The Student Services team will have office hours at Harmony House (561 Lomita Dr, Stanford, CA 94305) on Tuesday and Thursday from 10 a.m. to 4 p.m.
Machine Learning
Topics: statistical pattern recognition, linear and non-linear regression, non-parametric methods, exponential family, GLMs, support vector machines, kernel methods, deep learning, model/feature selection, learning theory, ML advice, clustering, density estimation, EM, dimensionality reduction, ICA, PCA, reinforcement learning and adaptive control, Markov decision processes, approximate dynamic programming, and policy search. Prerequisites: knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy to the equivalency of CS106A, CS106B, or CS106X, familiarity with probability theory to the equivalency of CS 109, MATH151, or STATS 116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51 or CS205.
Details:
- Catalog Number
- CS 229-01
- Class Number
- 23337
- Course Cost
- $5488.00
- Population
- Undergraduate, Graduate
- Units
- 4
- Interest Area
- Computer Science and Engineering
- Course Format & Length
- In-Person, 8 weeks
- Instructors
- Jehangir Amjad
- Dates
- -
- Prerequisites
-
knowledge of basic computer science principles and skills at a level sufficient to write a reasonably non-trivial computer program in Python/NumPy to the equivalency of CS106A, CS106B, or CS106X, familiarity with probability theory to the equivalency of CS 109, MATH151, or STATS 116, and familiarity with multivariable calculus and linear algebra to the equivalency of MATH51 or CS205.
- Schedule
- Tue, Thu 4:30 PM - 6:15 PM
- Cross Listings
- STATS 229
- Course Notes
-
May be taken for 3 units by Stanford graduate students.