Course Syllabus

1

08/22

Introduction

2

08/24

Why Machine Learning

2

08/29

Supervised Learning: Classification, Regression, Generalization and Model Complexity

3

09/31

Supervised Learning: K Nearest Neighbors, Linear Models, Naïve Bayes

3

09/05

Labor Day (Campus Closed)

4

09/07

Supervised Learning: Decision Trees, Ensembles DTs, Kernelized SVM

4

09/12

Supervised Learning: Neural Networks Fundamentals

5

09/14

Supervised Learning: Deep Learning

5

09/19

Supervised Learning: Deep Learning with Convolutional Neural Networks

6

09/21

Supervised Learning: Deep Learning with Recurrent Neural Networks

6

09/26

Supervised Learning: Some Applications of Deep Learning

7

09/28

Unsupervised Learning and Preprocessing: Principal Components Analysis

7

10/03

Unsupervised Learning and Preprocessing: Non-negative Matrix Factorization

8

10/05

Unsupervised Learning and Preprocessing: Manifold Learning with t-SNE

8

10/10

Unsupervised Learning and Preprocessing: K-Means and K-Means++

9

10/12

Unsupervised Learning and Preprocessing: Agglomerative Clustering, DBSCAN

9

10/17

Exam 1

10

10/19

Representing Data and Engineering Features: Categorical Variables

10

10/24

Representing Data and Engineering Features: Binning, Discretization, Linear Models and Trees

11

10/26

Representing Data and Engineering Features: Interactions, Polynomials, Automatic Feature Selection

11

10/31

Model Evaluation and Improvement: Cross Validation

12

11/02

Algorithmic Chains and Pipelines

12

11/07

Working with Text Data: Types of Data, Representation as Bag-of-Words

13

11/09

Working with Text Data: Stopwords, Rescaling, Model Coefficients

13

11/14

Working with Text Data: n-Grams, Advanced Tokenization, Stemming and Lemmatization

14

11/16

Exam 2

14

11/21

Reinforcement Learning: Introduction

15

11/23

Reinforcement Learning: Tabular Solution Methods

15

11/28

Reinforcement Learning: Approximate Solution Methods

16

11/30

Wrap Up

16

12/05

Project Presentations: Monday, December 05, 10:30 AM – 11:45 AM

Percentage

Grade

97 – 100 plus

A+

93 – 96

A

90 – 92

A-

87 – 89

B+

83 – 86

B

80 – 82

B-

77– 79

C+

73 – 76

C

70 – 72

C-

67 – 69

D+

63 – 66

D

60 - 62

D-

0-59

F