GitHub - arjuntheprogrammer/IIITH_AI_ML: This repository contains all the sessions covered during the IIIT-H AI ML course.

Talent Sprint

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

Modules

Python Essentials for AI/ML
- Intro to Python
- String, list & for Loop
- List Comprehensions & files
- Indentation & Code Blocking
Maths Essentials for AI/ML
1. Linear Algebra
  - Vectors, what even are they? https://www.youtube.com/watch?v=fNk_zzaMoSs
  - Linear combinations, span, and basis vectors https://www.youtube.com/watch?v=k7RM-ot2NWY
  - Linear transformations and matrices https://www.youtube.com/watch?v=kYB8IZa5AuE
  - Dot products and duality https://www.youtube.com/watch?v=LyGKycYT2v0
  - Change of basis https://www.youtube.com/watch?v=P2LTAUO1TdA
  - Eigenvectors and Eigenvalues https://www.youtube.com/watch?v=PFDu9oVAE-g
2. Calculus
  - Essence of calculus https://www.youtube.com/watch?v=WUvTyaaNkzM
  - The paradox of the derivative https://www.youtube.com/watch?v=9vKqVkMQHKk
  - Derivative formulas through geometry https://www.youtube.com/watch?v=S0_qX4VJhMQ
  - Visualizing the chain rule and product rule https://www.youtube.com/watch?v=YG15m2VwSjA
3. Statistics
  - Measures of Center
  - Measures of Spread
4. Probability
  - Addition Rule
  - Multiplication Rule
  - Conditional Probability
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
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
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
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%

Name		Name	Last commit message	Last commit date
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

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