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UTA027: Artificial Intelligence
TIET Patiala

Course Website

Instructors:

  1. ( RGB) Raghav B. Venkataramaiyer <bv.raghav>
  2. ( STT) Stuti Chug <stuti.chug>
  3. ( ABJ) Anu Bajaj <anu.bajaj>
  4. (PTKRA) Parteek Saini <psaini_phd24>

(1)Academic Calendar

  1. Requires thapar.edu login

Course Syllabus

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Evaluation

Code Title Date Weightage
SESS#A1 Assignment 1 CE 10
SESS#A2 Assignment 2 CE 10
SESS#QZ1 Quiz 1 18-02-2025 1730 IST 05
MST Mid Sem Exam TBA 25
SESS#QZ2 Quiz 2 06-05-2025 1730 IST 05
EST End Sem TBA 45

Schedule of Lectures

Orientation

  1. Overview and administrative details.

Predicate Calculus [MST]  [EST]

  1. Predicate Calculus
    Introduction to Predicate Logic and Representation of Knowledge and Heuristics
  2. Reasoning (Predicate Calculus)
  3. Application to Knowledge Graphs and Lifecycle of a research problem

See also: A01

The Graphical Standpoint [MST]  [EST]

  1. Graph Theory + BFS/DFS.
  2. Dijkstra’s Algorithm for Single-Source Shortest Path.
  3. Problem solving from a graphical stand point

Classical ML [MST]  [EST]

  1. Introduction to ML
  2. Linear Regression
  3. Classification and Logistic Regression
  4. Support Vector Machines

See Also: ML Notes or, Download

Neural Methods [MST]  [EST]

  1. Neuron and it application in Regression/ Classification
  2. Neuron and Piece-wise (Universal) Approximation
  3. Deep Neural Networks
  4. Recurrent Neural Networks
  5. State Machines and Reinforcement Learning

Computer Vision (Classical) [EST]

  1. Overview and Problems; Visual Cognition
  2. Canny’s Edges & Harris’ Corners
  3. Template Matching
  4. Hough Transform
  5. Pictorial Structures
  6. Visual Bag of Words
  7. Haar Cascades
  8. HOG+SVM

Computer Vision (Deep Learning) [EST]

  1. Convolution and its Arithmetic
  2. Visual Object Classification (Sequential and Residual Networks)
  3. Object Detection (Region Proposals and YOLO)
  4. Segmentation (U-Net)
  5. SLAM (Overview)
  6. Adversarial Learning
  7. Pix2Pix & Patch GAN
  8. StyleGAN (Progressive GAN, BiGAN)

Introduction to Advanced Topics [EST]

  1. Attention
  2. Diffusion
  3. Inverse Rendering

Schedule of Assignments123

S.No. Desc/Link Deadline
A01 Predicate Calculus 20-01-2025 0500 IST
A02 Graph Methods (TBA) Practice: Python and Algos 10-02-2025 0500 IST
A03 Linear Regression 24-02-2025 0500 IST
A04 Neural Regression ~10-02-2025 0500 IST~
A05 Sequence-to-Sequence Translation ~17-02-2025 0500 IST~
A06 Classify Sketches 01-04-2025 0500 IST
A07 Segment Sketches 14-04-2025 0500 IST
A08 Visual Object Detection 21-04-2025 0500 IST
A09 GAN based Generative AI 28-04-2025 0500 IST
A10 Proposals 05-05-2025 0500 IST

Resources

  1. [CL] The Central Library (Link)
  2. [RR] RefRead (Link)
  3. [TB] [CL] [RR] Luger, G. F. & others. (1998). Artificial intelligence: Structures and strategies for complex problem solving (Sixth). Pearson Education India. ISBN: 9788131743744
  4. [TB] [CL] Bishop, C. M. (2006). Pattern recognition and machine learning. Springer. ISBN: 9788132209065
  5. [RB] [CL] Cormen, T. H., Leiserson, C. E., Rivest, R. L., & Stein, C. (2022). Introduction to Algorithms (Fourth). MIT Press. ISBN: 9788120340077
  6. [RB] [CL] Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani. (2013). An Introduction to Statistical Learning (1st ed.). Springer. DOI: 10.1007/978-1-4614-7138-7 ISBN: 9781461471387 (Link)
  7. [RB] [CL] MacKay, D. J. C. (2003). Information theory, inference and learning algorithms. Cambridge University Press. ISBN: 9780521670517 (Link)
  8. [RB] Bertsekas, D., & Tsitsiklis, J. N. (2008). Introduction to probability (Vol. 1). Athena Scientific. ISBN: 9781886529236 (Google Scholar)
  9. [YT] [MOOC] Introduction to Probability. (MIT-OCW) (Archive 2011) (Archive 2018)
  10. [YT] [MOOC] Algorithms Illuminated. by Tim Roughgarden Videos: Part 1 Basics, Videos: Part 2 Graphs and Official Website
  11. [RB] [CL] Jurafsky, D., & Martin, J. H. (2025, January). Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition. ISBN: 9789332518414 (The Book), (The Chapter on Logistic Regression), (Official Website)
  12. [MOOC] Illinois Institute Page on Logistic Regression.
  13. [YT] Late Prof. Winston’s Lecture on SVM (MIT-OCW) Video by MIT-OCW

  1. Each assignment carries weightage of 2 marks ↩

  2. Some of the assignments are competition and leaderboard based; Marks awarded shall be based on rank on leaderboard. ↩

  3. A01-05 shall reflect on the webkiosk as consolidated SESS#A1 (MM:10); and similary A06-10 as SESS#A2 (MM:10). ↩