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Maths for ML 3: probability and optimization

  • about 13 h of lessonsحوالي 13 ساعة من الدروس
  • 9 modulesوحدات · 26 lessonsدروس
  • Englishالإنجليزية

Part 3 of 4, following the free book Mathematics for Machine Learning (Deisenroth, Faisal, Ong). Probability spaces, discrete and continuous distributions, the sum and product rules, Bayes' theorem, expectations and covariance, the Gaussian (marginals, conditionals, sums), conjugate priors and the exponential family, change of variables; then gradient descent, momentum and SGD, Lagrange multipliers, convexity and duality. Each lesson opens your own copy of the book (free from mml-book.com; inPace finds it), with notes, 3Blue1Brown videos, interactive explorers, worked exercises with instant feedback, and charts. Plan: 9 days.

byمن إعداد inPace checkedموثّق

Uses a book you download yourself, free:تستخدم كتابًا تحمّله بنفسك مجانًا: Mathematics for Machine Learning. mml-book.com inPace 1.20 or newer finds it on your device.يجده inPace 1.20 أو أحدث على جهازك.

mathmachine learninglinear algebramml book3blue1brownprobabilitystatisticsbayes

On these roadmaps:ضمن هذه المسارات: Maths for machine learning, Machine learning, from the maths up

What's insideمحتوى الدورة

Each module ends with tasks. The next module opens when they're done.تنتهي كل وحدة بمهام، وتُفتح الوحدة التالية عند إنجازها.

  1. Day 1: Probability spaces, discrete and continuous 5 lessonsدروس · 105 min د
    • Start here10 min د
    • Read: construction of a probability space (6.1)slidesشرائح35 min د
    • Read: discrete and continuous probabilities (6.2)slidesشرائح35 min د
    • Watch: Why "probability of 0" does not mean "impossible"YouTubeيوتيوب10 min د
    • Watch: Binomial distributionsYouTubeيوتيوب15 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة
  2. Day 2: Sum rule, product rule and Bayes' theorem 3 lessonsدروس · 71 min د
    • Read: sum rule, product rule and Bayes' theorem (6.3)slidesشرائح35 min د
    • Watch: Bayes theorem, the geometry of changing beliefsYouTubeيوتيوب15 min د
    • Watch: The medical test paradoxYouTubeيوتيوب21 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة
  3. Day 3: Means, variances and independence 2 lessonsدروس · 80 min د
    • Read: summary statistics and independence (6.4)slidesشرائح55 min د
    • Watch: Convolutions: adding random variablesYouTubeيوتيوب25 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة
  4. Day 4: The Gaussian distribution 6 lessonsدروس · 148 min د
    • Read: the Gaussian distribution (6.5)slidesشرائح60 min د
    • Explore: marginals and conditionals15 min د
    • Watch: But what is the Central Limit Theorem?YouTubeيوتيوب31 min د
    • See it: averages become Gaussian5 min د
    • Watch: Why π is in the normal distributionYouTubeيوتيوب24 min د
    • Watch: A pretty reason why Gaussian + Gaussian = GaussianYouTubeيوتيوب13 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة
  5. Day 5: Conjugate priors and the exponential family 2 lessonsدروس · 70 min د
    • Read: conjugacy and the exponential family (6.6)slidesشرائح55 min د
    • Explore: learn a coin's bias15 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة
  6. Day 6: Change of variables 2 lessonsدروس · 80 min د
    • Read: change of variables and inverse transform (6.7–6.8)slidesشرائح50 min د
    • Check yourself: chapter 630 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة
  7. Day 7: Gradient descent 3 lessonsدروس · 86 min د
    • Read: optimization using gradient descent (7.1)slidesشرائح45 min د
    • Explore: gradient descent with your own step size20 min د
    • Watch: Gradient descent, how neural networks learnYouTubeيوتيوب21 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة
  8. Day 8: Constrained optimization and Lagrange multipliers 1 lessonsدروس · 35 min د
    • Read: constrained optimization (7.2)slidesشرائح35 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة
  9. Day 9: Convex optimization 2 lessonsدروس · 85 min د
    • Read: convex optimization (7.3) and further reading (7.4)slidesشرائح60 min د
    • Check yourself: chapter 725 min د
    Practical taskمهمة عمليةExplain in your wordsاشرح بكلماتكQuizاختبار · 4 questionsأسئلة

Read a lesson firstاقرأ درسًا أولًا

Start here

This is part 3 of 4 of Mathematics for Machine Learning: probability and distributions, and continuous optimization (chapters 6 and 7). It uses gradients from part 2 and inner products from part 1.

First, get the book (free)

This course follows Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal and Cheng Soon Ong (Cambridge University Press, 2020). The authors give the PDF away for personal use, but it may not be shared, so it is not inside this course. Each lesson opens the right pages of your own copy.

  1. Download the PDF from mml-book.com (the link "PDF of the book").
  2. On this course's page, in the box "This course reads a book you get yourself", press Find it for me. inPace looks in your Downloads, Documents and Desktop folders (on a phone: Download, Documents, Books) and recognizes the book by its title and number of pages, whatever the file is called.
  3. If it's somewhere else, choose the folder or the file yourself.

If inPace warns that the page count is different, you have another printing: the lessons may open a page or two off. The course expects the 412-page PDF from mml-book.com.

Using an older inPace? Before 1.20, rename the file to mml-book.pdf and put it in this course's folder. Before 1.21, the optional task and explanation still count as required. Updating inPace fixes both.

Your plan

Days Module Book
1 Probability spaces, discrete and continuous 6.1–6.2
2 Sum rule, product rule and Bayes' theorem 6.3
3 Means, variances and independence 6.4
4 The Gaussian distribution 6.5
5 Conjugate priors and the exponential family 6.6
6 Change of variables 6.7–6.8
7 Gradient descent 7.1
8 Constrained optimization and Lagrange multipliers 7.2
9 Convex optimization 7.3–7.4

About 1 to 1.5 hours a day, for 9 days. The Gaussian (day 4) and convex optimization (day 9) are the longest.

How each module works

  • Read lessons open the book at the right pages, with notes under them: what matters most, what to skim, and the intuition behind the formulas. Read with a pencil. The book's examples are worth redoing by hand.
  • Watch lessons play a 3Blue1Brown video that shows the same idea moving. Watch it before or after the reading, whichever you like.
  • Explore lessons are small interactive pages: drag, slide and see the math change.
  • Check yourself pages give you exercises with instant feedback and worked answers. They don't count for anything.
  • Each module ends with a quiz you need 70% to pass to open the next module. There's also a task and a short explanation in your own words: they're optional, but doing them is the best way to make it stick.

Licenseالرخصة: CC-BY-SA-4.0 · Updatedآخر تحديث 2026-11-13

Book (not included): Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal and Cheng Soon Ong, Cambridge University Press 2020, free to download for personal use at mml-book.com. Videos: 3Blue1Brown (Grant Sanderson) on YouTube. Notes, charts, interactive pages, tasks and questions: inPace.

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