Tentative Schedule

	Title	Content	Example	Assignment
Week 1
August 5	Introduction to Machine Learning
August 6	Supervised Learning
August 7	T1
August 8	Data & Features
August 9	T2
Week 2
August 12	Vectors
August 13	K-Nearest Neighbors (KNN) Algorithm
August 14	T3
August 16	T4
Week 3
August 19	Linear Regression - Least Squares
August 20	Lineare Regression - Gradient Descent
August 21	Logistic Regression
August 22	T5
August 23	Support Vector Machine
Week 4
August 26	Introduction to Unsupervised Learning and Clustering
August 27	Hierarchical Clustering
August 28	T7
August 29	K-Means Clustering
August 30	T8

	Title	Content	Example	Assignment
Exam 1
August 30 - September 2	Exam 1 (Will be notified by the department)

	Title	Content	Example	Assignment
Week 5
September 2	Probability Theory
September 3	Naive Bayes Learning Algorithm
September 4	T9
September 5	Bayes Nets
September 6	T10
Week 6
September 9	Introduction to Decision Trees
September 10 - September 12	Entropy and Information Gain
September 11	T11
September 12	Overfitting and Pruning
September 13	T12
Week 7
September 16	Advantages and Limitations of Decision Trees
September 17	Introduction to Reinforcement Learning
September 18	T13
September 19	Reinforcement Learning through Feedback Networks
September 20	T14
Week 8
September 23	Function Approximation in Reinforcement Learning
September 24	Introduction to Ensemble Methods
September 25	T15
September 26	Random Forests
September 27	T16

	Title	Content	Example	Assignment
Exam 2
September 28 - September 29	Exam 1 (Will be notified by the department)

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