CS181: Machine Learning

Harvard University

Course Info

Instructors
  • David Parkes
    OH: Thur 2:30-3:30, 5.15-6p, MD 229
  • Alexander "Sasha" Rush
    OH: Wed 2:30-4, MD 217
  • Email: Piazza preferred or cs181 at seas.harvard.edu (instructors only)
Lectures
Teaching Assistants
  • Shai Szulanski, Jeffrey Ling, Samuel Cheng, Ankit Gupta, Aidi Zhang, Lily Zhang, Frances Ding, Mark Goldstein, Charles Liu, Jeffrey Chang, Rachit Singh, Joseph Song, Fanney Zhu, Christine Hwang, and Carl Denton
Forum and Announcements
Office Hours
  • Tue 8-10pm: Quincy DH
  • Wed 6-8pm: MD 119 / Second Floor
  • Wed 8-10pm: Lowell DH
  • Thu 8-10pm: Currier DH
  • Thu 8-10pm: Eliot DH
  • Fri 10-Noon: MD First Floor Lounge (one floor above ground!)
References
Section Times
  • Mon, 4-5,5-6p: MD 119
  • Tues, 4-5p: Northwest Basement (B105)
  • Wed, 4-5,5-6p: MD 119
Syllabus and Collaboration Policy
Links


Schedule

Date Area TopicDemos ReadingsAssignment (DUE: Fri 5pm of this week)
Jan. 24 Machine Learning
Jan. 26 Regression Linear Regression 1 Regression Bishop § 3.1, Sklearn § 3.1, 18.10
Jan. 27 Section 0 Math Review
Jan. 30 Section 1 Linear Reg ( sol )
Jan. 31 Linear Regression 2 Gaussian
BasisRegression
Bishop § 2.3, 3.1 T1 Regression (submit | self-grading)
Feb. 2 Model Regularization Model Selection
Feb. 6 Section 2 Model Selection (sol) Sklearn
Feb. 7 Bayesian Linear Regression Bishop § 3.3 P1 Regression (submit | kaggle)
Feb. 9 Classification Linear Classification Perceptron Bishop § 4.1, Sklearn § 15.9
Feb. 13 Section 3 Bayes Classification (sol)
Feb. 14 Probabilistic Classification Probabilistic Classification Bishop § 4.2, 4.3, Sklearn § 18.1, 29.24
Feb. 16 Neural Networks Neural Networks 1 Neural Networks 1
TF Playground
Bishop § 5.1-5.2
Feb. 20 Section 4 Probabilistic Classification & NN1 (sol)
Feb. 21 Neural Networks 2 Neural Networks 1
ConvNet JS
Bishop § 5.3 T2 Classification (submit | self-grading)
Feb. 23 Margin-Based Models Margin-Based Classification
Feb. 27 Midterm Review
Feb. 28 Support Vector Machines
Mar. 2 Midterm 1
Mar. 7 Topics 1 Deep Learning P2 Classification (submit | kaggle)
Mar. 9 Unsupervised Hierarchical Clustering, K-Means
Mar. 21 Mixture Models, EM T3 SVMs (submit)
Mar. 23 Topic Models
Mar. 28 Dimensionality Reduction T4 Clustering (submit)
Mar. 30 Graphical Models HMMs
Apr. 4 Bayesian Networks P3 Unsupervised (submit | kaggle)
Apr. 6 Inference
Apr. 11 Reinforcement Learning MDP / Value and Policy Iteration T5 EM (submit)
Apr. 13 Reinforcement Learning
Apr. 18 Deep RL
Apr. 20 Topics 2 Learning Theory
Apr. 25 Midterm 2 P4 RL (submit | kaggle)

Grading

Grades are determined by four aspects of the class:

  • Four Practicals [P] (30%)
  • Five Homeworks [T] (30%)
  • Midterm Exam 1 (20%)
  • Midterm Exam 2 (20%)