Skip to content
/ IIITH_AI_ML Public

This repository contains all the sessions covered during the IIIT-H AI ML course.

8 stars 4 forks Branches Tags Activity
Star
Notifications You must be signed in to change notification settings

arjuntheprogrammer/IIITH_AI_ML

BranchesTags

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

10 Commits
Week0
Week0
 
 
Week1
Week1
 
 
Week10
Week10
 
 
Week11
Week11
 
 
Week12
Week12
 
 
Week13
Week13
 
 
Week14
Week14
 
 
Week2
Week2
 
 
Week3
Week3
 
 
Week4
Week4
 
 
Week5
Week5
 
 
Week6
Week6
 
 
Week7
Week7
 
 
Week8
Week8
 
 
Week9
Week9
 
 
README.md
README.md
 
 

Repository files navigation

Talent Sprint

https://iiith-aiml.talentsprint.com/dashboard

Modules

  1. Python Essentials for AI/ML

    • Intro to Python
    • String, list & for Loop
    • List Comprehensions & files
    • Indentation & Code Blocking

  2. Maths Essentials for AI/ML

    1. Linear Algebra
    2. Calculus
    3. Statistics
      • Measures of Center
      • Measures of Spread
    4. Probability
      • Addition Rule
      • Multiplication Rule
      • Conditional Probability

  3. M0: Getting Ready

    1. Terminology and Definitions
      • What is Machine Learning?
      • Question: How are Artificial Intelligence, Machine Learning and other things related?
      • Question: Where does Data Science fit in all this? Are different skills required for it?
      • Visuals
      • ML Glossary - https://developers.google.com/machine-learning/glossary
    2. Fundamental Abstraction
      • Problem Space
      • ML Frameworks
      • Spam Detection
      • Training
      • Testing
    3. ML Avatars
      • The Machine Learning Framework
      • Spam Detection
      • Medical Diagnosis
      • Stock Trading
      • Sentiment Analysis
      • Disease Confirmation
      • Product Recommendation
      • Loan Approval
      • Face Recognition
      • Voice Detection

  4. M1: Problem Formulation and Solving

    • Formulating Real World Problems for AI/ML
    • Classification and Regression Problems
    • Intuitive and Simple Algorithms
    • Representation of the World and Real Data
    • Visualization, Data Preparation, Unsupervided Learning
    • End-to-End Problem Solving

  5. M2: Closer Look at AI/ML Algorithms

    • Linear Algorithms, Optimization and Training
    • Non-Linear Solutions and MLP
    • Gradient Descent and Backpropagation
    • Decision Trees, Random Forest, and Ensembles
    • Principles and Practice of ML
    • Support Vector Machines and Kernels

  6. M3: Deep Learning and Practical Issues

    • Introduction of Deep Learning and Toolchain
    • Convolutional Neural Networks
    • Auto-Encoders
    • Recurrent Neural Networks
    • Overview of Advance Topics
    • Human in the Loop Solutions, Deployment

Program Coverage

Date FirstHalf SecondHalf
Saturday,02-Mar-19 Python / Math Sessions Python / Math Sessions
Sunday,03-Mar-19 Python / Math Sessions Python / Math Sessions
Saturday,09-Mar-19 Class Test; Pandas and Mathplotlib; Exp_A_Data Munging Cloud API(Exp); ML Avatars
Sunday,10-Mar-19 Class test; Experiment 1,2,3; 3 ML Algorithms Decision Trees and Over fitting; KNN & Linar Classfier (exp);
Saturday,16-Mar-19 Lecture - Linear Regression Mini_Hackathon
Sunday,17-Mar-19 Lecture Day 1.Representing Text and Language, 2.Perceptrons, Neural Networks, Gradient Descent Lecture 3.Learning Representations: Word2Vec, Lecture 4.Dimensionality Reduction
Saturday,23-Mar-19 Individual Lab : Expt 1: Newsgroups: Bow; Expt 2: Newsgroups: nGrams;
Sunday,24-Mar-19 Class Test Demo Lec 1: Word2Vec: Mahabharatha; Ind. Lec 1: Performance Metrics Expt 5: Newsgroups: Word2Vec; Expt 6: Linear Classifier; Expt 7: Applying other metrics for Newsgroups
Saturday,30-Mar-19 Mini-Hackathon Mini-Hackathon
Sunday,31-Mar-19 Lecture: PCA and EigenFaces, Multi-Layer Perceptrons Features for Perception-1, Features for Perception-2
Saturday,06-Apr-19 Festival week Holiday. Happy Ugadi Festival week Holiday. Happy Ugadi
Sunday,07-Apr-19 Festival week Holiday. Happy Ugadi Festival week Holiday. Happy Ugadi
Saturday,13-Apr-19 "Individual Lab : Expt 8: MNIST-MLP Expt 9: Celebrity Faces - PCA Expt 10: CIFAR-100 Expt 11: Eigenfaces for classification Expt 12: Speech-""Yes""/""No"" Classifier"
Sunday,14-Apr-19 Class Test Ind. Lec 2: Visualization; Demo Lec 2: Cloud APIs "Individual Lab : Expt 13: Visualization - TSNE Expt 14: Visualization - ISOMAP Expt 15: Alexa API Experiments"
Saturday,20-Apr-19 Hackathon Hackathon
Sunday,21-Apr-19 09. Convolutional Layer 10. Back Propagation 11. ML Pipeline 12. Overfitting and Generalization
Saturday,27-Apr-19 Expt 16: Leave one out Validation Expt 17: K-fold Validation Expt 18: Polynomial Curve Fitting
Sunday,28-Apr-19 Class Test Ind. Lec 3: Clustering Demo Lec 3: PyTorch Expt 19: K-Means Expt 20: Hierarchical Clustering Expt 21: Fashion MNIST; Expt 22: Polynomial curve fitting Expt 23: Instrumenting CNN
Saturday,04-May-19 Mini-Hackathon Mini-Hackathon
Sunday,05-May-19 Class Test; 13. Random Forests, Ensemble Techniques 14. Support Vector Machines 15. Time Series/RNN 16. Human in the Loop Systems
Saturday,11-May-19 Expt 24: SVM, SVM with kernels Expt 25: Face recognition with SVM Expt 26: Random Forests, Ensemble Methods Expt 27: Weather Prediction Expt 28: Rocchio's algorithm
Sunday,12-May-19 Class Test Ind. Lec 4: Recommendation Systems Demo Lec 4: Timeseries Application Ind. Lec 5: Deployment, Practical Issues; Expt 29: Movie Recommendation system KNN Expt 30: Movie Recommendation system SVD KNN Expt 31: Alexa Chatbot
Saturday,18-May-19 Hackathon Hackathon
Sunday,19-May-19 Lecture Sessions : Class Test; 17. Convolutional Neural Networks; 18. Autoencoders Lexture Sessions : 19. Appreciating CNNs; 20. RNN, LSTM, GRU
Saturday,25-May-19 Expt 32: Transfer learning and Finetuning Expt 33: Visualization of CNNs
Sunday,26-May-19 Class Test Lecture 5: Model Compression; Demo: Deployment, Practical Issues "Expt 37: Uniform and Non Uniform Quantizations; Expt 38: Student and Teacher Networks; Expt 39: Weight Intializations and updates"
Saturday,01-Jun-19 Mini-Hackathon Mini-Hackathon
Sunday,02-Jun-19 Class Test 21. Beyond AlexNet 22. BP Revisited 23.Siamese Networks 24. Advanced Topics: GANs
Saturday,08-Jun-19 Expt 40: Siamese Expt 41: GAN Tutorial from PyTorch Expt 42: Tuning Hyperparameter learning rate Expt 43: Tuning hyperparamter optimizer _ Adam Expt 44: Hackathon debrief
Sunday,09-Jun-19 Hackathon Hackathon

GRADES

https://iiith-talentsprint.trainmoo.in/applite/grades

Scores 602/667 90.25%

Name Score Outof Percentage
M0_W0_CT_2 - 03/03/2019 9 9 100.00%
M0_W0_CT_1 - 03/03/2019 6 6 100.00%
M0_W1_CT_ - 03/09/2019 9 9 100.00%
M0_W1_CT_ - 03/10/2019 6 6 100.00%
M0_W1_IL_1 - 03/16/2019 6 10 60.00%
M0_W0_MH_1 - 03/16/2019 22 25 88.00%
M1_W1_CT_2 - 03/17/2019 9 9 100.00%
M1_W1_CT_1 - 03/17/2019 6 6 100.00%
M1_W1_WT_1 - 03/23/2019 10 10 100.00%
M1_W2_CT_1 - 03/24/2019 6 6 100.00%
M1_W2_CT_2 - 03/24/2019 9 9 100.00%
M1_W2_WT_2 - 03/28/2019 10 10 100.00%
M1_W1_IL_2 - 03/30/2019 6 8 75.00%
M1_W3_MH_2 - 03/30/2019 24 25 96.00%
M1_W3_CT_2 - 03/31/2019 8 9 88.89%
M1_W3_CT_1 - 03/31/2019 6 6 100.00%
M1_W3_WT_3 - 04/04/2019 10 10 100.00%
M1_W2_IL_3 - 04/06/2019 6 6 100.00%
M1_W3_IL_4 - 04/13/2019 10 10 100.00%
M1_W4_CT_1 - 04/14/2019 6 6 100.00%
M1_W4_CT_2 - 04/14/2019 9 9 100.00%
M1_W4_WT_4 - 04/18/2019 10 10 100.00%
M1_W4_IL_5 - 04/21/2019 2 6 33.33%
M1_W4_H_1 - 04/21/2019 49 50 98.00%
M2_W5_CT_2 - 04/21/2019 4 9 44.44%
M2_W5_CT_1 - 04/21/2019 6 6 100.00%
M2_W5_WT_5 - 04/25/2019 10 10 100.00%
M2_W1_IL_6 - 04/28/2019 6 6 100.00%
M2_W6_CT_2 - 04/28/2019 9 9 100.00%
M2_W6_CT_1 - 04/28/2019 6 6 100.00%
M2_W6_WT_6 - 05/02/2019 10 10 100.00%
M2_W6_MH_3 - 05/04/2019 22 25 88.00%
M2_W7_CT_1 - 05/05/2019 6 6 100.00%
M2_W7_CT_2 - 05/05/2019 9 9 100.00%
M2_W2_IL_7 - 05/05/2019 10 10 100.00%
M2_W7_WT_7 - 05/09/2019 10 10 100.00%
M2_W3_IL_8 - 05/11/2019 10 10 100.00%
M2_W8_CT_1 - 05/12/2019 6 6 100.00%
M2_W8_CT_2 - 05/12/2019 7 9 77.78%
M2_W8_WT_8 - 05/16/2019 10 10 100.00%
M2_W4_IL_9 - 05/18/2019 4 4 100.00%
M2_W8_H_2 - 05/18/2019 36 50 72.00%
M3_W9_CT - 05/19/2019 4 6 66.67%
M3_W9_WT_9 - 05/23/2019 10 10 100.00%
M3_W1_IL_10 - 05/25/2019 12 15 80.00%
M3_W10_CT_1 - 05/26/2019 6 6 100.00%
M3_W10_CT_2 - 05/26/2019 9 9 100.00%
M3_W10_WT_10 - 05/30/2019 10 10 100.00%
M3_W10_MH_4 - 06/01/2019 27 25 108.00%
M3_W2_IL_11 - 06/01/2019 6 9 66.67%
M3_W11_CT_1 - 06/02/2019 6 6 100.00%
M3_W11_CT_2 - 06/02/2019 9 9 100.00%
M3_W11_WT_11 - 06/06/2019 2 10 20.00%
M3_W3_IL_12 - 06/08/2019 12 12 100.00%
M3_W12_CT_1 - 06/09/2019 6 6 100.00%
M3_W11_H_3 - 06/09/2019 39 50 78.00%
M3_W12_CT_2 - 06/09/2019 9 9 100.00%

Certificate

IMG_3602 IMG_3603

Group Picture

AIML BLR B8 Class Photo

About

This repository contains all the sessions covered during the IIIT-H AI ML course.

Resources

Readme
Activity

Stars

8 stars

Watchers

2 watching

Forks

4 forks
Report repository

Releases

No releases published

Packages

No packages published

Languages