Machine Learning in Digital Signal Processing
( 25 Modules )

Module #1
Introduction to Digital Signal Processing
Overview of digital signal processing, importance of machine learning in DSP, and course objectives

Module #2
Basics of Machine Learning
Introduction to machine learning, types of machine learning, supervised and unsupervised learning

Module #3
Signal Processing Fundamentals
Review of signal processing concepts, Fourier transform, filtering, and convolution

Module #4
Time-Frequency Analysis
Introduction to time-frequency analysis, Short-Time Fourier Transform, and Continuous Wavelet Transform

Module #5
Signal Denoising using Machine Learning
Introduction to signal denoising, traditional methods, and machine learning-based approaches

Module #6
Convolutional Neural Networks (CNNs) for Signal Processing
Introduction to CNNs, architectures, and applications in signal processing

Module #7
Recurrent Neural Networks (RNNs) for Signal Processing
Introduction to RNNs, architectures, and applications in signal processing

Module #8
Autoencoders for Signal Processing
Introduction to autoencoders, architectures, and applications in signal processing

Module #9
Signal Compression using Machine Learning
Introduction to signal compression, traditional methods, and machine learning-based approaches

Module #10
Feature Extraction and Selection
Introduction to feature extraction and selection, techniques, and importance in signal processing

Module #11
Classification in Signal Processing
Introduction to classification, machine learning algorithms, and applications in signal processing

Module #12
Regression in Signal Processing
Introduction to regression, machine learning algorithms, and applications in signal processing

Module #13
Clustering in Signal Processing
Introduction to clustering, machine learning algorithms, and applications in signal processing

Module #14
Anomaly Detection in Signal Processing
Introduction to anomaly detection, machine learning algorithms, and applications in signal processing

Module #15
Signal Segmentation using Machine Learning
Introduction to signal segmentation, machine learning algorithms, and applications in signal processing

Module #16
Audio Signal Processing using Machine Learning
Applications of machine learning in audio signal processing, music information retrieval, and speech recognition

Module #17
Image and Video Signal Processing using Machine Learning
Applications of machine learning in image and video signal processing, computer vision, and object recognition

Module #18
Biomedical Signal Processing using Machine Learning
Applications of machine learning in biomedical signal processing, ECG analysis, and medical imaging

Module #19
Machine Learning for Sensor Signal Processing
Applications of machine learning in sensor signal processing, IoT, and wireless sensor networks

Module #20
Real-World Applications of Machine Learning in DSP
Case studies and real-world applications of machine learning in digital signal processing

Module #21
Signal Processing using Deep Learning
Introduction to deep learning, architectures, and applications in signal processing

Module #22
Signal Processing using Transfer Learning
Introduction to transfer learning, applications in signal processing, and fine-tuning pre-trained models

Module #23
Evaluation Metrics for Machine Learning in DSP
Introduction to evaluation metrics, performance evaluation, and model selection in machine learning

Module #24
Challenges and Future Directions in Machine Learning for DSP
Discussion of challenges, limitations, and future directions in machine learning for digital signal processing

Module #25
Course Wrap-Up & Conclusion
Planning next steps in Machine Learning in Digital Signal Processing career

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Machine Learning in Digital Signal Processing ( 25 Modules )

Machine Learning in Digital Signal Processing
( 25 Modules )