I went through your PDF lecture slides and pulled out all the AI tools, libraries, and platforms
mentioned, organized lecture by lecture. Here’s the complete structured list (none left out):
Lecture 1: Introduction & Motivation
● ChatGPT, Notion AI – text generation
● Midjourney, RunwayML, Adobe Firefly – visual generation (images, video, creative
design)
● Figma AI plugins, Hotjar, Maze – UX and research (prototyping, user testing, analytics)
● Google Colab, Hugging Face – ML prototyping, Python demos, pretrained models
Lecture 2: Fundamentals of AI & ML
● Cloudflare Bot Management, Cloudflare Turnstile – CAPTCHA/bot detection
alternatives
Lecture 3: Generative AI & Design Trends
● GPT, DALL·E, Midjourney, Runway, Stable Diffusion – generative AI models for text,
image, and design
● Adobe Firefly – AI-powered creative tools (images, video, audio, safe generative
content)
Lecture 4: Collaborative Intelligence in Design
● Autodesk Dreamcatcher – generative design for architecture
● Rhino + Grasshopper (ML plugins) – parametric + AI-assisted generative design
● Fusion 360 (AI suggestion engine, topology optimization) – AI-assisted product
prototyping
● CLO 3D, GANs – virtual prototyping and AI fashion design
Lecture 5: AI in Concept Development
● Image Generators: GANs, Diffusion models, DALL·E, GAWWN, Neural Style
Transfer – image creation & manipulation
● Vizcom / Sketch2Render – sketch-to-render AI tool
● Sketch-RNN, Collabdraw, Drawing Apprentice – AI-assisted sketching tools
● Concept Evaluators (ML-based aesthetic scoring, Sentient Sketchbook) – design
evaluation & co-creation
Lecture 5 (continued): AI in Research & Data Mining
● Google AI – research use cases
Data Mining Tools for Designers
● RapidMiner
● Weka
● Orange
● KNIME
● AI-powered analytics in Google Cloud & AWS
Lecture 7: Design Data & Pattern Analysis
● Clustering & Dimensionality Reduction Libraries (implied: scikit-learn, ML clustering
methods)
Lecture 8: Design Data & Pattern Analysis – II
● TensorFlow, PyTorch, Scikit-learn – ML libraries for pattern analysis
● Node-RED, Arduino, ESP32, Raspberry Pi – IoT prototyping platforms
● Figma, Blender, Rhino + Grasshopper – design tools
● Python, MATLAB, R – data handling and analysis
● Tableau, PowerBI – data visualization
● GCP, AWS ML pipelines – cloud-based ML deployment
Lecture 9: AI for UX & Persona Generation
● Google Analytics Insights – data-driven persona generation
● Free User Persona Generators – automated persona creation tools
● Colormind, Fontjoy – AI-driven font and color pairing
● Adobe Express, Canva, RelayThat – AI platforms for branding and content
● Adobe Photoshop (AI editing) – automated image editing
● Tableau (again) – data visualization
● Miro Assist – collaborative brainstorming with AI
I extracted all the short-forms (abbreviations/acronyms) and their full forms from your
lecture slides. Here’s a clean list:
Core AI/ML Terms
● AI – Artificial Intelligence
● ML – Machine Learning
● DL – Deep Learning
● NLP – Natural Language Processing
Models & Architectures
● CNN – Convolutional Neural Network
● RNN – Recurrent Neural Network
● GAN – Generative Adversarial Network
● VAE – Variational Autoencoder
● GAWWN – Generative Adversarial What-Where Network
● t-SNE – t-distributed Stochastic Neighbor Embedding
● PCA – Principal Component Analysis
● LDA – Linear Discriminant Analysis
● UMAP – Uniform Manifold Approximation and Projection
Evaluation & Metrics
● K-L Divergence – Kullback–Leibler Divergence
● WCSS – Within-Cluster Sum of Squares
Data & Pipelines
● ETL – Extract-Transform-Load
● ELT – Extract-Load-Transform
● CEP – Complex Event Processing
● IoT – Internet of Things
Security & Verification
● CAPTCHA – Completely Automated Public Turing test to tell Computers and Humans
Apart
● reCAPTCHA – Revised CAPTCHA (Google’s version with image/street sign tests)
Design & Creativity
● MI-CC – Mixed-Initiative Co-Creativity
● A.I.R. – Athlete Imagined Revolution (Nike case study)
Platforms & Tools
● AWS – Amazon Web Services
● GCP – Google Cloud Platform
● UI – User Interface
● UX – User Experience
👍
Got it This will be a bit long since the slides mention many algorithms and methods across
AI, ML, DL, data mining, and design. To keep it clear, I’ll make lecture-wise structured notes.
Here’s Part 1 (Lectures 1–3) — let me know when to continue with Part 2.
📘 Algorithms & Technical Methods
(Lecture Notes)
Lecture 1: Introduction & Motivation
🔹 Machine Learning Basics
● Supervised Learning
○ Uses labeled data.
○ Example: image classification.
○ Learns mapping from input → output.
● Unsupervised Learning
○ Works with unlabeled data.
○ Example: clustering personas.
○ Discovers hidden structures/patterns.
● Reinforcement Learning (RL)
○ Agent learns by interacting with environment.
○ Uses reward signals to optimize decisions.
○ Example: game strategy learning.
🔹 Representation Methods (ML Models)
● Decision Trees – hierarchical rules for classification/regression.
● Rule-based Systems – IF–THEN logic rules.
● Instance-based Models – nearest neighbor approaches.
● Graphical Models – Bayesian networks, Markov networks.
● Neural Networks – layers of perceptrons learning non-linear patterns.
● Support Vector Machines (SVMs) – maximize decision boundary margin.
● Model Ensembles – combine multiple weak learners (e.g., bagging, boosting).
🔹 Evaluation Metrics
● Accuracy, Precision, Recall, Squared Error
● Likelihood, Posterior Probability
● Cost/Utility-based metrics
● Margin, Entropy, K-L Divergence
🔹 Optimization Methods
● Greedy Search – local best choice (fast, but may be suboptimal).
● Gradient Descent – minimize convex objective functions iteratively.
● Linear Programming – constrained optimization of linear objectives.
🔹 Deep Learning Architectures
● CNN (Convolutional Neural Network) – image analysis.
● RNN (Recurrent Neural Network) – sequential/time-series data.
● Transformers – attention-based, works for text, image, multimodal.
🔹 Generative AI Techniques
● GAN (Generative Adversarial Network) – Generator vs Discriminator competition.
● VAE (Variational Autoencoder) – probabilistic encoding & decoding of latent space.
● Transformers (GPT, DALL·E) – generative sequence models.
● Diffusion Models – iterative denoising to generate high-quality images.
Lecture 2: Fundamentals of AI & ML
🔹 Types of AI
1. Reactive AI – responds only to present input (no memory).
2. Limited Memory AI – uses past data to adapt (most modern AI).
3. Theory of Mind AI – hypothetical, understands human emotions/intentions.
4. Self-Aware AI – hypothetical, human-like consciousness.
🔹 CAPTCHA & reCAPTCHA
● CAPTCHA – distorted text/images used to differentiate humans vs bots.
● reCAPTCHA (Google) – uses street signs, images, behavioral tracking.
● Checkbox CAPTCHA – analyzes human-like cursor movement randomness.
● Invisible reCAPTCHA – runs in background, assigns risk score.
● Cloudflare Bot Management / Turnstile – alternative bot detection methods.
🔹 ML Pipeline
1. Data Collection – gather user interactions, design datasets.
2. Model Training – algorithms like decision trees, neural nets.
3. Evaluation & Deployment – validate on test data, deploy in real use.
Lecture 3: Generative AI & Design Trends
🔹 Core Generative Models
● GANs – generator creates content, discriminator evaluates.
● VAEs – encode input into latent space, decode for variation generation.
● Transformers – sequence-to-sequence, attention-based (e.g., GPT).
● Diffusion Models – iterative process of adding/removing noise to create images.
🔹 Design-specific Generative Techniques
● Shape Grammars – production rules for generating 2D/3D geometric shapes.
● Mixed-Initiative Co-Creativity (MI-CC) – human + AI co-design workflows.
● Sketch-RNN – neural net for completing hand-drawn sketches.
● CRIS Chatbot – for qualitative + quantitative research (AI-aided).
● Sentient Sketchbook – AI-assisted idea generation for game design maps.
✅ That covers all Lectures 1–3 algorithms/methods.
Great 👍 Let’s continue.
Here’s Part 2 (Lectures 4–5: Collaborative Intelligence, Concept Development, Research
& Data Mining).
📘 Algorithms & Technical Methods
(Lecture Notes – Part 2)
Lecture 4: Collaborative Intelligence in Design
🔹 Human–AI Collaboration Principles
● AI > Human Principle – AI should only handle tasks where it clearly outperforms
humans (repetitive/data-heavy).
● Task Allocation –
○ AI → repetitive, data-intensive, optimization tasks.
○ Humans → creativity, strategy, empathy.
🔹 Case Study Tools & Methods
● Autodesk Dreamcatcher – generative design using optimization (constraints + goals →
multiple design options).
● Rhino + Grasshopper + ML plugins – parametric + AI-driven generative architecture.
● Fusion 360 (Topology Optimization) – CAD tool with AI-suggested geometry
refinements.
● GANs for Fashion Patterns – deep generative networks for textile/pattern generation.
● CLO 3D – virtual prototyping/simulation of garments.
🔹 Critical Issues in Human–AI Teams
● Explainable AI (XAI) – algorithms and methods that make AI decision processes
interpretable.
● CAST (Coordinated Awareness of Situation by Teams) – human + AI coordination
model for stable performance.
Lecture 5: AI in Concept Development
🔹 Image Generators
● GANs (Generative Adversarial Networks) – realistic but novel visuals, e.g., chair
design datasets.
● Diffusion Models – denoising-based image generation.
● GAWWN (Generative Adversarial What-Where Network) – generates images from
text + spatial inputs.
● Neural Style Transfer (CNN-based) – blend content of one image with style of another
(Gatys et al., 2016).
🔹 Advanced GAN Applications
● Style Transfer for Aesthetics – modify artistic style of products.
● Morphing / Interpolation – blend between two reference images to explore design
variants.
🔹 Sketching Methods
● Sketch-RNN (Ha & Eck, 2018) – RNN-based model to complete hand-drawn sketches.
● CollabDraw (Fan et al., 2019) – alternating human–AI sketch contributions in a web
tool.
● Drawing Apprentice (Davis et al., 2016) – AI understands semantic meaning of
sketches, complements user input.
🔹 3D Model Generators/Modifiers
● Autodesk Dreamcatcher – generative 3D modeling with optimization.
● Sketch2Render (Vizcom) – converts sketches into realistic renders.
🔹 Facilitator Functions
● Adobe Sensei Subject Select Tool – AI-based image segmentation/selection.
● Concept Evaluators (ML aesthetic scoring) – ML metrics for novelty, detail, and
feasibility.
● Sentient Sketchbook – game design tool suggesting map variations to trigger lateral
thinking.
Lecture 5 (Research & Data Mining)
🔹 Data Mining Concepts
● Data Mining – extracting patterns/knowledge from large datasets.
● Lead User Detection – identifying early adopters using mining methods.
● Preference Trend Mining (PTM) – analyzing shifts in user preferences over time.
🔹 AI in Customer Analysis
● Deep Learning for Aesthetics – predicting perception of design features (e.g., Range
Rover case).
● Sentiment Analysis – text mining to capture emotional tone of user reviews.
🔹 AI Research Methods
● Conversational AI (Chatbots) – automate interviews, gather qualitative insights.
● Crowdsourcing + AI – combine diverse user inputs with AI summarization.
● Automated Trend Analysis – Google AI, Adobe Sensei.
🔹 Data Mining Tools
● RapidMiner – drag-and-drop ML/data workflows.
● Weka – Java-based open-source ML toolkit.
● Orange – interactive data mining and visualization.
● KNIME – modular platform for analytics and reporting.
● Google Cloud AI analytics – large-scale cloud ML analysis.
● AWS AI analytics – Amazon’s ML services for predictive insights.
✅ That’s Lectures 4–5 covered (Collaboration + Concept Development + Research).
Do you want me to continue with Part 3 (Lectures 7–8: Data & Pattern Analysis I & II)?
Perfect 🙌 Let’s wrap up with Part 3 (Lectures 7–8: Data & Pattern Analysis I & II).
📘 Algorithms & Technical Methods
(Lecture Notes – Part 3)
Lecture 7: Design Data & Pattern Analysis – I
🔹 Pattern Recognition Basics
● Pattern Recognition – assigning categories/labels to data using statistical or ML
methods.
● Applications: classification, clustering, anomaly detection.
🔹 Clustering Algorithms
● K-Means Clustering
○ Centroid-based unsupervised method.
○ Steps: Choose k → Initialize centroids → Assign points → Update centroids →
Iterate.
○ Pros: simple, scalable.
○ Cons: sensitive to initialization, assumes spherical clusters.
○ Improvements: K-Means++ initialization.
● Hierarchical Clustering
○ Builds a tree (dendrogram) of clusters.
○ Agglomerative (bottom-up): merge closest points iteratively.
○ Divisive (top-down): split until individual clusters.
○ Linkage methods: single-link, complete-link, average-link, Ward’s method.
○ Pros: no need for pre-defined k, multilevel insights.
○ Cons: computationally expensive, sensitive to noise.
🔹 Dimensionality Reduction
● PCA (Principal Component Analysis)
○ Linear method capturing max variance with fewer dimensions.
● LDA (Linear Discriminant Analysis)
○ Supervised method for maximizing class separability.
● t-SNE (t-distributed Stochastic Neighbor Embedding)
○ Nonlinear, preserves local neighborhoods for visualization (2D/3D).
● UMAP (Uniform Manifold Approximation & Projection)
○ Topology-based, faster than t-SNE, preserves global + local structure.
🔹 Feature Extraction
● Converts raw data into informative features.
● Benefits: reduced computation, better model performance, avoids overfitting,
interpretable insights.
Lecture 8: Design Data & Pattern Analysis – II
🔹 Probabilistic Approaches
● Bayesian Models – incorporate uncertainty into design decisions.
● Semi-Supervised Learning – small labeled set + large unlabeled set.
🔹 Data Pipeline Patterns
● Batch Processing – process in scheduled chunks (e.g., daily).
● Stream Processing – real-time data handling.
● Lambda Architecture – combines batch (accuracy) + stream (speed).
● Kappa Architecture – unified streaming (real-time + historical via logs).
● ETL (Extract-Transform-Load) – transform before loading.
● ELT (Extract-Load-Transform) – transform after loading in storage.
● Polyglot Storage – use multiple DBs (SQL + NoSQL) depending on data type.
🔹 Streaming Patterns
● Raw Data Streaming – process unstructured sensor/IoT data directly.
● Event Streaming – structured events (e.g., e-commerce user actions).
● Complex Event Processing (CEP) – detect relationships across multiple data streams
(e.g., fraud detection).
● Stateless Streaming – no memory of past events, handles independent tasks.
● Stateful Streaming – retains context, enables session-based analysis.
🔹 Transformation Patterns
● Data Cleaning – handle missing, noisy, imbalanced data.
● Feature Engineering on-the-fly – just-in-time transformations.
🔹 ML Pitfall Solutions via Patterns
● Oversampling – fix data imbalance.
● Context-Aware Imputation – fill missing values intelligently.
● Data Leakage Prevention – separate test/train data.
● Strategy Pattern – flexible algorithm selection.
● Explainability Tools (XAI) – improve model interpretability.
🔹 Visual Pattern Recognition
● CNNs for Style Detection – auto-tagging images, detecting design/layout styles.
● Applications: branding, UI layout, composition, typography.
🔹 Tools for Pattern Analysis
● Python ML libraries – Scikit-learn, TensorFlow, PyTorch.
● Visualization – Tableau, PowerBI.
● Cloud Pipelines – GCP, AWS ML pipelines.
👍
Got it Your Lecture 10 (Market Trend Analysis using NLP) covers a set of algorithms and
methods specific to Natural Language Processing (NLP).
Here are the structured notes:
📘 Algorithms & Technical Methods
(Lecture 10: Market Trend Analysis using
NLP)
Text Preprocessing
● Tokenization – splitting text into words/phrases.
● Stopword Removal – removing common words (e.g., "is", "the") with little value.
● Lemmatization – reducing words to base form (e.g., "running" → "run").
● Stemming – crude chopping of suffixes (e.g., "connection" → "connect").
● Cleaning – remove URLs, emojis, punctuation, special characters.
Feature Extraction Methods
● Bag-of-Words (BoW) – word counts, ignores order/context.
● TF-IDF (Term Frequency – Inverse Document Frequency)
○ Weights words by frequency in a document vs rarity in the corpus.
● Word Embeddings
○ Word2Vec (CBOW & Skip-gram) – learns vector representation from context.
■ CBOW (Continuous Bag of Words): predicts target word from
neighbors.
■ Skip-gram: predicts context from target word.
○ GloVe – Global Vectors for word representation, trained on co-occurrence matrix.
○ FastText – embeddings using subword information.
○ Contextual Embeddings (BERT, GPT) – embeddings vary by sentence context.
Sentiment Analysis
● Classifies text as positive, negative, or neutral.
● Useful for consumer attitude analysis (product reviews, social media).
Topic Modeling
● Unsupervised methods to find latent themes in text.
● LDA (Latent Dirichlet Allocation) – probabilistic model of topics.
● NMF (Non-Negative Matrix Factorization) – factorizes document-term matrix.
Trend Detection using NLP
● Keyword/phrase frequency tracking.
● Sentiment shift monitoring over time.
● Pattern recognition across multiple platforms (Twitter, Instagram, Amazon reviews).
Visualization Methods
● Word Clouds – visual frequency representation.
● Time Series Graphs – sentiment or keyword trend over time.
● Network Graphs – topic/keyword co-occurrence relationships.
Case Study Methods
● Fashion Industry – NLP on Instagram hashtags → seasonal trends, influencers.
● Product Design – NLP on reviews → complaints, desired features, improvement
prioritization.
Tools & Platforms
● Python NLP libraries: NLTK, spaCy, gensim, scikit-learn.
● Deep Learning Frameworks: TensorFlow, PyTorch.
● Visualization Tools: Tableau, Power BI, matplotlib.
● Cloud NLP Services: AWS Comprehend, Google Cloud NLP, Azure Text Analytics.
Limitations
● Sarcasm & humor detection is weak.
● Contextual ambiguity.
● New slang and evolving language.
● Deep models require high computational resources.
✅ That’s a complete algorithm/method overview for Lecture 10.
