at 5:00 PM at CDS-202 </h5> -->
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      <!-- <h5> Regular Class Timings (from 14th Jan 2025): Mon 11:30 - 13:00, Wed 11:30 - 13:00, Location: CDS 102 seminar room </h5> -->
      <h5> Course Registration Form: <a href= 'https://forms.gle/5cPpti7N8rL8MC659' target='blank'> Link to Google Form</a> <strong>Filling up the Google form is compulsory.</strong></h5>
      <h5>Teams Code (2026 DLCV): t21qooo</h5>
<!--       <h5 style="color: red;">Please note: Tomorrow's class will be replaced by a talk by Dr. Ekdeep Singh Lubana at 4:00 PM in CDS 102. Attendance is mandatory for DS 265 students.</h5> -->
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      <h2>Brief description of the course</h2>

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<p>In the recent years, Deep Learning has pushed to boundaries of research in many fields. This course focuses on the application of Deep Learning in the field of Computer Vision. The first half of the course formulates the basics of Deep Learning, which are built on top of various concepts from Image Processing and Machine Learning.  The second half highlights the various flavors of Deep Learning in Computer Vision, including Generative Models (VAE, GANs, Diffusion Models, Flow Matching), 3D reconstruction (NeRFs, 3D Gaussian Splatting etc), Recurrent Models, and Deep Reinforcement Learning Models, as well as foundational models such as Stable Diffusion.
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      <a href="{{ site.baseurl }}/about" class="read-more">Read More</a>

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    <h2>Prerequisites</h2>
    <p>Primary (crucial): Machine Learning and Computer Vision / Image Processing
    </p><p>Secondary (familiarity preferred): Probability, Statistics and Linear Algebra.
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    <h2>Course Outcomes</h2>
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<li>Thoroughly Understanding the fundamentals of deep learning.</li>
<li>Gaining knowledge of the different modalities of deep learning currently used.</li>
<li>Gaining Knowlegde about state-of the art models and other important works in recent years.</li>
<li>Learning the skills to implement deep learning based AI systems (use of multiple packages etc.)</li>
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