Outline of Case Studies, Labs and Projects for the four verticals of AI.

Machine Learning

Labs:

1. Classification of Multivariate Gaussion Distribution using FeedForward Network

Projects:

1. Automatic Image Classification with Neural Networks and PyBrain

The learnings will be,

• Understanding implementation difference between Classification and Regression
• Data split importance
• Accuracy Calculations

The skills learned will be,

• Reading images with Python
• Converting images to datasets
• Feature extraction in images that will lead to classification

Case Studies:

1. Back Propagation Algorithm Implementation with Python

NLP

Labs:

1. Conditional Frequency Distributions
   • Frequency Distribution and Dispersion Plots
   • Plotting and Tabulating Distributions
   • Counting Words by Genre
2. Convert English Text to Pig Latin
   • Definition of Pig Latin Each word of the text is converted as follows: move any consonant (or consonant cluster) that appears at the start of the word to the end, then append ay, e.g. string → ingstray, idle → idleay. http://en.wikipedia.org/wiki/Pig_Latin
   • Write a function to convert a word to Pig Latin.
   • Write code that converts text, instead of individual words. Extend it further to preserve capitalization, to keep qu together (i.e. so that quiet becomes ietquay), and to detect when y is used as a consonant (e.g. yellow) vs a vowel (e.g. style).
3. Process the Brown Corpus to find answers to the following questions,
   • Which nouns are more common in their plural form, rather than their singular form? (Only consider regular plurals, formed with the -s suffix.)
   • Which word has the greatest number of distinct tags. What are they, and what do they represent?
   • List tags in order of decreasing frequency. What do the 20 most frequent tags represent?
   • Which tags are nouns most commonly found after? What do these tags represent?
4. Write a tag pattern to identify places of work from a set of resumes by building your own grammar.

Projects:

Analysis of a real world event using Twitter data. This includes graphing with matplotlib.

The various analysis will be,

• Sentiment Classification
• Keyword Extraction
• Frequency Distribution

The feature extractors will be,

• Unigrams + Bigrams
• Frequency Distribution Extraction

Skills learnt will be,

• Data Gathering
• Data Classification
• Data Normalizing
• Graphing
• JSON API Calls and Ratelimiting
• Reading and Storing YAML data

Case Studies

1. Resume Parser

• Accepts resumes in PDF format
• Must parse the applicants for,
  • Name
  • Birthdate
  • Address
  • Experience in years
  • Categorize skills

Algorithmic Programming

Labs:

• Sierpinski Triangle
• Tower of Hanoi
• Exploring a Maze
• Implementing a binary search tree that accepts any input type
• The Knight’s Tour Problem
• Dijkstra’s Problem

Projects:

1. Create rate-limiting tool with Python and Redis
   • Use the Python Redis package, https://pypi.python.org/pypi/redis.
   • Redis will be used to store all the values to be processed.
   • A background job will read the data from Redis and process them at regular intervals.
   • Background jobs will be managed using the RQ Python package, http://python-rq.org/
   • Create a CLI function that would start these background processes

Case Studies

1. An Anagram Detection Example using multiple Algorithm Solutions
   • Solution 1: Checking Off
   • Solution 2: Sort and Compare
   • Solution 3: Brute Force
   • Solution 4: Count and Compare
2. Longest Common Sunsequence Algorithm Python Implementation
3. Automatic Text Generation with Hidden Markov Models

Bayesian Inference

Labs:

1. Modelling Coin flip patterns using Bayes Rule

Projects:

1. Ordering Reddit submissions
   • Theory
     • Relook at Law of Large Numbers Relook at Naive Bayes Theorem
     • Relook at Gaussian distribution
   • Introduction to the problem
   • Discussion on factors involved
     • Popularity (Number of upvotes)
     • Difference of upvotes and downvotes
     • Time adjusted (rate of upvotes/downvotes)
     • Ratio of upvotes to total number of votes
   • Understanding how human behavior affects data

Case Studies

1. Algorithm for human deceit
   • A look at Binomial distribution
   • Example: Cheating Students