| Course Status : | Upcoming |
| Course Type : | Elective |
| Language for course content : | English |
| Duration : | 12 weeks |
| Category : |
|
| Credit Points : | 4 |
| Level : | Undergraduate/Postgraduate |
| Start Date : | 21 Jul 2025 |
| End Date : | 30 Nov 2025 |
| Enrollment Ends : | 31 Aug 2025 |
| Exam Date : | 11 Dec 2025 IST |
| Translation Languages : | English |
| NCrF Level : | 4.5 |
| Industry Details : | Teaching |
| Exam Shift : | Shift 2 |
Note: This exam date is subject to change based on seat availability. You can check final exam date on your hall ticket.
|
swayam@nitttrc.edu.in, swayam@nitttrc.ac.in
|
Week-1:
Representation of shapes and features Ø Introduction
to pattern recognition Ø Shape description, Chain code, Moments,
Convex hull, Medial axis Ø Texture, GLCM Week-2:
Linear algebra, probability, and random variables Ø Vectors,
Distance function, Matrix operation, Tensor product Ø Type
of matrix, Matrix calculus, Gradient, Jacobian, Hessian Ø Numerical
optimization, Gradient descent, Newton’s method Ø Dilemmas
in machine learning, Curse of dimensionality Ø Random
variable, Statistical averages, Moments, Characteristic function, Cumulants Ø Random
vectors, Statistical descriptions, Linear transformation, Real valued normal
random vector, Central limit theorem Week-3:
Regression & Classification Ø Regression,
Linear regression, Design matrix, Squared error, Moore-Penrose matrix Ø Linear
basic function models, Logarithmic curve, Polynomial regression Ø Bayesian
regression, Maximizing Likelihood, Bayesian learning, MAP Ø Classification,
Bayes theorem, Naïve Bayes classifier, Decision region, Decision boundary,
Error probability, Minimizing risk Week-4:
Performance evaluation Ø Confusion
matrix, Accuracy, Precision, Recall, F1-score, Specificity, Sensitivity,
Balanced accuracy, MCC, Cohen’s Kappa score, Geometric mean, Dice similarity
coefficient, Conformity coefficient Ø Graphical
methods, ROC, AUC, DET, PR curve, tSNE Ø Multiclass
classification measure, Macro average, Micro average Ø MSE,
UQI, SSIM, IoU, PCC, VOE Week-5,6:
Clustering Ø Types
of clusters, Criteria of grouping elements, Distance measure, Evaluation
criteria of clustering Ø Hierarchical
clustering, K-means algorithm, K-medoid Ø Graph
based clustering, MST, Kruskal’s algorithm, Prim’s algorithm Ø DBSCAN Ø Soft
clustering, Fuzzy clustering, Overlapping K-means clustering Week-7,8:
Perceptron Ø Rosenblatt’s
perceptron, Perceptron convergence theorem, Relation b/w perceptron and Bayes
classifier, Illustration with AND & OR gate, Limitations Ø Multilayer
perceptron, Batch learning and online learning, Back propagation algorithm,
Activation functions, Rate of learning, momentum, Cross validation Ø XOR
problem, Effective use of back propagation, Back propagation and
differentiation, Hessian and its effect in online learning, Adaptive control
of learning rate Ø Generalization,
Convolutional network Week-9:
SVM Ø Hyperplane
for linearly separable and non-linearly separable patterns, Optimum design of
SVM, Examples of SVM, SVM as linear regression, XOR problem, SVM dual
formulation Week-10:
Deep learning and Autoencoder Ø Evolution
of ANN, Difference b/w AI, ML, and DL, Types of DL models, CNN, Components of
CNN, Parameter computation in CNN, Pretrained models, VGG16, AlexNet Ø Autoencoder,
Deep autoencoder, Variational autoencoder Week-11:
RBF & Dimensionality reduction Ø Cover’s
theorem, RBF, Hybrid learning for RBF network Ø Principle
of self-organization, Principal component analysis, Dimensionality reduction Week-12:
Information theoretic learning: Entropy, Mutual
information, KL divergence, ICA Stochastic learning: Markov chain, Gibb’s sampling, Boltzmann machine, Deep belief net Dynamic programming: Markov decision process, Dynamic programming, Q-leaning
Assignments – 30 %, Final exam – 70 % |
1. Haykin
S., “Neural Networks and Learning Machines”, Prentice Hall, ISBN: 9780131471399
2. C.M.
Bishop, “Pattern Recognition and Machine Learning”, Springer 2006.
3. Tom
Mitchell, “Machine Learning”, McGraw Hill, ISBN:9780071154673
4. Trevor
Hastie, Robert Tibshirani, and Jerome H. Friedman, “The elements of statistical
learning”, 2nd Edition, Springer 2009.
5. Duda
Hart and Stock, “Pattern Classification”, Wiley 2001.
"The SWAYAM Course Enrolment and learning is free. However, to obtain a certificate, the learner must register and take the proctored exam in person at one of the designated exam centres. The registration URL will be announced by NTA once the registration form becomes available. To receive the certification, you need to complete the online registration form and pay the examination fee. Additional details, including any updates, will be provided upon the publication of the exam registration form. For more information about the exam locations and the terms associated with completing the form, please refer to the form itself."
Grading Policy:
- Internal Assignment Score: This accounts for 30% of the final grade and is calculated based on the average of the best three assignments out of all the assignments given in the course.
- Final Proctored Exam Score: This makes up 70% of the final grade and is derived from the proctored exam score out of 100.
- Final Score: The final score is the sum of the average assignment score and the exam score.
Eligibility for Certification:
- To qualify for a certificate, you must achieve an average assignment score of at least 10 out of 30, and an exam score of at least 30 out of 70. If one of the 2 criteria is not met, you will not get the certificate even if the Final score >=40/100.
Certificate Details:
- The certificate will include your name, photograph, roll number, and the percentage score from the final exam. It will also feature the logos of the Ministry of Education, SWAYAM, and NITTTR.
- Certificate Format: Only electronic certificates (e-certificates) will be issued; hard copies will not be dispatched.
Once again, thanks for your interest in our online courses and certification. Happy Learning.
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