🎓How I Study AIHISA
đź“–Read
📄Papers📰Blogs🎬Courses
đź’ˇLearn
🛤️Paths📚Topics💡Concepts🎴Shorts
🎯Practice
⏱️Coach🧩Problems🧠Thinking🎯Prompts🧠Review
SearchSettings
Stanford CS329H: Machine Learning from Human Preferences | Autumn 2024 | Human-centered Design | How I Study AI
đź“š Stanford CS329H: Machine Learning from Human Preferences5 / 8
PrevNext
Stanford CS329H: Machine Learning from Human Preferences | Autumn 2024 | Human-centered Design
Watch on YouTube

Stanford CS329H: Machine Learning from Human Preferences | Autumn 2024 | Human-centered Design

Beginner
Stanford
Machine LearningYouTube

Key Summary

  • •Machine learning means building computer systems that learn patterns from data to make predictions or decisions. Instead of hand-coding rules like “if you see whiskers and triangle ears, it’s a cat,” we feed many labeled examples and let algorithms learn the rules. The goal is to generalize from past data to new, unseen cases in the future.
  • •It is not just writing code, doing data analysis, or only statistics. You still program, explore data, and use statistics, but ML combines all of these with algorithms and system building. It is also not only deep learning; deep learning is one branch among many ML methods.
  • •Common real-world uses include personalization on Amazon, Netflix, and YouTube. Search engines like Google and Bing rank pages using ML, and email providers filter spam automatically. Banks use ML for fraud alerts, doctors use it to read X-rays, and self-driving cars use it to detect people, cars, and traffic lights.
  • •Core example problems include image classification (what’s in a picture?), sentiment analysis (is text positive or negative?), machine translation (turn text from one language to another), speech recognition (turn voice into text), and recommendation (suggest items a user may like). These examples show both different input types (images, text, audio) and output types (labels, text). Together, they map the main areas where ML shines.
  • •The class aims to help you pick algorithms, apply them to data, debug results, and judge when ML is the right tool. You’ll also learn how to read research papers and understand current ML advances. This means becoming confident at both practice and understanding theory at a useful level.
  • •Grading is 40% homeworks, 30% midterm, and 30% final project. Homeworks apply class algorithms to real-world datasets using Python with NumPy, SciPy, and scikit-learn. The midterm mixes multiple choice and short answers on concepts; the final project applies ML to a problem you choose, with a 6–8 page report and a short presentation.

Why This Lecture Matters

This lecture gives beginners a practical, end-to-end view of machine learning that maps directly to real work. Product managers, data analysts, junior engineers, and researchers can all benefit from knowing how to turn raw data into useful predictions and decisions. The content shows exactly where ML improves products—personalization in e-commerce and streaming, search ranking on the web, spam filtering in email, fraud alerts for payments, medical imaging assistance, and self-driving car perception—so you can connect the dots from idea to implementation. By learning to choose algorithms, work with Python libraries like NumPy, SciPy, and scikit-learn, and debug results, you gain skills that make you productive quickly on real datasets. Clear policies and project guidelines teach teamwork, planning, and honest scholarship, which matter in both school and industry. This knowledge solves common problems like information overload (recommendations), safety and trust (fraud detection, spam filtering), and access (translation, speech-to-text). You can apply it by running small experiments: split data, train a baseline, measure properly, and iterate. Over time, you’ll learn when to use simpler models and when to scale up, which saves time and resources. Career-wise, these are foundational skills for data science and ML engineering roles, and they prepare you to understand and adopt new research ideas. In today’s industry, where almost every product uses data to personalize, prioritize, or automate decisions, understanding ML’s core loop is essential for building smart, trustworthy systems.

Lecture Summary

Tap terms for definitions

01Overview

This session introduces the foundations of machine learning (ML) and sets clear expectations for what you will learn and how the course will run. The central idea is simple but powerful: instead of writing a long list of rules by hand, we let computers learn patterns from data and then use those patterns to make predictions or decisions. Think of the classic “detect a cat” problem. In the old way, you might code rules for ears, whiskers, and fur color. In ML, you feed many pictures labeled “cat” and “not cat,” and the system learns the patterns that separate the two classes. The lecture defines ML as building systems that learn from data using computation to make predictions or decisions. It highlights the practice of actually building useful systems, not just studying math or code in isolation. It also underlines the predictive nature: we learn from past data to guess the future.

This lecture is for beginners who want to gain practical ML skills: choosing algorithms, applying them to real data, debugging results, and judging when ML is appropriate. You will write Python code and use standard libraries like NumPy, SciPy, and scikit-learn, so a basic comfort with Python is recommended or should be developed early. While statistics, programming, and data analysis all matter, ML combines them into a coherent craft of building predictive systems. The class also aims to help you read research papers, so you can follow the latest developments and understand what new ideas mean in practice.

After this lecture, you should be able to explain what ML is and is not. You will recognize core real-world use cases such as personalization (Amazon, Netflix, YouTube), search ranking (Google, Bing), spam filtering (email), fraud detection (credit cards), medical imaging assistance (X-rays), and self-driving perception (detecting pedestrians, other vehicles, and traffic lights). You will understand example problem types across data modalities: image classification (image in, label out), sentiment analysis (text in, sentiment out), machine translation (text in one language, text out in another), speech recognition (audio in, text out), and recommender systems (user history in, ranked items out). You will also know exactly how the course is graded, how to form teams, what tools to use, and what the project expectations are.

The lecture is structured in four parts. First, it defines ML and clarifies what it includes and excludes. Second, it surveys everyday use cases, showing how ML shows up in products you already use. Third, it walks through representative problem types to ground the definition with concrete tasks and outputs. Fourth, it explains the course logistics: grading breakdown, homework schedule and tools, project expectations, late-day policy, and academic integrity. The lecture closes with a short Q&A on Python resources, suggesting Codecademy, “Automate the Boring Stuff with Python,” and plentiful online tutorials. The core message is consistent: ML is about building systems that learn from data to make predictions or decisions, and you will practice doing exactly that throughout the course.

Key Takeaways

  • âś“Start from the problem, not the model. Write a one-line goal that names the input, output, and success measure, so every later choice stays aligned. If you can’t define this cleanly, your data and model choices will likely miss the mark. A clear target makes feature prep, algorithm choice, and evaluation much easier.
  • âś“Learning from data beats hand-written rules for complex tasks. Use many labeled examples to let the model discover patterns you can’t easily code. This approach scales to messy, changing inputs like emails, clicks, and photos. It also adapts faster than a manual rulebook.
  • âś“Use the simplest baseline that could work. A quick logistic regression or small tree can reveal whether the data is learnable and where errors occur. Baselines are fast to train and debug, and they set a reference for improvements. Don’t jump to complex models before you’ve established a baseline.
  • âś“Split your data into train, validation, and test, and respect the test set. Tune on validation, and evaluate once on the test to see true generalization. Mixing these sets leads to overly optimistic results. Keep records of how each split was made.
  • âś“Pick metrics that reflect real goals. For spam, balance precision and recall so you neither miss spam nor hide real mail. For recommendations and search, use ranking metrics that mirror user experience. Wrong metrics push you toward wrong models.
  • âś“Iterate with discipline and logs. Change one thing at a time—features, model, or hyperparameters—and write down results. This makes progress measurable and reversible. You’ll learn faster and avoid confusion.
  • âś“

Glossary

Machine Learning

A way to make computers learn patterns from data to make predictions or decisions. Instead of writing every rule by hand, we show many examples and let the system figure out the rules. It works best when there’s too much complexity for manual coding. The goal is to do well on new, unseen cases.

Data

Information we feed into a computer to learn from, such as images, text, audio, or numbers. Good data represents the problem well, including common and rare cases. Clean and accurate data gives better models. Biased or messy data hurts results.

Algorithm

A step-by-step method a computer uses to solve a problem. In ML, algorithms learn from data by changing internal settings to reduce mistakes. Different algorithms work better for different tasks. Choosing the right one matters.

Prediction

A model’s guess about the future or about a new input it has not seen before. Predictions often come with a confidence score. They help systems act smartly. The better the prediction, the better the decision.

Decision

An action taken by a system using predictions and rules. It connects model output to real outcomes. Decisions often involve thresholds and trade-offs. Good decisions align with business or user goals.

#machine learning#learning from data#prediction#decision making#personalization#search ranking#spam filtering#fraud detection#medical imaging#self-driving cars#image classification#sentiment analysis#machine translation#speech recognition#recommendation systems#python#numpy#scikit-learn
Version: 1
  • •Homework logistics: four equally weighted assignments, due Fridays at 5 PM, submitted via Gradescope. You may work in groups of up to three and submit one solution per group. Code must be written in Python, and familiarity with NumPy, SciPy, and scikit-learn is strongly recommended.
  • •Final project logistics: due the last day of class and submitted via Gradescope. You can work solo or in teams of up to three and will present for 5–10 minutes. Your 6–8 page report should explain your problem, data, method, results, and lessons learned.
  • •Policy highlights: you have three late days total, not usable on the midterm, and at most one late day per assignment. Submissions more than three days late get a zero. Academic integrity is required: no cheating, no plagiarism, and no collaboration outside your group.
  • •To get started with Python, use resources like Codecademy and the book “Automate the Boring Stuff with Python.” Many free tutorials on YouTube and other sites can help you ramp up. Practice by solving small tasks and then moving to ML libraries like scikit-learn.
  • •The big idea is learning from data to predict the future. We replace manual rule-writing with data-driven learning so systems can handle complexity and scale. This approach powers the smart tools you use every day.

    • 02Key Concepts

    • 01

      🎯 Machine Learning Defined: ML builds systems that learn from data using computation to make predictions or decisions. 🏠 It’s like teaching by example instead of writing a giant rulebook. 🔧 You collect labeled examples, pick an algorithm, and train it so it discovers patterns that map inputs to outputs. 💡 Without ML, hand-coding rules becomes impossible for complex, messy, and changing data. 📝 A cat vs. not-cat image classifier learns visual patterns from many labeled images instead of relying on hand-written rules.

    • 02

      🎯 Learning from Data: The core shift is from writing rules to learning rules. 🏠 It’s like showing a child many pictures of cats and non-cats until they “get it.” 🔧 The system uses large datasets with labels to adjust internal parameters that minimize mistakes on training examples. 💡 This scales better than manual rules and adapts to complex patterns humans can’t easily encode. 📝 Provide thousands of cat and non-cat photos, and the model learns to predict the correct label for new photos.

    • 03

      🎯 Predictions and Decisions: ML uses past data to forecast future outcomes or choose actions. 🏠 It’s like using yesterday’s weather to decide whether to bring an umbrella tomorrow. 🔧 Models estimate probabilities or outputs for new inputs based on what they learned, then select a decision (e.g., show or not show an ad). 💡 Without predictive power, systems can’t personalize or react smartly. 📝 Click histories help predict the next ad a user is likely to click.

    • 04

      🎯 ML Is Not Just Coding: You still write code, but the code is for learning from data, not crafting every rule. 🏠 It’s like building a machine that can figure out its own playbook. 🔧 You implement data loading, model training, evaluation, and deployment loops that let the model learn. 💡 Purely manual code can’t keep up with variation and complexity in real data. 📝 Instead of writing a “spam rule” list, you train a spam detector from many labeled emails.

    • 05

      🎯 ML Is Not Just Data Analysis: Data analysis describes what happened; ML predicts what will happen. 🏠 It’s like looking at your past report card (analysis) versus predicting next semester’s grades (ML). 🔧 Analysts compute summaries; ML systems fit models that generalize from patterns to new cases. 💡 Without prediction, you cannot power recommendations or alerts. 📝 Beyond charting email counts, a spam model predicts if the next email is spam.

    • 06

      🎯 ML Is Not Just Statistics: Statistics is vital but incomplete for ML’s system-building needs. 🏠 It’s like knowing all the ingredients but also needing a working kitchen. 🔧 ML blends statistics with algorithms, programming, and engineering to train, serve, and monitor models. 💡 Without the computational and systems part, models don’t run at scale in real products. 📝 An email spam filter must be trained, deployed, updated, and measured continuously.

    • 07

      🎯 ML Is Not Only Deep Learning: Deep learning is a popular subfield but not the whole story. 🏠 It’s like sports: basketball is big, but there are many sports. 🔧 Some tasks may use simpler models like logistic regression or trees, which can be faster and easier to debug. 💡 Using the right tool matters more than using the trendiest tool. 📝 A small text sentiment task might work well with a classic linear model.

    • 08

      🎯 Personalization: Systems tailor content based on your history and preferences. 🏠 It’s like a librarian who remembers what you liked and recommends similar books. 🔧 Models learn from past clicks, views, and purchases to rank items you’re likely to engage with next. 💡 Without personalization, users face overload and irrelevant options. 📝 Amazon, Netflix, and YouTube recommend products, movies, or videos you’ll probably enjoy.

    • 09

      🎯 Search Ranking: ML decides which results to show and in what order. 🏠 It’s like a smart guide picking the most useful pages for your question. 🔧 Features from the query, page content, and user behavior feed a model that scores pages for relevance. 💡 Good ranking saves time and improves satisfaction. 📝 Google and Bing use ML to place the best answers at the top.

    • 10

      🎯 Spam Filtering: ML separates unwanted emails from real messages. 🏠 It’s like a bouncer who recognizes troublemakers by patterns. 🔧 From subject lines, sender info, and text cues, models learn what looks spammy versus normal. 💡 Without filters, inboxes get flooded and important mail gets lost. 📝 Gmail uses ML to route spam away automatically.

    • 11

      🎯 Fraud Detection: Systems flag suspicious transactions based on patterns in purchase behavior. 🏠 It’s like a guard dog that notices when something “doesn’t smell right.” 🔧 Models compare new transactions to your history and known fraud signals to estimate risk. 💡 Without this, fraud losses rise and customers lose trust. 📝 A card company may call to verify a purchase when the model’s risk score is high.

    • 12

      🎯 Medical Imaging Assistance: ML helps read scans like X-rays to spot problems. 🏠 It’s like an extra pair of sharp eyes for the doctor. 🔧 Trained on many labeled images, the model highlights regions that may indicate disease or injury. 💡 This supports faster, more accurate care. 📝 A system may flag a spot that looks like a fracture or early cancer.

    • 13

      🎯 Self-Driving Perception: ML detects people, cars, and signals in real time. 🏠 It’s like giving the car eyes and a brain to interpret the road. 🔧 Vision models process camera and sensor data frame by frame to identify objects and their positions. 💡 Without robust perception, autonomy is unsafe. 📝 The system must recognize pedestrians and traffic lights reliably.

    • 14

      🎯 Image Classification: Assign a label to an image from a set of classes. 🏠 It’s like sorting photos into the right album. 🔧 The model learns visual features that distinguish classes and predicts the best label for new images. 💡 This enables content tagging and safety filters. 📝 “Cat” vs. “dog” is a classic starter task.

    • 15

      🎯 Sentiment Analysis: Decide if text expresses positive, negative, or neutral feelings. 🏠 It’s like sensing someone’s mood from their message. 🔧 The system learns word patterns and phrases tied to sentiment polarity. 💡 Companies track feedback at scale this way. 📝 “This movie was great!” → positive; “This movie was terrible!” → negative.

    • 16

      🎯 Machine Translation: Convert text from one language to another. 🏠 It’s like having a bilingual friend who rephrases for you. 🔧 Models learn mappings between languages so meaning stays intact across grammar and vocabulary. 💡 This breaks language barriers online. 📝 “Hello, how are you?” → “Hola, ¿cómo estás?”

    • 17

      🎯 Speech Recognition: Turn spoken audio into text. 🏠 It’s like a fast typist who listens and writes. 🔧 The system maps sound patterns to phonemes and words learned from many speech examples. 💡 This powers assistants and transcription. 📝 Microphone input becomes readable text.

    • 18

      🎯 Recommendation Systems: Suggest items a user is likely to enjoy next. 🏠 It’s like a shopkeeper who knows your tastes. 🔧 Models use user histories and item similarities to rank and present a short, relevant list. 💡 Good recommendations boost engagement and reduce choice overload. 📝 A user who watched sci-fi films gets new sci-fi suggestions.

    • 19

      🎯 Course Logistics: Clear structure enables consistent learning and fair evaluation. 🏠 It’s like a road map showing where you’ll go and how you’ll be graded. 🔧 Homeworks (40%) apply algorithms in Python; the midterm (30%) checks concepts; the final project (30%) solves a problem you choose with a 6–8 page report and short talk. 💡 Rules on late days and integrity keep things fair and on time. 📝 Submit via Gradescope, work in groups up to three, and use NumPy, SciPy, and scikit-learn.

    03Technical Details

    Overall Architecture/Structure of an ML Project

    1. Problem framing (what decision or prediction?)
    • 🎯 Definition: Clarify the input, the desired output, and the business or user goal.
    • 🏠 Analogy: It’s like deciding what kind of map you need before a trip: a city map (classification) or a highway map (regression or ranking).
    • 🔧 Technical: Define input modality (image, text, audio), output type (label, score, text), and success metric (e.g., accuracy for classification, relevance for ranking). Scope the decision latency (real time vs. batch) and constraints (privacy, compute, cost).
    • đź’ˇ Why it matters: A fuzzy goal leads to mismatched data, wrong algorithms, and unclear results.
    • đź“ť Example: “Given a product page view (input), predict the top five related products to show (output), to improve click-through rate (goal).”
    1. Data collection and labeling
    • 🎯 Definition: Gather examples that represent the problem and attach correct labels when needed.
    • 🏠 Analogy: Like building a study guide from many past exams with the correct answers attached.
    • 🔧 Technical: Collect historical logs, public datasets, or human annotations. Ensure coverage of common and rare cases. Store examples with metadata (time, source) to track drift later.
    • đź’ˇ Why it matters: Poor or biased data breaks the model’s ability to generalize, no matter how fancy the algorithm.
    • đź“ť Example: For spam filtering, compile a dataset of emails labeled spam/ham from real inbox traffic (with privacy safeguards).
    1. Feature preparation or representation
    • 🎯 Definition: Turn raw inputs into forms models can use (pixels, tokenized text, spectrograms).
    • 🏠 Analogy: Like converting ingredients into a recipe-ready state (chopped, measured, mixed).
    • 🔧 Technical: Images may be resized and normalized; text is tokenized; audio is turned into frames. For classical ML, engineer features (e.g., word counts), while many modern models learn features automatically.
    • đź’ˇ Why it matters: Bad representations hide useful patterns and hurt accuracy.
    • đź“ť Example: Sentiment analysis might use bag-of-words or embeddings as inputs to a classifier.
    1. Model selection
    • 🎯 Definition: Choose an algorithm that fits the task, data size, and constraints.
    • 🏠 Analogy: Picking the right tool from a toolbox: you don’t use a sledgehammer for a thumbtack.
    • 🔧 Technical: For small text tasks, a linear classifier can work well; for large images, convolutional networks are common; for recommendations, factorization or neural ranking may fit.
    • đź’ˇ Why it matters: The “right-sized” model balances performance, speed, and ease of debugging.
    • đź“ť Example: Start an email spam filter with logistic regression over TF-IDF features, then iterate.
    1. Training
    • 🎯 Definition: Fit the model to data so it learns patterns that map inputs to outputs.
    • 🏠 Analogy: It’s like practicing many problems until you get the hang of them.
    • 🔧 Technical: Split data into train/validation/test sets; optimize model parameters to minimize loss on the training set while monitoring validation performance to avoid overfitting.
    • đź’ˇ Why it matters: Training teaches the model; validation ensures it’s learning generalizable patterns, not just memorizing.
    • đź“ť Example: Train an image classifier on labeled pictures; stop when validation accuracy plateaus.
    1. Evaluation
    • 🎯 Definition: Measure how well the model performs on unseen data in a way that reflects real goals.
    • 🏠 Analogy: Like a final exam that checks true understanding, not memorization.
    • 🔧 Technical: Use held-out test sets and metrics that match the task (accuracy for classification; precision/recall for skewed classes; ranking metrics for search/recommendations). Consider latency and resource use.
    • đź’ˇ Why it matters: The wrong metric can point you to the wrong “best” model.
    • đź“ť Example: For spam, track precision (few false alarms) and recall (catch most spam) together.
    1. Deployment and monitoring
    • 🎯 Definition: Put the model into the product and keep watch as real data evolves.
    • 🏠 Analogy: Like launching a ship and then constantly checking the weather and instruments.
    • 🔧 Technical: Serve the model behind an API, collect feedback loops, monitor input shifts and performance drops, and retrain as needed.
    • đź’ˇ Why it matters: Data changes over time; a good model today can degrade tomorrow.
    • đź“ť Example: A fraud model sees new scam patterns; weekly retraining helps catch them.

    How the Concepts Map to Lecture Examples

    Personalization (Amazon/Netflix/YouTube)

    • Inputs: user history (clicks, views, purchases), item metadata (genres, categories), and context (time, device).
    • Model: Recommendation systems that rank items by predicted engagement.
    • Output: Top-N recommended items personalized to each user.
    • Deployment: Real-time ranking on page load; monitor clicks and watch time as feedback.

    Search Ranking (Google/Bing)

    • Inputs: the search query, page content features, link structure, and past behavior signals.
    • Model: Learning-to-rank algorithms that score candidate pages.
    • Output: Ordered list of results; quality measured by relevance metrics and click behavior.
    • Deployment: Must be fast, scalable, and safe; constant A/B testing.

    Spam Filtering (Email)

    • Inputs: email header fields, sender reputation, text content, links.
    • Model: Classifier that outputs spam vs. ham with a confidence score.
    • Output: Route to inbox, spam folder, or flag for review.
    • Constraints: High precision is critical to avoid losing real messages.

    Fraud Detection (Credit Cards)

    • Inputs: transaction amount, merchant type, location, time, user profile.
    • Model: Anomaly detection or supervised classifier trained on confirmed fraud labels.
    • Output: Risk score and possible action (approve, flag, or call the user).
    • Monitoring: Adapt quickly to new fraud tactics and seasonality.

    Medical Imaging (X-rays)

    • Inputs: patient scan images and labels from radiologists.
    • Model: Image classifier or detector that highlights suspicious regions.
    • Output: Decision support for clinicians, not a sole diagnosis.
    • Safety: Requires careful validation and regulatory considerations.

    Self-Driving Perception

    • Inputs: camera, lidar, radar streams; synchronized and calibrated.
    • Model: Object detection and segmentation models running in real time.
    • Output: Detected objects with positions and classes; feeds planning and control.
    • Constraints: Extremely low latency and high reliability under varied conditions.

    Representative Problem Types

    1. Image Classification
    • Input: image array; Output: class label.
    • Typical steps: preprocess (resize/normalize), train a classifier, evaluate accuracy.
    • Uses: content moderation, photo search, scene understanding.
    1. Sentiment Analysis
    • Input: text string; Output: sentiment label (positive/negative/neutral).
    • Typical steps: tokenize text, represent (bag-of-words, embeddings), train a classifier.
    • Uses: customer feedback mining, brand monitoring.
    1. Machine Translation
    • Input: source-language text; Output: target-language text.
    • Typical steps: learn mappings between languages; ensure meaning preservation.
    • Uses: cross-language communication and content access.
    1. Speech Recognition
    • Input: audio waveform; Output: transcribed text.
    • Typical steps: feature extraction from audio, sequence modeling, decoding.
    • Uses: assistants, dictation, subtitles.
    1. Recommendation
    • Input: user-item interaction history; Output: ranked list of items.
    • Typical steps: model user preferences and item features; rank candidates.
    • Uses: e-commerce, media platforms, news feeds.

    Tools/Libraries Used in the Course

    • Python: The primary programming language for assignments and projects. It is beginner-friendly and has rich ML libraries.
    • NumPy: Provides fast arrays and numerical operations; used for data manipulation and linear algebra.
    • SciPy: Adds scientific computing utilities, optimization, and signal processing routines that often support ML tasks.
    • scikit-learn: A go-to library for classical ML with consistent APIs for preprocessing, model training, and evaluation; ideal for classification, regression, and simple pipelines.
    • Gradescope: The submission platform for homeworks and the final project; supports group submissions and structured grading.

    Step-by-Step Implementation Guide (Beginner-Friendly)

    A. Set up your environment

    1. Install Python 3.x and create a virtual environment.
    2. Install NumPy, SciPy, and scikit-learn (e.g., pip install numpy scipy scikit-learn).
    3. Verify imports in a Python shell.

    B. Load and inspect data

    1. Read your dataset (CSV for text/structured; image folders for vision; audio files for speech).
    2. Print shapes, class counts, and sample rows or images to understand what you have.
    3. Check for missing values and unusual outliers that might break training.

    C. Split data

    1. Create train/validation/test splits so you can measure generalization.
    2. Keep the test set untouched until final evaluation.
    3. Use the validation set to choose models and tune thresholds.

    D. Preprocess inputs

    1. Text: lowercase, tokenize, maybe remove punctuation; create features (e.g., TF-IDF) or use embeddings.
    2. Images: resize to a standard shape, normalize pixel values; optionally augment training data.
    3. Audio: trim or segment audio, compute time-frequency representations.

    E. Choose a baseline model

    1. For classification, try logistic regression or a simple tree-based model in scikit-learn.
    2. Train quickly and measure validation performance to get a reference point.
    3. Keep the baseline code clean so you can reuse it for new features or models.

    F. Train and iterate

    1. Adjust features or hyperparameters to improve validation performance.
    2. Watch for overfitting: training accuracy high but validation accuracy low signals a problem.
    3. Save the best model and document what changed each iteration.

    G. Evaluate and report

    1. Compute relevant metrics on the test set.
    2. Create simple plots or tables to summarize results.
    3. Write up a short explanation of what worked, what didn’t, and next steps.

    H. Deploy (project-oriented)

    1. Wrap the model in a function or API for easy use.
    2. Log predictions and feedback for future improvement.
    3. Schedule periodic retraining if data changes.

    Tips and Warnings

    • Start simple: A strong baseline beats a fancy but fragile approach.
    • Match metrics to goals: For spam, balance precision and recall; for recommendations, focus on ranking quality.
    • Data quality > model complexity: Clean, representative data often increases accuracy more than switching algorithms.
    • Keep a log: Track experiments so you can reproduce improvements and avoid repeating mistakes.
    • Respect constraints: Some tasks need real-time speed; others can run in batch mode overnight.
    • Group work: Split tasks by strengths—data prep, modeling, evaluation, write-up—and integrate early.
    • Academic integrity: Write your own code within your group, cite sources, and never copy solutions.

    Course Logistics (From the Lecture)

    • Grading breakdown: 40% homeworks (four, equally weighted, real-world datasets, Python/NumPy/SciPy/scikit-learn), 30% midterm (multiple choice + short answer on concepts), 30% final project (pick any ML problem, 6–8 page report, 5–10 minute presentation).
    • Homework rules: Due Fridays at 5:00 PM via Gradescope; groups up to three; one submission per group; code in Python.
    • Final project: Due last day of class; submit via Gradescope; groups up to three; present to class; write a 6–8 page report.
    • Late policy: Three total late days across the quarter; can’t use on midterm; max one late day per assignment; more than three days late earns zero.
    • Academic integrity: No cheating, no plagiarism, and no collaboration outside your group; ask questions if unsure.
    • Python resources: Codecademy, “Automate the Boring Stuff with Python,” and many beginner-friendly tutorials on YouTube and beyond.

    Why This Setup Works

    • Aligns with the definition: You’ll build systems that learn from data to make predictions or decisions.
    • Emphasizes practice: Homeworks and the final project force you to pick algorithms, apply them, and debug—exactly what ML engineers do.
    • Builds independence: By the end, you can choose when ML is appropriate and select efficient solutions, even if not deep learning.
    • Encourages growth: Reading research and presenting your project prepares you to engage with the broader ML community.

    In sum, the technical backbone is a repeatable loop—frame the problem, gather and prepare data, pick and train a model, evaluate properly, deploy carefully, and keep monitoring. The lecture illustrates this loop with familiar applications: recommendations, search, spam, fraud, health imaging, and autonomous driving. It rounds out with concrete course logistics so you can plan your time and tools, and it points to accessible Python resources so you can get productive quickly.

    04Examples

    • đź’ˇ

      Cat Detector Example: Input is many images labeled “cat” or “not cat.” The model processes pixel values and learns patterns that separate the two groups. Output is a label for a new image, with a confidence score. The key lesson is that you no longer hand-code rules for ears and whiskers; the model learns them from data.

    • đź’ˇ

      Ad Click Prediction: Input includes a user’s past ad clicks and context like time or device. The model estimates the probability the user will click the next ad shown. Output is a score used to decide whether to show an ad or which ad to show. The key point is using past behavior to predict future actions.

    • đź’ˇ

      Product Purchase Forecast: Input is a shopper’s past purchases and browsing patterns. The model predicts items the user is likely to buy next or soon. Output can be a list of recommended products or a purchase probability for each item. The key idea is personalization based on history.

    • đź’ˇ

      Loan Default Risk: Input includes credit history, income, and past payment behavior. The model outputs a probability of default for a new loan application. The decision threshold guides approvals or additional checks. The key teaching is that past financial signals help predict future risk.

    • đź’ˇ

      Personalization on Netflix: Input is watch history, ratings, and content metadata such as genre and actors. The model ranks movies or shows you are most likely to watch next. Output is a personalized homepage with rows tailored to your tastes. The emphasis is on reducing choice overload and improving engagement.

    • đź’ˇ

      Search Query Ranking: Input is the user query plus features from candidate pages like relevance to terms and user behavior signals. The model scores and orders results to place the most helpful at the top. Output is a ranked results page shown in milliseconds. The lesson is that ML helps sift the web efficiently.

    • đź’ˇ

      Spam Filtering in Email: Input is email content, headers, sender reputation, and link patterns. The model classifies messages as spam or not, often with a probability. Output routes messages to inbox or spam folder automatically. The key point is protecting users from junk without hiding important mail.

    • đź’ˇ

      Credit Card Fraud Alert: Input is a stream of transactions with amounts, locations, times, and merchant types. The model gives a risk score; high scores may trigger a verification call. Output is an action: approve, flag, or hold for review. The lesson is catching anomalies fast to protect users and reduce losses.

    • đź’ˇ

      X-ray Assistance: Input is medical images and associated labels from expert radiologists. The model highlights abnormal regions and predicts potential conditions. Output guides doctors with suggestions, not final diagnoses. The key idea is ML as decision support to improve speed and accuracy.

    • đź’ˇ

      Self-Driving Detection: Input is camera and sensor data from the car’s surroundings. The model detects pedestrians, vehicles, and traffic lights in real time. Output is a list of objects with positions used by planning systems. The lesson is that accurate perception is essential for safety.

    • đź’ˇ

      Image Classification Pipeline: Input is a dataset of images and labels. The process includes preprocessing, model training, and choosing the best checkpoint based on validation accuracy. Output is predicted labels for new images. The example shows the classic supervised learning loop in action.

    • đź’ˇ

      Sentiment Analysis Pipeline: Input is review text such as “This movie was great!” The process tokenizes text, represents it as features, and trains a classifier for polarity. Output is a label like positive or negative, possibly with a confidence. The key idea is learning mood patterns from word usage.

    • đź’ˇ

      Machine Translation Flow: Input is a sentence in the source language. The model learns to produce an equivalent sentence in the target language while preserving meaning. Output is the translated text. The example underscores converting one representation of meaning into another language’s form.

    • đź’ˇ

      Speech-to-Text Flow: Input is recorded audio speech. The system extracts features that represent sounds, maps them to words, and outputs text. Output is a transcript you can read or search. The focus is on turning a continuous sound signal into a sequence of words.

    • đź’ˇ

      Recommendation List Generation: Input is a user profile with past interactions. The model evaluates many candidate items and produces a top-N ranked list. Output is a personalized carousel on a page or app. The main lesson is ranking by predicted user interest.

    05Conclusion

    This lecture lays a clear foundation: machine learning builds systems that learn patterns from data to make predictions or decisions. The heart of ML is shifting from hand-written rules to learning from examples, which lets us handle complex, messy, and ever-changing real-world inputs. You saw where this matters most in daily life: personalization for products and media, search ranking on the web, spam filtering in email, fraud alerts for payments, medical imaging assistance in clinics, and perception for self-driving cars. You also saw the common problem types—image classification, sentiment analysis, machine translation, speech recognition, and recommendations—which together span images, text, and audio.

    Equally important, you learned what ML is not: it is not just writing code, not just data analysis, not just statistics, and not only deep learning. ML combines coding, data understanding, algorithms, and systems thinking to deliver useful predictions. The course is designed to make you fluent in this process: four hands-on homeworks in Python using NumPy, SciPy, and scikit-learn; a midterm focused on core concepts; and a final project where you apply ML to a problem you choose and present your results. Clear policies on late days and academic integrity keep the class fair and focused, and practical Python resources help you get up to speed quickly.

    To cement these ideas, try small end-to-end projects right away: build a simple text sentiment classifier, a basic image labeler, or a toy recommender from sample data. Practice splitting data, training a baseline, measuring performance, and iterating. As you grow, you can tackle richer tasks or larger datasets, but the core loop—frame, collect, represent, train, evaluate, deploy, monitor—stays the same. The core message to remember is simple and powerful: use data to learn, not hand-write rules, and always measure success on new, unseen cases. With that mindset and the course structure, you are set to become confident at solving real ML problems and at knowing when ML is the right tool for the job.

    Mind latency and scale from day one. Ranking search results or recommendations must be fast and consistent. Plan for real-time constraints or choose batch updates accordingly. A model that’s too slow won’t ship.
  • âś“Data quality dominates model choice. Fix label errors, balance classes if needed, and ensure coverage of edge cases. Cleaner data often improves results more than swapping algorithms. Invest in better data pipelines.
  • âś“Use Python’s core ML stack effectively. NumPy handles arrays, SciPy supports scientific routines, and scikit-learn provides solid classical models and preprocessing. Mastering these tools makes you productive on homework and projects. Practice on small tasks to build confidence.
  • âś“Know what ML is not. It’s not just code, not just data analysis, and not just statistics. It requires combining all three with systems thinking. It’s also not only deep learning—pick the right tool for the job.
  • âś“Design for monitoring and updates. Real-world data drifts, and models lose accuracy over time. Set up feedback loops, track input shifts, and plan retraining cadences. Healthy monitoring prevents silent failures.
  • âś“Work well in teams and follow integrity rules. Divide tasks by strengths, integrate early, and review each other’s code. Cite sources and keep collaboration within your group. Good teamwork and honesty make projects stronger.
  • âś“Start learning Python now if you’re new. Use Codecademy, “Automate the Boring Stuff with Python,” and short video tutorials. Practice basic scripting before jumping into ML libraries. Comfort with Python speeds every step of an ML project.
  • âś“Document your project thoroughly. In your 6–8 page report, explain the problem, data, method, results, and lessons learned. Clear writing helps others understand and also clarifies your own thinking. Good reports reflect real understanding.

    • Label

    The correct answer attached to an example in the training data. Labels teach the model what output is desired. They must be accurate and consistent. Bad labels confuse the model.

    Feature

    A measurable piece of information the model uses to learn. Features can be words counts, pixel values, or audio patterns. Good features make patterns easier to learn. Many modern models learn features automatically.

    Training

    The process of adjusting a model’s internal settings using data so it makes fewer mistakes. The model practices on many examples. It stops when improvement slows or validation results worsen. Good training leads to generalization.

    +22 more (click terms in content)