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

Introduction to Unit 1 (Video)

• An overview of “Machine Learning for Engineers”
• Why this theory-first approach is crucial
• Summary of key topics covered in Unit 1

---

Section 1.1: Defining ML from an Engineer’s Perspective

Section 1.1 Introduction (Video)

• Why: As an engineer, why you should approach ML differently
• High-level summary of the topics in Section 1.1

Course Video 1.1.1 – ML as a Toolkit for Problem Solving

• Machine Learning vs. Traditional Programming Methods
• When to favor machine learning solutions

Course Video 1.1.2 – Integration Points with Traditional Software

• How machine learning components fit into existing systems
• Considerations for production deployment

Course Video 1.1.3 – Key Differences between Methods and Methods

• Data-centric versus code-centric thinking
• How data workflow and iterative experimentation differ from standard software cycles

---

Section 1.2: ML Paradigms and Core Concepts

Section 1.2 Introduction (Video)

• A brief overview of supervised learning, unsupervised learning, and reinforcement learning
• Why these examples are important to engineers

Course Video 1.2.1 – Supervised Learning vs. Unsupervised Learning

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

Course Video 1.2.2 – Basics of Reinforcement Learning

• Core Ideas: Agency, Action, Reward
• Application of reinforcement learning in interactive systems

Course Video 1.2.3 – Training, Validation and Test Sets

• data segmentation strategy
• Cross-validation for robust evaluation

Course Video 1.2.4 – Overfitting and Underfitting

• Common causes and warning signs
• Technologies to prevent or mitigate these problems

Course Video 1.2.5 – Basic Model Evaluation Metrics

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

---

Section 1.3: Basic Mathematical Fundamentals

Section 1.3 Introduction (Video)

• The importance of mathematics to machine learning theory
• An overview of how these topics unify ML methods

Course Video 1.3.1 – Probability and Statistics

• Basic statistical indicators and distribution
• Dealing with uncertainty in machine learning

Course Video 1.3.2 – Linear Algebra

• Vectors, matrices, and key operations in ML
• Why this is critical to model calculations

Course Video 1.3.3 – Optimization

• Error Minimization Concept
• Understanding gradient descent intuitively

---

Section 1.4: ML Pipelines and Terminology

Section 1.4 Introduction (Video)

• Emphasis on the end-to-end process of ML projects
• Key terms that engineers must master

Course Video 1.4.1 – Core Terms

• Model, features, labels, training, inference
• Data and code boundaries

Course Video 1.4.2 – ML Pipeline Overview

• Data collection→preprocessing→training→evaluation→deployment
• Where engineers typically step in

Course Video 1.4.3 – Why ML requires a different workflow

• Compared with traditional software
• The iterative nature of data-driven development

---

Unit 2: Traditional ML Model Landscape

Introduction to Unit 2 (Video)

• Transformation from basic concepts to specific machine learning algorithms
• The importance of classic models before entering deep learning

---

Section 2.1: Overview of Common ML Models

Section 2.1 Introduction (Video)

• A high-level overview of widely used classic models
• How to choose based on explainability and complexity

Course Video 2.1.1 – Linear Models

• Basic knowledge of linear regression and logistic regression
• Advantages, Disadvantages and Practical Use Cases

Course Video 2.1.2 – Decision Trees and Random Forests

• tree-based approach
• Tradeoff: Interpretability vs. Performance

Course Video 2.1.3 – Support Vector Machines

• profit maximization concept
• Core techniques for handling nonlinear data

Course Video 2.1.4 – Model Selection Criteria

• Match models to problem type, complexity, and data constraints

---

Section 2.2: Model Evaluation and Selection

Section 2.2 Introduction (Video)

• Revisiting performance indicators and practical heuristics
• How to avoid common pitfalls

Course Video 2.2.1 – A Deep Dive into Performance Metrics

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

Course Video 2.2.2 – Overfitting and Underfitting in Practice

• Diagnosis and Remedies Beyond Theory
• Tools and techniques to systematically address these issues

Course Video 2.2.3 – Choosing the Right Model

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

---

Unit 3: Basics of Neural Networks and Deep Learning

Introduction to Unit 3 (Video)

• Why Neural Networks Are Popular
• The transition from classic machine learning to deep learning

---

Section 3.1: Neural Network Construction Module

Section 3.1 Introduction (Video)

• High-level architecture of neural networks
• Key components built from scratch

Course Video 3.1.1 – Neurons, Layers and Activations

• Basic calculations of neurons
• Popular activation functions (ReLU, sigmoid, tanh)

Course Video 3.1.2 – Basics of Backpropagation

• Gradient flow explained
• The role of partial derivatives in updating weights

Course Video 3.1.3 – Loss Functions and Optimizers

• MSE, cross entropy, etc.
• SGD vs. Adam vs. other optimizers

---

Section 3.2: High-Order Architectures (CNN and RNN)

Section 3.2 Introduction (Video)

• How professional architecture handles domain-specific data
• Brief principles of imaging and sequence tasks

Course Video 3.2.1 – Convolutional Neural Network (CNN)

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

Course Video 3.2.2 – Recurrent Neural Network (RNN)

• sequential data processing
• Basic knowledge of time series and language modeling

---

Module 4: Large Language Model and Transformer Architecture

Introduction to Unit 4 (Video)

• The transition from RNN to Transformer
• Why the Master of Laws is the core of NLP today

---

Section 4.1: Transformer Basics

Section 4.1 Introduction (Video)

• An overview of the fundamental changes brought about by the attention mechanism
• What scaling means in modern NLP

Course Video 4.1.1 – Self-Attention Mechanism

• How converters capture contextual dependencies
• Multi-Head Attention Basics

Course Video 4.1.2 – Position Encoding

• Preserve word order in parallel architecture
• Sines and Learning to Code

Course Video 4.1.3 – Model Scaling

• What is a “large” language model?
• Training and Hardware Considerations

---

Section 4.2: Exploring LLM prospects

Section 4.2 Introduction (Video)

• Open source vs. proprietary solutions
• Licensing and usage issues

Course Video 4.2.1 – Open Source LL.M.

• Llama 2 series, Mistral AI, Falcon, BLOOMZ, MPT
• Features, typical use cases and size differences

Course Video 4.2.2 – Exclusive LL.M.

• OpenAI GPT series, Anthropic Claude, Google PaLM/Gemini
• Licensing, Usage Guidelines and Cost Factors

---

Module 5: Pre-training, fine-tuning and transfer learning

Introduction to Unit 5 (Video)

• Why reusing models makes sense
• Fine-tuning how to connect general knowledge to domain tasks

---

Section 5.1: How pre-training works

Section 5.1 Introduction (Video)

• Explanation of large-scale pre-training methods
• Historical background (ImageNet, large text corpus)

Course Video 5.1.1 – Learning General Representations

• The concept of “universal characteristics”
• Why pre-trained models speed development

---

Section 5.2: Fine-tuning the strategy

Section 5.2 Introduction (Video)

• What does it mean to fit into an existing model?
• Common pitfalls engineers should be aware of

Course Video 5.2.1 – Feature Extraction

• Use pre-trained layers to perform new tasks
• When to freeze or thaw layers

Course Video 5.2.2 – Balancing Performance and Complexity

• Trade-offs between partial and full fine-tuning
• domain adaptation strategy

---

Section 5.3: Practical Applications of Transfer Learning

Section 5.3 Introduction (Video)

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

Course Video 5.3.1 – Workflow Example

• Typical pipeline for applying pretrained models
• Data requirements, environment settings

Course Video 5.3.2 – Performance Adjustment Techniques

• Hyperparameter adjustment, monitoring improvement
• Handling domain transfers and professional data

---

Unit 6: Emerging ML Technologies and Ethical Considerations

Introduction to Unit 6 (Video)

• A forward-looking view on the development of machine learning
• Why ethical and social factors matter

---

Section 6.1: Multimodal Models

Section 6.1 Introduction (Video)

• Definition and application of multimodal approaches
• The growth of cross-domain tasks

Course Video 6.1.1 – Combining Different Material Types

• text+picture+audio
• Typical architectural considerations

Course Video 6.1.2 – Practical Use Cases

• Multi-modal search engine, image subtitles, video analysis

---

Section 6.2: Edge Artificial Intelligence

Section 6.2 Introduction (Video)

• Why deploy models at the edge?
• Limitations and benefits of just-in-time systems

Course Video 6.2.1 – Deploying on Edge Devices

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

Course Video 6.2.2 – Practical Implementation

• Real-life examples of edge reasoning
• Maintain performance under resource constraints

---

Section 6.3: Artificial Intelligence Ethics and Future Prospects

Section 6.3 Introduction (Video)

• The importance of fairness, accountability and transparency
• Changing regulations

Course Video 6.3.1 – The Development of Ethical Artificial Intelligence

• Bias detection and mitigation techniques
• Data privacy issues

Course Video 6.3.2 – Emerging Architectures and Potential Impact

• Continuous learning, advanced architecture
• Stay informed about the latest breakthroughs

---

Conclusion and next steps (video)

• Review the basic theories learned
• How to use this theoretical foundation to transition to practical projects
• Resources and communities for continuous learning, collaboration, and staying current