Syllabus: ♦ Introductions: An Introduction to Machine Learning Concepts, importance, applications, and examples. ♦ Essential mathematics for machine learning: Linear Algebra and random variables. ♦ Shallow and Deep Neural networks as classifier and function approximator: Single-Layer Perceptron (SLP), Multi-Layer Perceptron (MLP), error back propagation (EBP) algorithm, most important theorems. ♦ Regularization, Normalization, and Optimization with emphasis on stochastic gradient descent (SGD) and its variations. ♦ Convolutional Neural Networks (CNN): History, foundations, architecture, learning. ♦ Application of CNN in computer vision: Most important network (AlexNet , GoogleNet , VGGNet , ResNet , and state of art architecture). ♦ Recurrent Neural Networks: RNN, LSTM, GRU, and state of art networks, learning and applications in natural language and signal processing. ♦ Unsupervised Learning: Auto Encoder (AE), Variational Auto Encoder (VAE), Conditional Variational Auto Encoder (CVAE) ♦ Adversarial learning: Generative Adversarial Networks (GAN) and Conditional GAN (CGAN), mathematical foundation, architecture, applications, and most important networks (GAN, DCGAN, CycleGAN , WGAN, and state of art) ♦ Diverse applications, current status and future of deep learning
References:
1) I. Goodfellow, etc., Deep Learning, MIT Press, 2016.
2) S. Theodoridis, Machine Learning: A Bayesian and Optimization Approach, Academic Press, 2015
3) Top Hot Papers
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Syllabus: Image Acquisition and Simple Transformation Image Sampling and Quantization Two Dimensional System Theory Image Enhancement in Spatial Domain Image Enhancement in Frequency Domain Image Restoration and Optimal Image Processing Color Fundamentals and Color Image Processing A Brief on Wavelets and its Application in Image Processing Image Compression Image Segmentation Morphological Image Processing Representation and Description
References:
R. C. Gonzalez and R. E. Woods, Digital Image Processing, 4th Ed., 2018, Prentice Hall.
A. K. Jain, Fundamental of Digital Image Processing, 1989, Prentice Hall.
J. C. Russ, The Image Processing Handbook, 4th Edition, 2002, CRC Press.
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Syllabus: A Review on Digital Image Processing Advanced Methods in Medical Image Noise Removal Non Local Mean (NLM) Nonlinear Anisotropic Diffusion Filtering Total Variation Wavelet Denoising Sparse Image Denoising Advanced Methods in Medical Image Segmentation: Statistical Methods (GMM, PNN, MLP, …) Region Based Parametric and Geometric Deformable Models Medical Image Registration: Feature Based Voxel Based Medical Image Interpolation
References:
Principles and Advanced Methods in Medical Imaging and Image Analysis, A. P. Dhawan, H.K. Huang, and D. SH. Kim, 2008.
Biomedical Image Processing, Thomas M. Deserno (Editor), Springer-Verlag, 2011.
Medical Image Processing-Techniques and Applications, G. Dougherty, Springer-Verlag, 2011.
Advanced Biomedical Image Analysis, M. A. Haidekker, Wiley, 2011.
Biomedical Images Analysis, R. M. Rangayyan, 2005.
Handbook of Biomedical Image Analysis (3 Volumes), J. S. Suri, D. L. Wilson, and S. Laxaminarayan, 2005.
Mathematical Models for Registration and Applications to Medical Imaging, O. Scherzer, 2006.
Medical Image Analysis Methods, L. Costaridou, 2005.
Insight into Images: Principles and Practice for Segmentation, Registration, and Image Analysis, By: T. S. Yoo, 2004.
Medical Image Processing, Reconstruction and Restoration: Concepts and Methods, J. Jan, 2005.
2-D and 3-D Image Registration for Medical, Remote Sensing, and Industrial Applications, A. A. Goshtasby, 2005.
Medical Image Registration, J. Hanjal, D. Hawkes, and D. Hill, 2001.
Handbook of Medical Imaging – Processing and Analysis, I. N. Bankman, 2000
Pattern Recognition for Medical Imaging, A. Meyer-Base, 2004.
Image Processing Techniques for Tumor Detection, M. Dekker.
Top survey papers.
Lecture Videos (as taught 1400-1401-1):
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Syllabus: Introduction to basics concepts and definition Bayesian Decision Theory and Discrimination Functions Linear Classifier Nonlinear Classifier Introduction to Deep Learning Feature Selection and Feature Reduction Feature Generation I, Basic Feature Generation II, Application Clustering and Validation
References:
S. Theodoridis and K. Koutroumbas, Pattern Recognition, 4th Ed., 2008, Academic Press.
Ch. Bishop, Pattern Recognition and Machine Learning, 1st ed., 2006, Springer Verlage.
R. Duda, P. Hart, and D. Stock, Pattern Classification, 2000, Wiley.
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Syllabus: ♦ Mutual Inductance and Transformers, ♦ Three-Phase Circuits (Balanced and Unbalanced), ♦ Graph Theory and Network Equations, and Tellegen Theorem, ♦ The Laplace Transform in Circuit Analysis, ♦ Natural Frequency, ♦ Transfer Function, Poles, and Zeros, ♦ Two-Port Circuits, ♦ Nodal and Mesh Analysis, ♦ Cut-Set and Loop Analysis, ♦ Modified Nodal Analysis., ♦ State Equations.
References:
1) Ch. A. Desor and E. S. Kuh, Basic Circuit Theory, 1966, McGraw-Hill.
2) C. K. Alexander and M. N. O. Sadiku, Fundamentals of Electric Circuits, 6th, McGraw Hill.
3) R. C. Dorf and J. A. Svoboda, Introduction to Electric Circuits, 9th, 2013, John Wiley.
4) J. W. Nilsson and S. Riedel, Electric Circuits, 11th, 2019, Pearson.
Lecture Videos (as taught 1400-1401-1):
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Syllabus: ♦ Introduction to Circuits, ♦ Basic Concept and Famous Waveform, ♦ Voltage and Current Laws, ♦ Basic Components (R, L, C, VS, IS) ♦ Simple Circuits (Series, Parallels) ♦ Resistive Network Analysis (Thevenin and Norton Equivalent, Nodal and Mesh Analysis) ♦ First Order Circuits (RC and RL), ♦ Second Order Circuits (RLC, RC2, and RL2), ♦ Operational Amplifier (Op-Amp), ♦ Linear Circuits Analysis in Time Domain and Convolution Theorem, ♦ Sinusoidal Steady-State Analysis, ♦ Transfer Functions and Filters, ♦ AC Circuit Power Analysis.
References:
1) Ch. A. Desor and E. S. Kuh, Basic Circuit Theory, 1966, McGraw-Hill.
2) C. K. Alexander and M. N. O. Sadiku, Fundamentals of Electric Circuits, 6th, McGraw Hill.
3) R. C. Dorf and J. A. Svoboda, Introduction to Electric Circuits, 9th, 2013, John Wiley.
4) J. W. Nilsson and S. Riedel, Electric Circuits, 11th, 2019, Pearson.
Lecture Videos (as taught 1400-1401-1):
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Syllabus: ♦ Introduction, ♦ Signals and Systems, ♦ Linear Time-Invariant Systems and Convolution ♦ Fourier Series Representation of Periodic Signals, ♦ The Continuous-Time Fourier Transform, ♦ The Discrete-Time Fourier Transform, ♦ Time and Frequency Characterization of Signals and Systems, ♦ Sampling, ♦ The Laplace Transform, ♦ The Z-Transform.
References:
A. V. Oppenheim, A. S. Willsky, with S. H. Nawab, Signals and Systems, 2nd Ed., 1996, Pearson.
S. Haykin and ,B. V. Veen, Signals and Systems, 2nd Ed., 2002, John Wiley.
دانشجویان گرامی، فهرست زیر لیست مقدماتی و پردازش نشده، دانشجویان فاقد شاخه تحصیلی (دانشجویان ورودی 99 و تعدادی از دانشجویان ورودی 97 و 98) را در بر دارد
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