補充教材:

• Machine Learning Algorithms: 課本作者自行維護的原始碼

• Machine Learning Crash Course: by Google.

• Useful Tips: update whenever needed.
• 台大資工系教師在 MOOC 上的機器學習課程：Artificial Intelligence - Learning & Theory, Machine Learning Foundations: Mathematics, and Machine Learning Foundations: Algorithms

• Data Preparation:
  1. Data Cleaning and Preprocessing for Beginners

• 非常棒的數學基礎的介紹：
  1. Why divide the sample variance by N-1?: 介紹 likelihood function, maximum likelihood estimator 等
  2. Partial Derivative:
     1. How Do You Find the Partial Derivative of a Function?
     2. Finding the Gradient of a Vector Function
     3. Derivative of the Logarithmic Function
     4. Derivative of the Exponential Function
  3. Linear Algebra:
     1. Linear Algebra 101: Part 1: matrix elimination, row echelon form, vector space, and subspace.
     2. Linear Algebra 101: Part 2: Linear combination, Column and Row spaces, nullspace, rank, pivot variables and free variables.
     3. Linear Algebra 101: Part 3: Linear independence, Span, Basis, 4 fundamental subspaces, and Orthogonality.
     4. Linear Algebra 101: Part 4: Projections, Orthonormal vectors, Orthogonal matrix, and Gram Schmidt process.
     5. Linear Algebra 101: Part 5: Determinants.
     6. Linear Algebra 101: Part 6: Eigenvalues and eigenvectors.
     7. Linear Algebra 101: Part 7: Eigendecomposition when symmtric.
     8. Linear Algebra 101: Part 8: Positive Definite Matrix.
     9. Linear Algebra 101: Part 9: Singular Value Decomposition.
  4. What are eigenvectors and eigenvalues?
  5. A geometric interpretation of the covariance matrix
  6. Transforming vectors using matrices and Linear Transformation

• Demystifying Entropy.

• The Ultimate Guide to 12 Dimensionality Reduction Techniques (with Python codes).

• Benchmarking State-of-the-Art Deep Learning Software Tools.

資料集:

• UCI Machine Learning Repository, skilearn datasets, and a list of datasets.

• YouTube-8M Segments Dataset

• Big Mart Sales III: 賣場的銷售資料，包含一些 missing values，這是其他已經清理過的資料集不同的地方。