[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
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