Tentative Schedule of Topics

We will cover selected sections from chapters of the textbook (3rd Edition). Additional notes/slides/readings will also be provided as necessary. Along with traditional lecture content, the class sessions will include discussions, assessments, active problem-solving and learning through live coding, and implementation exercises. Please make sure that you complete the assigned readings (preferably in advance of class sessions), and check the Canvas site regularly for updates.

Weekly Schedule

Weeks	Reading	Topics
1	Ch. 1 and 2	Preliminaries; Python’s scientific computing ecosystem.
2, 3	Ch. 3	Data similarity; Locality-sensitive hashing.
4	Ch. 4	Data Streaming algorithms; Probabilistic sketches (class notes)
5	Ch. 6	Frequent itemsets and association rules.
6, 7	Ch. 7	Review of relevant linear algebra (notes); Clustering
8	Ch. 5 and 11	Link analysis; Dimension reduction.
9	Ch. 9	Recommendation systems.
10	Ch. 10	Data mining in social networks.
11, 12	Ch. 12	Classical machine learning algorithms.
13	Ch. 13	Deep learning.
14		Term project presentations.