## Course curriculum

1. Statistics Module Slides - presentation

1. Focus and Objectives

3. READING: Jeff Dean Rice Talk - State of Artificial Intelligence (Read entire document) (Dated but useful)

4. Module 1 - SLIDES - Part 1

5. Module 1 - SLIDES - Part 2

6. Module 1 - SLIDES - Part 3

7. Module 1 - SLIDES - Part 4

8. Module 1 - SLIDES - Part 5

9. Module 1 - SLIDES - Part 6

10. Module 1 - SLIDES - Part 7

11. Lesson 1: Introduction to Machine Learning

12. Lesson 1: Lab 1

13. Lesson 2-1: Lab-2a

14. Lesson 2-1: Pandas

15. Lesson 2-1: Exploring Pandas

16. Lesson 2-2: Lab-2b

17. Lesson 2-2: Lab 2c

18. Lesson 2-3: Visualization

19. Lesson 2-4: Lab-2d

20. Lesson 2-4: Visualization-Stats

21. Lesson 2-4: Lab 3a

22. Lesson 3-1: Sklearn

23. Lesson 3-2: Lab-3b

24. Lesson 3-2: Linear Regression

25. Lesson 3-3: Multivariate Linear Regression

26. Lesson 3-4: Logistic Regression (updated audio)

1. Module 2 - Focus and Objectives

4. Lesson 1a: Classification (Support Vector Machines)

5. Lesson 1b: Classification (Naive Bayes)

6. Lesson 2-1: Lab1a and 1b

1. Focus and Objectives

5. SLIDES - In pdf format (ALL SLIDES AVAILABLE HERE)

6. Module 3 - SLIDES - Part 1

7. Module 3 - SLIDES - Part 2

8. Module 3 - SLIDES - Part 3

9. Module 3 - SLIDES - Part 4

10. Module 3 - SLIDES - Part 5

11. Module 3 - SLIDES - Part 6

12. Lesson 1a: Classification (Support Vector Machines)

13. Lesson 1b: Classification (Naive Bayes)

14. Lesson 2-1: Lab1a and 1b

15. Lesson 2a: Decision Trees

16. Lesson 2b: Random Forests

17. Lesson 2-1: Lab-2a and 2b

18. Lesson 2-1: Lab-2c

19. Lesson 3a: Clustering

20. Lesson 3b: Principal Component Analysis

21. Lesson 2-1: Lab-3a and 3b

22. Lesson 3-1: Lab-3c (Principal Component Analysis)

1. Focus and Objectives

2. READING: Introduction to Deep Learning

3. READING: Introduction to Linear Algebra

5. SLIDES - In pdf format (ALL SLIDES AVAILABLE HERE)

6. Module 4 - SLIDES - Part 1

7. Module 4 - SLIDES - Part 2

8. Module 4 - SLIDES - Part 3

9. Module 4 - SLIDES - Part 4

10. Module 4 - SLIDES - Part 5

11. Module 4 - SLIDES - Part 6

12. Module 4 - SLIDES - Part 7

13. Module 4 - SLIDES - Part 8

14. Module 4 - SLIDES - Part 9

15. Module 4 - SLIDES - Part 10

16. Lesson 1a: Deep Learning - Intro

17. Lesson 1a: Lab 1a - Tensorflow Playground

18. Lesson 1b: TensorFlow - Intro

19. Lesson 1b: Lab 1b - Tensorflow Sessions

20. Lesson 1c: TensorFlow- Low Level API

21. Lesson 2a: TensorFlow - Linear Models

22. Lesson 2a: Lab 2a and 2b

23. Lesson 2b: TensorFlow - High-Level API

24. Lesson 2b: Lab 2c and 2d

25. Lesson 3a: Lab 3a

26. Lesson 3a: Lab 3b and 3c

27. Lesson 3b: Lab 3d and 3e

28. Lesson 4: Multilayer Perceptron (MLP)

1. Focus and Objectives

2. READING: Place of Convolutional Neural Networks (CNN) and Deep Learning

3. READING: Parameter Sharing and CNN

5. READING: A Brief History of CNNs in Image Segmentation

7. SLIDES - In pdf format (ALL SLIDES AVAILABLE HERE)

8. Module 5 - SLIDES - Part 1

9. Lesson 1 - Convolutional Neural Networks

10. Lesson 2 - Convolutional Neural Networks, Extended

11. Lesson 3 - TensorBoard: Visualizing Learning