Week Lecture Topic
1 Lecture 1 Lecture 2   Introduction, Data Objects, and Types, Central Tendency,   Dispersion, Correlation, Normal Distribution
2 Lecture 3 Lecture 4   Hypothesis Testing, Visualization, Similarity Measures Part1
3 Lecture 5 Lecture 6   Similarity Measures Part 2, Data Quality, Cleaning, and   Integration
4 Lecture 7 Lecture 8   Data Reduction and Transformation, Dimensionality   Reduction, Classification Introduction
5 Lecture 9 Lecture 10   Decision Tree, Splitting Measures
6 Lecture 11 Lecture 12   Naive Bayes Classifier, Linear Models
7 Lecture 13 Lecture 14   Evaluation, Lazy Learning, Ensemble Methods
8 Lecture 15 Lecture 16   Bayesian Belief Networks, Support Vector Machines
9 -   Spring Break
10 Lecture 17 Lecture 18   Pattern Discovery Concepts, Pattern Discovery Methods:   Apriori, FPGrowth, and ECLAT
11 Lecture 19 Lecture 20   Sequential Pattern Mining, Evaluation
12 Lecture 21 Lecture 22   Constraint Pattern Mining, Graph Pattern Mining
13 Lecture 23 Lecture 24   Cluster Analysis Introduction, Partitioning-based Methods,   Hierarchical Clustering Methods
14 Lecture 25 Lecture 26   Density-based and Grid-based Clustering Methods,   Probabilistic Model-based Clustering Methods, Validation
15 Lecture 27 Lecture 28   Deep Learning
16 -    Reading Week (No Lectures)