Stanford University’s Residential Education program promotes the philosophy that living and learning are integrated and that formal teaching, informal learning, and personal support in residences are integral to a Stanford education. Meals play a key role in this mission of community building, leading, and learning. Therefore, residents of designated university residence halls (Branner, Crothers/Crothers Memorial, Florence Moore, Gerhard Casper, Lakeside, Ricker, Stern, Toyon, Wilbur, Yost, Murray, and EAST) are required to participate in an R&DE Stanford Dining Meal Plan. R&DE Stanford Dining is Committed to Excellence by providing meal plans that offer significant value, the highest quality, and most flexibility of dining across campus, along with a daily variety of delicious, nutritious options including vegetarian , vegan , nut-free , kosher , and halal . The Food Allergies @Stanford program offers support and dining accommodations to students with food allergies or other dietary concerns.
Full-time undergraduate tuition was $42,690 for 2013–2014.  Stanford's admission process is need-blind for US citizens and permanent residents; while it is not need-blind for international students, 64% are on need-based aid, with an average aid package of $31,411.  In 2012–13, the university awarded $126 million in need-based financial aid to 3,485 students, with an average aid package of $40,460.  Eighty percent of students receive some form of financial aid.  Stanford has a no-loan policy.  For undergraduates admitted in 2015, Stanford waives tuition, room, and board for most families with incomes below $65,000, and most families with incomes below $125,000 are not required to pay tuition; those with incomes up to $150,000 may have tuition significantly reduced.  17% of students receive Pell Grants,  a common measure of low-income students at a college.
In this course, you'll learn about some of the most widely used and successful machine learning techniques. You'll have the opportunity to implement these algorithms yourself, and gain practice with them. You will also learn some of practical hands-on tricks and techniques (rarely discussed in textbooks) that help get learning algorithms to work well. This is an "applied" machine learning class, and we emphasize the intuitions and know-how needed to get learning algorithms to work in practice, rather than the mathematical derivations.