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learningguideReading Berkeley CS189 Introduction to Machine Learning

Linear Algebra / Calculus / Probability Warm-up + Fashion Coding | Berkeley CS189 Assignment 1

CS189 Spring 2026 HW1 has three pieces: ten written math warm-ups (a linear system, limits of matrix powers via eigendecomposition, SVD, a matrix that flips an image, partial derivatives, a chain rule over a recursion, and four probability problems including Bayes for cancer screening), plus two public Modal notebooks on Fashion-MNIST. Part 1 drills pandas / Plotly / K-means / an MLP / matrix-based image augmentation / tensor puzzles; Part 2 does price regression, MAE / MSE / R², confusion matrices, and finally a secret test set whose images have been rotated. Due Feb 20; no official solutions.

#berkeley#cs189#machine-learning#course-guide#homework#linear-algebra#probability
techguideReading Harvard CS1810 Machine Learning

Do These 4 Problems First — They Tell You What to Patch | Harvard CS181 Assignment 0

HW0 checks CS181 prerequisites in four problems — y=Xw solvability, optimizing an objective, reasoning about randomness, and OLS in Python. The problem that slows you down most is the gap to patch before HW1.

#harvard#cs181#machine-learning#linear-algebra#calculus#probability#linear-regression#python
aideep-diveReading CMU 07-280 AI & ML I

Linear Regression and the Normal Equation | CMU 07-280 Lecture 7

Lecture 7 applies ERM to linear functions and squared loss, moves from a one-dimensional slope to `argmin ||y-Xθ||²`, and derives the normal equation when `XᵀX` is invertible.

#cmu#ai-course#linear-regression#machine-learning#linear-algebra
learningguideReading CMU 10-301 Introduction to Machine Learning

Find ML Foundation Gaps with Mathematics and Python | CMU 10-301 Assignment 1

HW1 is written and programming work: mathematical and CS foundations followed by a majority-vote classifier.

#cmu#machine-learning#python#linear-algebra