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\documentclass[11pt]{article} \usepackage[margin=1in]{geometry} \usepackage{titlesec} \usepackage{enumitem} \usepackage{hyperref} \usepackage{xcolor} \titleformat{\section}{\large\bfseries\color{teal!70!black}}{\thesection}{1em}{} \setlist{noitemsep,topsep=2pt} \title{\textbf{Introduction to Machine Learning} \\ \large CS 229 \ -- \ Fall 2026} \author{Prof. Jane Smith \\ \texttt{jsmith@university.edu}} \date{} \begin{document} \maketitle \vspace{-1.5em} \begin{center} \textbf{Class meeting:} MWF 10:00-10:50 AM \quad | \quad \textbf{Room:} Gates B03 \\ \textbf{Office:} Gates 312 \quad | \quad \textbf{Office hours:} Tuesdays 2-3 PM and by appointment \end{center} \section*{Course Description} An introduction to the field of machine learning, including linear regression, classification, neural networks, support vector machines, dimensionality reduction, and reinforcement learning. Emphasis on mathematical foundations and practical implementation. \section*{Learning Outcomes} By the end of this course, students will be able to: \begin{itemize} \item Formulate a real-world problem as a machine learning task with clear inputs, outputs, and evaluation criteria. \item Implement and train regression, classification, and neural network models from scratch. \item Diagnose under- and over-fitting and apply appropriate regularization. \item Critically evaluate ML papers and identify methodological strengths and weaknesses. \end{itemize} \section*{Required Texts} Hastie, Tibshirani, Friedman, \textit{The Elements of Statistical Learning} (free online). Supplementary readings posted weekly. \section*{Grading} 5 problem sets (40\%), midterm exam (25\%), final project (30\%), participation (5\%). \section*{Schedule} \begin{tabular}{p{2cm} p{6cm} p{4cm}} \textbf{Week} & \textbf{Topic} & \textbf{Readings / Assignments} \\ \hline 1 & Introduction and linear regression & HTF Ch. 1-3 \\ 2 & Logistic regression & HTF Ch. 4; PS1 due \\ 3 & SVMs and kernels & Bishop Ch. 7 \\ 4 & Decision trees and ensembles & HTF Ch. 9-10; PS2 due \\ 5 & Neural networks & DL Ch. 6; project proposal due \\ 6 & Midterm exam & \\ 7 & CNNs and computer vision & DL Ch. 9 \\ 8 & Sequence models & DL Ch. 10; PS3 due \\ 9 & Unsupervised learning & HTF Ch. 14 \\ 10 & Reinforcement learning & Sutton \& Barto \\ 11 & Fairness and ethics & Selected papers; PS4 due \\ 12 & Project presentations & \\ \end{tabular} \section*{Course Policies} \textbf{Academic integrity:} Discussion of problem sets is encouraged; copied solutions are not. \textbf{Late work:} Each student has 5 late days for problem sets; after that, 10\% per day. \textbf{Accommodations:} Students requiring accommodations should contact the disability office and forward documentation to me by the second week of the semester. \end{document}