CS229 Midterm

CS229 Midterm Covered topics Notes 1-5; Deep learning; 7-8

  1. Supervised learning and discriminative algorithms (Lecture Notes 1): basic concepts, linear regression, logistic regression, generalized linear models, softmax regression
  2. Generative learning algorithms (Lecture Notes 2): Gaussian discriminant analysis, Naive Bayes
  3. Support vector machines (Lecture Notes 3): We’ll only cover a subset of topics as described in @655.
  4. Learning theory (Lecture Notes 4): Only Section 1 (bias/variance trade-off).
  5. Regularization and model/feature selection (Lecture Notes 5).
  6. Evaluating and debugging learning algorithms, Practical advice (Lectures: end of 10/11, 10/16, 10/18, and start of 10/23)
  7. Deep learning (Lectures: 10/23, 10/25): NN architecture, forward propagation, backpropagation, vectorization
  8. Unsupervised learning (Lecture Notes 7a, 7b, and 8): Clustering, k-means, EM, mixture of Gaussians.
  9. We will not include PCA/ICA/Factor Analysis.

Exam Proctoring Montgomery County Community College Testing Center: College Hall Room 264 340 DeKalb Pike, Blue Bell, PA 19422 Phone: 215-641-6646 E-Mail: testing@mc3.edu

Weingarten Learning Center 215-573-9235

Philadelphia Free Library

Delaware County CC 610-325-2776