#### Section 1 : Introduction and Outline

 Lecture 1 Introduction and Outline Why would you want to use an HMM copy 4:4 Lecture 2 Unsupervised or Supervised 2:59 Lecture 3 Where to get the Code and Data Lecture 4 Anyone Can Succeed in this Course 11:55

#### Section 2 : Markov Models

 Lecture 5 The Markov Property 4:40 Lecture 6 Markov Models 7:2 Lecture 7 The Math of Markov Chains 5:16

#### Section 3 : Markov Models Example Problems and Applications

 Lecture 8 Example Problem Sick or Healthy 3:26 Lecture 9 Example Problem Expected number of continuously sick days 2:53 Lecture 10 Example application SEO and Bounce Rate Optimization 8:54 Lecture 11 Example Application Build a 2nd-order language model and generate phrases 13:7 Lecture 12 Example Application Google’s PageRank algorithm 5:4 Lecture 13 Suggestion Box 2:26

#### Section 4 : Hidden Markov Models for Discrete Observations

 Lecture 14 From Markov Models to Hidden Markov Models 6:2 Lecture 15 HMM - Basic Examples 8:4 Lecture 16 Parameters of an HMM 7:1 Lecture 17 The 3 Problems of an HMM 5:43 Lecture 18 The Forward-Backward Algorithm (part 1) 16:59 Lecture 19 The Forward-Backward Algorithm (part 2) 7:9 Lecture 20 The Forward-Backward Algorithm (part 3) 7:18 Lecture 21 The Viterbi Algorithm (part 1) 6:14 Lecture 22 The Viterbi Algorithm (part 2) 15:5 Lecture 23 HMM Training (part 1) 4:41 Lecture 24 HMM Training (part 2) 10:22 Lecture 25 HMM Training (part 3) 13:34 Lecture 26 HMM Training (part 4) 13:17 Lecture 27 How to Choose the Number of Hidden States 7:2 Lecture 28 Baum-Welch Updates for Multiple Observations 4:54 Lecture 29 Discrete HMM in Code 20:33 Lecture 30 The underflow problem and how to solve it 5:5 Lecture 31 Discrete HMM Updates in Code with Scaling 11:53 Lecture 32 Scaled Viterbi Algorithm in Log Space 3:39

#### Section 5 : Discrete HMMs Using Deep Learning Libraries

 Lecture 33 Gradient Descent Tutorial 4:30 Lecture 34 Theano Scan Tutorial 12:40 Lecture 35 Discrete HMM in Theano Lecture 36 Improving our Gradient Descent-Based HMM 5:10 Lecture 37 Tensorflow Scan Tutorial 12:43 Lecture 38 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM Pdf

#### Section 6 : HMMs for Continuous Observations

 Lecture 39 Gaussian Mixture Models with Hidden Markov Models 4:12 Lecture 40 Generating Data from a Real-Valued HMM 6:35 Lecture 41 Continuous-Observation HMM in Code (part 1) 18:38 Lecture 42 Continuous-Observation HMM in Code (part 2) 5:13 Lecture 43 Continuous HMM in Theano 16:33 Lecture 44 Continuous HMM in Tensorflow 9:26

#### Section 7 : HMMs for Classification

 Lecture 45 Generative vs 2:30 Lecture 46 HMM Classification on Poetry Data (Robert Frost vs

#### Section 8 : Bonus Example Parts-of-Speech Tagging

 Lecture 47 Parts-of-Speech Tagging Concepts 5:0 Lecture 48 POS Tagging with an HMM 5:58

#### Section 9 : Theano, Tensorflow, and Machine Learning Basics Review

 Lecture 49 (Review) Gaussian Mixture Models 3:5 Lecture 50 (Review) Theano Tutorial 7:47 Lecture 51 (Review) Tensorflow Tutorial 7:27

#### Section 10 : Setting Up Your Environment (FAQ by Student Request)

 Lecture 52 Windows-Focused Environment Setup 2018 20:20 Lecture 53 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 17:33

#### Section 11 : Extra Help With Python Coding for Beginners (FAQ by Student Request)

 Lecture 54 How to Code by Yourself (part 1) Lecture 55 How to Code by Yourself (part 2) 9:23 Lecture 56 Proof that using Jupyter Notebook is the same as not using it 12:29 Lecture 57 Python 2 vs Python 3 4:29

#### Section 12 : Effective Learning Strategies for Machine Learning (FAQ by Student Request)

 Lecture 58 How to Succeed in this Course (Long Version) 10:24 Lecture 59 Is this for Beginners or Experts Academic or Practical Fast or slow-paced Lecture 60 Machine Learning and AI Prerequisite Roadmap (pt 1) 11:15 Lecture 61 Machine Learning and AI Prerequisite Roadmap (pt 2) 16:7

#### Section 13 : Appendix FAQ Finale

 Lecture 62 What is the Appendix 2:39