This course is based on in-person lectures, in-person recitation sessions, class notes, and homework.

The class notes in the Notes column below are the official study materials. All appendices in the notes are optional. The note set n_00 next to Overview and Logistics covers part of the prerequisites. You are supposed to know that material already, so this set is optional reading on case you need a refresher.

Lecture slides are also made available for your convenience in the Slides column below. However, they are optional reading and should not be used as your main study materials.

Lectures are automatically recorded on the Panopto system, and are available through Sakai. You need a Duke affiliation to access the recordings.

Prerecorded videos are also available as a backup in the Videos column below for most but not all topics, in case you miss a class or want to review something. However, these videos are optional, and they cover only approximations of the official material.

# Topic Notes Slides Prerecorded Videos HW Due
0 Overview and Logistics (n_00) s_00 Sep 7
1 Functions and Data Fitting n_01 s_01 v_01_1, v_01_2
2 Machine Learning Concepts n_02 s_02 v_02
3 Nearest Neighbor Predictors n_03 s_03 v_03
4 Function Optimization n_04 s_04 v_04
5 Linear Predictors Part 1 n_05 s_05 v_05
6 Linear Predictors Part 2 n_06 s_06 v_06
7 Validation and Testing n_07 s_07 v_07
October 26: Midterm Exam on topics 1-5 (not 6)
8 Support Vector Machines n_08 s_08
9 Kernels n_09 s_09
10 Decision Trees and Forests n_10 s_10 v_10_1, v_10_2
11 Neural Networks n_11 s_11 v_11
12 Training Neural Networks n_12 s_12 v_12
13 Improving Generalization n_13 s_13 v_13
14 Topics not Covered -- s_14 --
December 16, 9am: Final Exam on topics 6-12 (13, 14 not on the exam)

COMPSCI 371, Duke University, Site based on the fluid 960 grid system