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Foyzul Karim
Foyzul Karim

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Machine Learning Foundations for Software Engineers: A Comprehensive Theory-First Approach [draft]

[This is a draft plan, titles can be changed while actually making the course]

A dynamic, futuristic digital illustration showcasing an engineer surrounded by flowing mathematical equations, neural network diagrams, and snippets of code. The background features a blueprint-like grid with interconnected data pathways, symbolizing an end-to-end ML pipeline. The color palette is primarily cool blues and greens, with accents of vibrant orange to highlight innovation. The overall composition should convey a rigorous, theory-first approach to machine learning while emphasizing practical engineering elements. Minimal or no text overlay, focusing on the synergy between mathematics, code, and cutting-edge technology

Module 1: Introduction to Machine Learning for Engineers

Module 1 Intro (Video)

  • Overview of what “Machine Learning for Engineers” entails
  • Why this theory-first approach is crucial
  • Summary of key topics covered in Module 1

Section 1.1: Defining ML from an Engineer’s Perspective

Section 1.1 Intro (Video)

  • Rationale: Why approach ML differently as an engineer
  • High-level summary of topics in Section 1.1

Lesson Video 1.1.1 – ML as a Problem-Solving Toolkit

  • ML vs. traditional programming approaches
  • When to favor ML solutions

Lesson Video 1.1.2 – Integration Points with Conventional Software

  • How ML components fit into existing systems
  • Considerations for production deployments

Lesson Video 1.1.3 – Key Differences in Approach & Methodology

  • Data-centric vs. code-centric mindsets
  • How data workflow and iterative experimentation differ from standard software cycles

Section 1.2: ML Paradigms & Core Concepts

Section 1.2 Intro (Video)

  • Brief overview of supervised, unsupervised, and reinforcement learning
  • Why these paradigms matter for engineers

Lesson Video 1.2.1 – Supervised vs. Unsupervised Learning

  • Definitions, examples, and practical use cases
  • Regression vs. classification in supervised learning
  • Clustering and pattern recognition in unsupervised learning

Lesson Video 1.2.2 – Reinforcement Learning Basics

  • Core idea: agents, actions, and rewards
  • Where RL might be applied in interactive systems

Lesson Video 1.2.3 – Training, Validation, & Test Sets

  • Data splitting strategies
  • Cross-validation for robust evaluation

Lesson Video 1.2.4 – Overfitting & Underfitting

  • Common causes and warning signs
  • Techniques to prevent or mitigate these issues

Lesson Video 1.2.5 – Basic Model Evaluation Metrics

  • Accuracy, precision, recall, F1 score, ROC-AUC
  • When and why to use each metric

Section 1.3: Essential Mathematical Foundations

Section 1.3 Intro (Video)

  • Importance of math for ML theory
  • Overview of how these topics unify ML approaches

Lesson Video 1.3.1 – Probability & Statistics

  • Basic statistical measures and distributions
  • Handling uncertainty in ML

Lesson Video 1.3.2 – Linear Algebra

  • Vectors, matrices, and key operations in ML
  • Why this is crucial for model computations

Lesson Video 1.3.3 – Optimization

  • Error minimization concepts
  • Intuitive look at gradient descent

Section 1.4: ML Pipeline & Terminology

Section 1.4 Intro (Video)

  • Emphasizing the end-to-end flow of an ML project
  • Key terms engineers must master

Lesson Video 1.4.1 – Core Terminologies

  • Models, features, labels, training, inference
  • Data vs. code boundaries

Lesson Video 1.4.2 – ML Pipeline Overview

  • Data collection → preprocessing → training → evaluation → deployment
  • Where engineers typically intervene

Lesson Video 1.4.3 – Why ML Requires Different Workflows

  • Comparison with conventional software
  • The iterative nature of data-driven development

Module 2: Traditional ML Model Landscape

Module 2 Intro (Video)

  • Transition from foundational concepts to concrete ML algorithms
  • Importance of classical models before jumping into deep learning

Section 2.1: Overview of Common ML Models

Section 2.1 Intro (Video)

  • High-level overview of widely used classical models
  • How to choose based on interpretability and complexity

Lesson Video 2.1.1 – Linear Models

  • Linear Regression, Logistic Regression basics
  • Strengths, weaknesses, and real-world use cases

Lesson Video 2.1.2 – Decision Trees & Random Forests

  • Tree-based methods
  • Trade-offs: interpretability vs. performance

Lesson Video 2.1.3 – Support Vector Machines

  • The concept of maximizing margins
  • Kernel tricks for handling non-linear data

Lesson Video 2.1.4 – Model Selection Criteria

  • Matching models to problem types, complexity, and data constraints

Section 2.2: Model Evaluation & Selection

Section 2.2 Intro (Video)

  • Revisit performance metrics and practical heuristics
  • How to avoid common pitfalls

Lesson Video 2.2.1 – Deep Dive into Performance Metrics

  • When to use accuracy, F1, ROC-AUC in real scenarios
  • Class imbalance considerations

Lesson Video 2.2.2 – Overfitting vs. Underfitting in Practice

  • Diagnostics and remedies beyond theory
  • Tools and techniques to systematically address these issues

Lesson Video 2.2.3 – Choosing the Right Model

  • Combining domain knowledge with ML fundamentals
  • Balancing interpretability, performance, and resource constraints

Module 3: Neural Networks & Deep Learning Fundamentals

Module 3 Intro (Video)

  • Why neural networks gained popularity
  • Transition from classical ML to deep learning

Section 3.1: Neural Network Building Blocks

Section 3.1 Intro (Video)

  • High-level architecture of a neural network
  • Key components for building from scratch

Lesson Video 3.1.1 – Neuron, Layers, & Activations

  • Basic computations of a neuron
  • Popular activation functions (ReLU, sigmoid, tanh)

Lesson Video 3.1.2 – Backpropagation Basics

  • Gradient flow explanation
  • Role of partial derivatives in updating weights

Lesson Video 3.1.3 – Loss Functions & Optimizers

  • MSE, Cross-Entropy, and beyond
  • SGD vs. Adam vs. other optimizers

Section 3.2: Advanced Architectures (CNNs & RNNs)

Section 3.2 Intro (Video)

  • How specialized architectures tackle domain-specific data
  • Brief rationale for image vs. sequential tasks

Lesson Video 3.2.1 – Convolutional Neural Networks (CNNs)

  • Convolutional layers, pooling, and their applications
  • Image-based tasks and object recognition

Lesson Video 3.2.2 – Recurrent Neural Networks (RNNs)

  • Sequential data processing
  • Time-series, language modeling basics

Module 4: Large Language Models & Transformer Architectures

Module 4 Intro (Video)

  • The shift from RNNs to Transformers
  • Why LLMs are central in current NLP

Section 4.1: Transformer Fundamentals

Section 4.1 Intro (Video)

  • Overview of the radical change introduced by attention mechanisms
  • Significance of scaling in modern NLP

Lesson Video 4.1.1 – Self-Attention Mechanisms

  • How transformers capture contextual dependencies
  • Multi-head attention basics

Lesson Video 4.1.2 – Position Encodings

  • Preserving word order in a parallel architecture
  • Sinusoidal vs. learned encodings

Lesson Video 4.1.3 – Model Scaling

  • What qualifies as a “large” language model
  • Training and hardware considerations

Section 4.2: Exploring the LLM Landscape

Section 4.2 Intro (Video)

  • Comparison of open source vs. proprietary solutions
  • Licensing and usage concerns

Lesson Video 4.2.1 – Open Source LLMs

  • Llama 2 family, Mistral AI, Falcon, BLOOMZ, MPT
  • Capabilities, typical use cases, and size distinctions

Lesson Video 4.2.2 – Proprietary LLMs

  • OpenAI GPT family, Anthropic Claude, Google PaLM/Gemini
  • Licensing, usage guidelines, and cost factors

Module 5: Pre-training, Fine-tuning & Transfer Learning

Module 5 Intro (Video)

  • Why reusing models makes sense
  • How fine-tuning bridges general knowledge to domain tasks

Section 5.1: How Pre-training Works

Section 5.1 Intro (Video)

  • Explanation of large-scale pre-training approaches
  • Historical context (ImageNet, large text corpora)

Lesson Video 5.1.1 – Learning General Representations

  • The concept of “universal features”
  • Why pre-trained models accelerate development

Section 5.2: Fine-tuning Strategies

Section 5.2 Intro (Video)

  • What it means to adapt an existing model
  • Common pitfalls engineers should watch for

Lesson Video 5.2.1 – Feature Extraction

  • Using pre-trained layers for new tasks
  • When to freeze or unfreeze layers

Lesson Video 5.2.2 – Balancing Performance & Complexity

  • Trade-offs in partial vs. full fine-tuning
  • Domain adaptation strategies

Section 5.3: Transfer Learning in Action

Section 5.3 Intro (Video)

  • Real-life case studies and best practices
  • Steps to ensure successful adaptation

Lesson Video 5.3.1 – Workflow Example

  • Typical pipeline for applying a pre-trained model
  • Data requirements, environment setup

Lesson Video 5.3.2 – Performance Tuning Tips

  • Hyperparameter tweaks, monitoring improvements
  • Handling domain shifts and specialty data

Module 6: Emerging ML Technologies & Ethical Considerations

Module 6 Intro (Video)

  • A forward-looking perspective on ML developments
  • Why ethical and societal factors matter

Section 6.1: Multimodal Models

Section 6.1 Intro (Video)

  • Definition and applications of multimodal approaches
  • Growth of cross-domain tasks

Lesson Video 6.1.1 – Combining Different Data Types

  • Text + images + audio
  • Typical architecture considerations

Lesson Video 6.1.2 – Real-World Use Cases

  • Multimodal search engines, image captioning, video analytics

Section 6.2: Edge AI

Section 6.2 Intro (Video)

  • Why deploy models on-edge?
  • Constraints and benefits for real-time systems

Lesson Video 6.2.1 – Deployment on Edge Devices

  • Hardware limitations (e.g., IoT, mobile)
  • Model compression strategies

Lesson Video 6.2.2 – Practical Implementations

  • Real-world examples of edge inference
  • Maintaining performance under resource constraints

Section 6.3: Ethical AI & Future Perspectives

Section 6.3 Intro (Video)

  • Significance of fairness, accountability, and transparency
  • Evolving regulations

Lesson Video 6.3.1 – Developments in Ethical AI

  • Techniques for bias detection and mitigation
  • Data privacy concerns

Lesson Video 6.3.2 – Emerging Architectures & Potential Impact

  • Continual learning, advanced architectures
  • Staying updated on the latest breakthroughs

Conclusion & Next Steps (Video)

  • Recap of foundational theory learned
  • How to transition to hands-on projects using this theory base
  • Resources & communities for continued learning, collaboration, and staying current

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