Why Do Ridge, Lasso, and Weight Decay Make Models More Stable?
Regularization adds a preference against extreme parameters. Ridge, Lasso, and weight decay trade some training fit for a model that generalizes more reliably.
learning
345 postsHow Can Statistics and ML Evaluation Be Rerun to Reach the Same Conclusion?
A reproducible workflow preserves the evidence chain from data to conclusion. Results need data versions, code, seeds, environment, metrics, and raw outputs.
Why Can a Sample Say Something About a Population or Model?
Sampling makes sample statistics fluctuate, and standard error describes that fluctuation. This post separates SD, SE, sampling distributions, and CLT, then connects them to benchmark uncertainty.
How Do Sampling Distributions Become Exam-Ready Reasoning?
A sampling distribution describes how a statistic fluctuates under repeated sampling. Means, proportions, and variances each connect to common distributions used in intervals and tests.
After 53 Posts, How Do You Connect Statistics to ML, Causality, and Mathematical Statistics?
The series does not finish all of statistics. It gives beginners a working map for exams, ML/AI evaluation, causality, Bayesian thinking, time series, and mathematical statistics.
How Does One Regression Line Become Prediction, Interpretation, and Error?
Simple linear regression uses one X to describe the average change in Y. Slope, intercept, residuals, and squared error form the smallest supervised learning model.
How Does Monte Carlo Use Repeated Simulation to Answer Hard Statistical Questions?
Monte Carlo repeats a data-generating process many times so sampling variation, power, coverage, and evaluation instability become visible.
Where Should You Start Statistics If You Need Exams and ML/AI?
Do not start statistics exam prep by memorizing formulas. Start with the sequence of data, probability, sampling, inference, regression, then connect those ideas to model evaluation, A/B testing, and uncertainty in ML/AI.
Why Should Time-Series Data Not Be Randomly Split?
Time-series data have order. Random splits can leak future information into training and make forecasting or monitoring results look better than they are.
Which Test Fits a Two-Group Mean or Proportion Difference?
Two-group comparisons start by classifying the outcome and the design: numeric or binary, independent or paired. That choice determines the standard error, test statistic, and conclusion.
How Does Variable Selection Avoid Memorizing the Training Data?
Variable selection is not only about choosing predictors. It is about avoiding noisy training-set wins that do not generalize.
Statistics Is Not Formula Memorization: What Is It Deciding?
The core of statistics is judgment: describe data, estimate unknowns, compare differences, inspect associations, and make decisions under uncertainty.
Inference When Ghosts Are Invisible | Berkeley CS188 Topic Guide
Lectures 13–18 and Project 4 move from factor operations and variable elimination to exact inference and particle filtering, letting Pacman track invisible ghosts through noisy distance sensors.
Completing CS188: Turn 28 Lectures and Projects P0–P5 into a Portfolio | Berkeley CS188 Course Overview
Lectures 26–28 close with nuclear monitoring, AI safety, and reflection. Independent completion should preserve assumptions, test evidence, and failure analysis for Projects 1–5 instead of reporting only autograder scores.
Choosing Minimax, Alpha-Beta, and Expectimax | Berkeley CS188 Topic Guide
Lectures 5–8 use CSPs to practice variables, constraints, and search order before Project 2 implements minimax, alpha-beta, and expectimax. Their key difference is the assumption made about other agents.
From VPI and Naive Bayes to Attention | Berkeley CS188 Topic Guide
Lectures 19–25 connect rational decisions and VPI to machine learning, while Project 5 uses PyTorch for regression, classification, CNNs, attention, and an optional character-GPT.
From Value Iteration to Q-Learning | Berkeley CS188 Topic Guide
Lectures 9–12 and Project 3 use the same Gridworld to contrast value iteration with a known model, Q-learning from unknown dynamics, and approximate Q-learning that generalizes through features.
Pacman from DFS and BFS to A* | Berkeley CS188 Topic Guide
Lectures 1–4 and Project 1 connect DFS, BFS, UCS, A*, state representation, and heuristic design. The goal is not memorizing algorithms but separating what the frontier, cost, and state each control.
Learn AI Through Projects P0–P5 | Berkeley CS188 Course Overview
CS188 Spring 2026 publishes 28 recordings, 27 lecture slide sets, 11 discussions, and Projects P0–P5. P0 is a Python/autograder tutorial, P1–P4 use Pacman settings, and P5 contains general machine-learning tasks.
Spring 2025 Overview: HW1–7 with Code and Data You Can Run, Plus What Fall 2026 Looks Like | Berkeley CS189 Course Overview
CS189 exists online in several terms. From post 2 onward this series uses Spring 2026 (eecs189.org/sp26, A3) as its base, walking through each lecture block and assignment; Spring 2025 (Shewchuk) is a different classic route with 25 lectures of notes, HW1–7 and past exams, but its official recordings sit behind a bCourses login; Fall 2026 is still in progress and serves only as a reference. This post is the series entry point and full table of contents.