Section 1 : Introduction and Outline
Lecture 1 Introduction and Outline Why would you want to use an HMM copy 00:04:04 Duration
Lecture 2 Unsupervised or Supervised 00:02:59 Duration
Lecture 3 Where to get the Code and Data
Lecture 4 Anyone Can Succeed in this Course 00:11:55 Duration

Section 2 : Markov Models
Lecture 1 The Markov Property 00:04:40 Duration
Lecture 2 Markov Models 00:07:02 Duration
Lecture 3 The Math of Markov Chains 00:05:16 Duration

Section 3 : Markov Models Example Problems and Applications
Lecture 1 Example Problem Sick or Healthy 00:03:26 Duration
Lecture 2 Example Problem Expected number of continuously sick days 00:02:53 Duration
Lecture 3 Example application SEO and Bounce Rate Optimization 00:08:54 Duration
Lecture 4 Example Application Build a 2nd-order language model and generate phrases 00:13:07 Duration
Lecture 5 Example Application Google’s PageRank algorithm 00:05:04 Duration
Lecture 6 Suggestion Box 00:02:26 Duration

Section 4 : Hidden Markov Models for Discrete Observations
Lecture 1 From Markov Models to Hidden Markov Models 00:06:02 Duration
Lecture 2 HMM - Basic Examples 00:08:04 Duration
Lecture 3 Parameters of an HMM 00:07:01 Duration
Lecture 4 The 3 Problems of an HMM 00:05:43 Duration
Lecture 5 The Forward-Backward Algorithm (part 1) 00:16:59 Duration
Lecture 6 The Forward-Backward Algorithm (part 2) 00:07:09 Duration
Lecture 7 The Forward-Backward Algorithm (part 3) 00:07:18 Duration
Lecture 8 The Viterbi Algorithm (part 1) 00:06:14 Duration
Lecture 9 The Viterbi Algorithm (part 2) 00:15:05 Duration
Lecture 10 HMM Training (part 1) 00:04:41 Duration
Lecture 11 HMM Training (part 2) 00:10:22 Duration
Lecture 12 HMM Training (part 3) 00:13:34 Duration
Lecture 13 HMM Training (part 4) 00:13:17 Duration
Lecture 14 How to Choose the Number of Hidden States 00:07:02 Duration
Lecture 15 Baum-Welch Updates for Multiple Observations 00:04:54 Duration
Lecture 16 Discrete HMM in Code 00:20:33 Duration
Lecture 17 The underflow problem and how to solve it 00:05:05 Duration
Lecture 18 Discrete HMM Updates in Code with Scaling 00:11:53 Duration
Lecture 19 Scaled Viterbi Algorithm in Log Space 00:03:39 Duration

Section 5 : Discrete HMMs Using Deep Learning Libraries
Lecture 1 Gradient Descent Tutorial 00:04:30 Duration
Lecture 2 Theano Scan Tutorial 00:12:40 Duration
Lecture 3 Discrete HMM in Theano
Lecture 4 Improving our Gradient Descent-Based HMM 00:05:10 Duration
Lecture 5 Tensorflow Scan Tutorial 00:12:43 Duration
Lecture 6 INTRODUCTION TO BRAINMEASURES PROCTOR SYSTEM

Section 6 : HMMs for Continuous Observations
Lecture 1 Gaussian Mixture Models with Hidden Markov Models 00:04:12 Duration
Lecture 2 Generating Data from a Real-Valued HMM 00:06:35 Duration
Lecture 3 Continuous-Observation HMM in Code (part 1) 00:18:38 Duration
Lecture 4 Continuous-Observation HMM in Code (part 2) 00:05:13 Duration
Lecture 5 Continuous HMM in Theano 00:16:33 Duration
Lecture 6 Continuous HMM in Tensorflow 00:09:26 Duration

Section 7 : HMMs for Classification
Lecture 1 Generative vs 00:02:30 Duration
Lecture 2 HMM Classification on Poetry Data (Robert Frost vs

Section 8 : Bonus Example Parts-of-Speech Tagging
Lecture 1 Parts-of-Speech Tagging Concepts 00:05:00 Duration
Lecture 2 POS Tagging with an HMM 00:05:58 Duration

Section 9 : Theano, Tensorflow, and Machine Learning Basics Review
Lecture 1 (Review) Gaussian Mixture Models 00:03:05 Duration
Lecture 2 (Review) Theano Tutorial 00:07:47 Duration
Lecture 3 (Review) Tensorflow Tutorial 00:07:27 Duration

Section 10 : Setting Up Your Environment (FAQ by Student Request)
Lecture 1 Windows-Focused Environment Setup 2018 00:20:20 Duration
Lecture 2 How to install Numpy, Scipy, Matplotlib, Pandas, IPython, Theano, and TensorFlow 00:17:33 Duration

Section 11 : Extra Help With Python Coding for Beginners (FAQ by Student Request)
Lecture 1 How to Code by Yourself (part 1)
Lecture 2 How to Code by Yourself (part 2) 00:09:23 Duration
Lecture 3 Proof that using Jupyter Notebook is the same as not using it 00:12:29 Duration
Lecture 4 Python 2 vs Python 3 00:04:29 Duration

Section 12 : Effective Learning Strategies for Machine Learning (FAQ by Student Request)
Lecture 1 How to Succeed in this Course (Long Version) 00:10:24 Duration
Lecture 2 Is this for Beginners or Experts Academic or Practical Fast or slow-paced
Lecture 3 Machine Learning and AI Prerequisite Roadmap (pt 1) 00:11:15 Duration
Lecture 4 Machine Learning and AI Prerequisite Roadmap (pt 2) 00:16:07 Duration

Section 13 : Appendix FAQ Finale
Lecture 1 What is the Appendix 00:02:39 Duration