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]

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