Mastering
Machine Learning for Real-life Problems
UCERD Rawalpindi
Supercomputing Center
UCERD Murree
This course is designed to provide students with a comprehensive introduction to the fundamental and intermediate theoretical principles of Machine Learning (ML) with with hands-on experience in deploying ML models on edge devices, particularly the Raspberry Pi microcontroller board.. The course covers fundamental machine learning concepts, including supervised and unsupervised learning, deep learning, and neural networks. It give concept of Tiny Machine Learning (TinyML) for resource-constrained devices, and real-time applications on the Raspberry Pi, fostering problem-solving skills and real-world project experience. Students will engage in real-life projects, bridging theory and practice.
On successful completion of this course, the student will be able to:
Recognize the core mathematical concepts of ML solvers using Python programming language
Use ML software stack to model & solve real-time application using embedded micro-controller devices.
Analyze the ML applications while targeting trade-off such as performance, scalability, accuracy, power, energy and system resources.
On successful completion of this course, the student will be able to:
Recognize the core mathematical concepts of ML solvers using Python programming language
Use ML software stack to model & solve real-time application using embedded micro-controller devices.
Analyze the ML applications while targeting trade-off such as performance, scalability, accuracy, power, energy and system resources.
Introduction to Artifical Intelligence, Past, Present and Future. (Slides)
Introduction to Machine Learning (Slides)
• Overview of Machine Learning
• Software Stack (Python Basic) (Slides)
• Applications of Machine Learning
• Complex Engineering Problem: Targets and Objectives (CEP, Rubrics)
Data Prepossessing (Slides)
• Data Collection and Cleaning
• Data Engineering
Data Exploration and Visualization (Slides)
Supervised Learning (Slides)
• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• Nearest Neighbors (k-NN) and Naïve Bayes
Tiny ML (Slides)
Unsupervised Learning (Slides)
• Clustering (K-Means, Hierarchical)
• Dimensionality Reduction (PCA) (Slides)
Neural Networks and Deep Learning (Slides)
• Introduction to Neural Networks
• Deep Learning, Feedforward Neural Network
Deep Learning for Computer Vision : (Slides 1) (Slides 2)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN) (Slides)
Special Topics and Applications (Slides)
• Natural Language Processing (NLP) and Sentiment Analysis
Introduction to Machine Learning (Slides)
• Overview of Machine Learning
• Software Stack (Python Basic) (Slides)
• Applications of Machine Learning
• Complex Engineering Problem: Targets and Objectives (CEP, Rubrics)
Data Prepossessing (Slides)
• Data Collection and Cleaning
• Data Engineering
Data Exploration and Visualization (Slides)
Supervised Learning (Slides)
• Linear Regression
• Logistic Regression
• Decision Trees and Random Forests
• Nearest Neighbors (k-NN) and Naïve Bayes
Tiny ML (Slides)
Unsupervised Learning (Slides)
• Clustering (K-Means, Hierarchical)
• Dimensionality Reduction (PCA) (Slides)
Neural Networks and Deep Learning (Slides)
• Introduction to Neural Networks
• Deep Learning, Feedforward Neural Network
Deep Learning for Computer Vision : (Slides 1) (Slides 2)
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN) (Slides)
Special Topics and Applications (Slides)
• Natural Language Processing (NLP) and Sentiment Analysis
Week 1
Introduction to Artifical Intelligence, Past, Present and Future.
Week 2
Introduction to Machine Learning
Overview of Machine Learning
Software Stack (Python Basic)
Applications of Machine Learning
Complex Engineering Problem: Targets and Objectives
Week 3
Data Prepossessing
Data Collection and Cleaning
Data Engineering
Week 4
Data Exploration and Visualization
Week 5
Supervised Learning
Linear Regression
Week 6
Logistic Regression
Week 7
Decision Trees and Random Forests
Week 8
Nearest Neighbors (k-NN) and Naïve Bayes
Week 9
Unsupervised Learning
• Clustering (K-Means, Hierarchical)
Week 10
• Dimensionality Reduction (PCA)
Week 11
Neural Networks and Deep Learning
• Introduction to Neural Networks
Week 12
• Deep Learning, Feedforward Neural Network
Week 13
Deep Learning :
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
Week 14
Special Topics and Applications
• Natural Language Processing (NLP) and Sentiment Analysis
Introduction to Artifical Intelligence, Past, Present and Future.
Week 2
Introduction to Machine Learning
Overview of Machine Learning
Software Stack (Python Basic)
Applications of Machine Learning
Complex Engineering Problem: Targets and Objectives
Week 3
Data Prepossessing
Data Collection and Cleaning
Data Engineering
Week 4
Data Exploration and Visualization
Week 5
Supervised Learning
Linear Regression
Week 6
Logistic Regression
Week 7
Decision Trees and Random Forests
Week 8
Nearest Neighbors (k-NN) and Naïve Bayes
Week 9
Unsupervised Learning
• Clustering (K-Means, Hierarchical)
Week 10
• Dimensionality Reduction (PCA)
Week 11
Neural Networks and Deep Learning
• Introduction to Neural Networks
Week 12
• Deep Learning, Feedforward Neural Network
Week 13
Deep Learning :
• Convolutional Neural Networks (CNN)
• Recurrent Neural Networks (RNN)
Week 14
Special Topics and Applications
• Natural Language Processing (NLP) and Sentiment Analysis
Deep Learning with Python, Second Edition
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Python Debugging for AI, Machine Learning, and Cloud Computing: A Pattern-Oriented Approach
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
Deep Learning for Medical Image AnalysisVolume:
Author(s):S. Kevin Zhou, Hayit Greenspan and Dinggang Shen (Eds.)
Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization, 2nd Edition
Python Pandas for Beginners: Pandas Specialization for Data Scientist (Python for Beginners in Data Science and Data Analysis Book 2)
Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Python Debugging for AI, Machine Learning, and Cloud Computing: A Pattern-Oriented Approach
Time Series Algorithms Recipes: Implement Machine Learning and Deep Learning Techniques with Python
Deep Learning for the Life Sciences: Applying Deep Learning to Genomics, Microscopy, Drug Discovery, and More
Deep Learning for Medical Image AnalysisVolume:
Author(s):S. Kevin Zhou, Hayit Greenspan and Dinggang Shen (Eds.)
Hands-On Data Analysis with Pandas: A Python data science handbook for data collection, wrangling, analysis, and visualization, 2nd Edition
Python Pandas for Beginners: Pandas Specialization for Data Scientist (Python for Beginners in Data Science and Data Analysis Book 2)
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