AccelerationConsortium · SissiFeng · Jan 13, 2025
diff --git a/docs/courses/data-science/2.1-single-objective.md b/docs/courses/data-science/2.1-single-objective.md
@@ -1,21 +1,42 @@
 # 🧩 2.1 Single-objective Optimization
 
-% ```{contents}
-% :depth: 2
-% ```
+##�� Learning Objectives
+- Understand the fundamentals of Bayesian Optimization (BO)
+- Learn to implement single-objective optimization using Honegumi
+- Master practical aspects of experimental optimization
+- Develop skills in model validation and analysis
 
-## 🔰 Tutorial
+## 🔰 Tutorial Materials
 
+### 1. Video Tutorial
 ✅ Watch [the portion about real-world Bayesian optimization and honegumi](https://youtu.be/IVaWl2tL06c?si=KQYNfAVw9f0AK-Ip&t=1874) from "A Gentle Introduction to Bayesian Optimization".
 
 <div style="position: relative; overflow: hidden; padding-top: 50%; margin-bottom: 25px; width: 75%;">
-    <iframe src="https://www.youtube.com/embed/IVaWl2tL06c?si=t2BTVJrREts6JPkd&amp;start=1837" title="YouTube video player" style="position: absolute; top: 0; left: 0; width: 100%; height: 100%; border: 0;" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share; fullscreen" allowfullscreen="allowfullscreen" mozallowfullscreen="mozallowfullscreen" msallowfullscreen="msallowfullscreen"  oallowfullscreen="oallowfullscreen" webkitallowfullscreen="webkitallowfullscreen"></iframe>
+    <iframe src="https://www.youtube.com/embed/IVaWl2tL06c?si=t2BTVJrREts6JPkd&amp;start=1837" title="YouTube video player" style="position: absolute; top: 0; left: 0; width: 100%; height: 100%; border: 0;" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture; web-share; fullscreen"></iframe>
 </div>
 
-✅ Read through the homepage of
-[Honegumi](https://honegumi.readthedocs.io/en/latest/), a Bayesian optimization
-template generator. This tool will be used at various points during the course
-to help with demonstrating advanced optimization topics.
+### 2. Key Concepts
+
+#### Bayesian Optimization Fundamentals
+- Surrogate model (Gaussian Process)
+- Acquisition functions
+- Exploration vs. exploitation trade-off
+- Sequential decision making
+
+#### Practical Implementation
+- Parameter space definition
+- Constraint handling
+- Experimental budget management
+- Model validation techniques
+
+### 3. Tools and Framework
+✅ Read through the [Honegumi documentation](https://honegumi.readthedocs.io/en/latest/):
+- Framework overview
+- Template generation
+- Configuration options
+- Best practices
+
+## 💻 Hands-on Tutorial
 
 :::{grid-item-card} Coding Tutorial
 Optimizing a 3D Printed Material for Strength Under Constraints
@@ -28,28 +49,53 @@ Coding Tutorial
 ```
 :::
 
-## 🚀 Quiz
+### Step-by-Step Guide
+1. Environment Setup
+   - Installing required packages
+   - Configuring development environment
+
+2. Problem Definition
+   - Defining parameter space
+   - Setting up constraints
+   - Creating objective function
+
+3. Optimization Implementation
+   - Using Honegumi templates
+   - Configuring AxClient
+   - Running experiments
+
+4. Results Analysis
+   - Model validation
+   - Feature importance analysis
+   - Visualization techniques
+
+## 📚 Additional Resources
 
+### Documentation
+- [Ax Platform Documentation](https://ax.dev/)
+- [BoTorch Tutorials](https://botorch.org/tutorials/)
+- [Gaussian Processes for Machine Learning](http://gaussianprocess.org/gpml/)
+
+### Research Papers
+- ["A Tutorial on Bayesian Optimization"](https://arxiv.org/abs/1807.02811)
+- ["Practical Bayesian Optimization of Machine Learning Algorithms"](https://papers.nips.cc/paper/2012/hash/05311655a15b75fab86956663e1819cd-Abstract.html)
+
+## 🚀 Quiz
 ::::{tab-set}
 :sync-group: category
 
 :::{tab-item} Sp/Su 2024
 :sync: sp2024
-
 https://q.utoronto.ca/courses/370068/assignments/1327549
 :::
-
 ::::
 
 ## 📄 Assignment
-
 ::::{tab-set}
 :sync-group: category
 
 :::{tab-item} Sp/Su 2024
 :sync: sp2024
-
 https://q.utoronto.ca/courses/370068/assignments/1327550
 :::
-
 ::::
diff --git a/docs/courses/data-science/2.2-multi-objective.md b/docs/courses/data-science/2.2-multi-objective.md
@@ -1,36 +1,99 @@
 # 🧩 2.2 Multi-objective Optimization
 
-% ```{contents}
-% :depth: 2
-% ```
+##�� Learning Objectives
 
-## 🔰 Tutorial
+After completing this tutorial, you will be able to:
+1. Understand the difference between single- and multi-objective optimization
+2. Apply Bayesian optimization to multi-objective problems
+3. Interpret Pareto fronts and trade-offs
+4. Implement constraints in multi-objective optimization
 
-% TODO: Add side-by-side buttons!
+## 🔰 Tutorial Materials
 
 ::::{grid} 1 2 2 2
-:::{grid-item-card} Concept Doc
-Single- vs. Multi-objective Optimization
+:::{grid-item-card} Concept Documentation
+**Single- vs. Multi-objective Optimization**
+
+Key concepts covered:
+- Trade-offs between competing objectives
+- Pareto optimality
+- Dominated vs non-dominated solutions
+- Visualization of Pareto fronts
+
 +++
 ```{button-link} https://honegumi.readthedocs.io/en/latest/curriculum/concepts/sobo-vs-mobo/sobo-vs-mobo.html
 :color: info
 :expand:
 :click-parent:
-Concept Doc
+Read Concepts
 ```
 :::
-:::{grid-item-card} Coding Tutorial
-Multi Objective Optmization of Polymers for Strength and Biodegradability
+
+:::{grid-item-card} Hands-on Tutorial
+**Multi-objective Optimization of Polymers**
+
+Practice exercises:
+- Setting up optimization parameters
+- Defining multiple objectives
+- Adding constraints
+- Analyzing Pareto optimal solutions
+- Visualizing results
+
 +++
 ```{button-link} https://honegumi.readthedocs.io/en/latest/curriculum/tutorials/mobo/mobo.html
 :color: info
 :expand:
 :click-parent:
-Coding Tutorial
+Start Tutorial
 ```
 :::
 ::::
 
+## 📚 Additional Resources
+
+### Key Concepts Review
+1. **Pareto Optimality**
+   - Definition and importance
+   - How to identify Pareto optimal solutions
+   - Real-world applications
+
+2. **Trade-off Analysis**
+   - Understanding competing objectives
+   - Quantifying trade-offs
+   - Making informed decisions
+
+3. **Constraint Handling**
+   - Types of constraints
+   - Implementation strategies
+   - Impact on optimization
+
+### Honegumi Tools
+1. **Optimization Setup**
+   ```python
+   from honegumi import MultiObjectiveOptimizer
+
+   # Example setup
+   optimizer = MultiObjectiveOptimizer(
+       parameters=[...],
+       objectives=[...],
+       constraints=[...]
+   )
+   ```
+
+2. **Visualization Functions**
+   ```python
+   # Example visualization
+   optimizer.plot_pareto_front()
+   optimizer.plot_parallel_coordinates()
+   ```
+
+3. **Results Analysis**
+   ```python
+   # Example analysis
+   optimal_points = optimizer.get_pareto_optimal()
+   trade_offs = optimizer.compute_trade_offs()
+   ```
+
 ## 🚀 Quiz
 
 ::::{tab-set}
@@ -39,11 +102,37 @@ Coding Tutorial
 :::{tab-item} Sp/Su 2024
 :sync: sp2024
 
-https://q.utoronto.ca/courses/370068/assignments/1327551
+Test your understanding:
+- [Practice Quiz](https://q.utoronto.ca/courses/370068/assignments/1327551)
+- Focus on concepts and implementation
 :::
 
 ::::
 
+## 📝 Pre-assignment Checklist
+
+Before starting the assignment, ensure you:
+
+1. **Understand Core Concepts**
+   - [ ] Pareto optimality
+   - [ ] Trade-off analysis
+   - [ ] Constraint implementation
+
+2. **Complete Tutorial**
+   - [ ] Read concept documentation
+   - [ ] Follow hands-on tutorial
+   - [ ] Test example code
+
+3. **Setup Environment**
+   - [ ] Install required packages
+   - [ ] Test Honegumi installation
+   - [ ] Verify visualization tools
+
+4. **Review Examples**
+   - [ ] Multi-objective optimization setup
+   - [ ] Constraint definition
+   - [ ] Results analysis
+
 ## 📄 Assignment
 
 ::::{tab-set}
@@ -52,7 +141,9 @@ https://q.utoronto.ca/courses/370068/assignments/1327551
 :::{tab-item} Sp/Su 2024
 :sync: sp2024
 
-https://q.utoronto.ca/courses/370068/assignments/1327552
+Ready for the challenge?
+- [Assignment Link](https://q.utoronto.ca/courses/370068/assignments/1327552)
+- Apply concepts to real-world epoxy optimization
 :::
 
 ::::