Modules / Lectures

Module Name | Download |
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noc19_cs61_assignment_Week_1 | noc19_cs61_assignment_Week_1 |

noc19_cs61_assignment_Week_2 | noc19_cs61_assignment_Week_2 |

noc19_cs61_assignment_Week_3 | noc19_cs61_assignment_Week_3 |

noc19_cs61_assignment_Week_4 | noc19_cs61_assignment_Week_4 |

noc19_cs61_assignment_Week_5 | noc19_cs61_assignment_Week_5 |

noc19_cs61_assignment_Week_6 | noc19_cs61_assignment_Week_6 |

noc19_cs61_assignment_Week_7 | noc19_cs61_assignment_Week_7 |

noc19_cs61_assignment_Week_8 | noc19_cs61_assignment_Week_8 |

Sl.No | Chapter Name | MP4 Download |
---|---|---|

1 | Lecture 1 Background: Introduction | Download |

2 | Lecture 2 : Probability: Concentration inequalities | Download |

3 | Lecture 3 : Linear algebra: PCA, SVD | Download |

4 | Lecture 4 : Optimization: Basics, Convex, GD | Download |

5 | Lecture 5 : Machine Learning: Supervised, generalization, feature learning, clustering. | Download |

6 | Lecture 6 : Memory-efficient data structures: Hash functions, universal / perfect hash families | Download |

7 | Lecture 7 : Bloom filters | Download |

8 | Lecture 8 : Sketches for distinct count | Download |

9 | Lecture 9 : Sketches for distinct count (Contd.) | Download |

10 | Lecture 10 : Misra-Gries sketch | Download |

11 | Lecture 11 : Frequent Element: Space Saving and Count Min. | Download |

12 | Lecture 12 : Frequent Element: Count Sketch | Download |

13 | Lecture 13 : Near Neighbors | Download |

14 | Lecture 14 : Locality Sensitive Hashing. | Download |

15 | Lecture 15 : Building LSH Tables. | Download |

16 | Lecture 16 : Approximate near neighbors search: Extensions e.g. multi-probe, b-bit hashing, Data dependent variants | Download |

17 | Lecture 17 : Approximate near neighbors search: Extensions e.g. multi-probe, b-bit hashing, Data dependent variants (Contd.) | Download |

18 | Lecture 18 : Approximate near neighbors search: Extensions e.g. multi-probe, b-bit hashing, Data dependent variants (Contd.) | Download |

19 | Lecture 19 : Randomized Numerical Linear Algebra: Random projection | Download |

20 | Lecture 20 : Randomized Numerical Linear Algebra: Random projection (Contd.) | Download |

21 | Lecture 21: "Randomized Numerical Linear Algebra:a) Matrix multiplication + QB decomposition" | Download |

22 | Lecture 22: "Randomized Numerical Linear Algebra:b) CUR+CX" | Download |

23 | Lecture 23: "Randomized Numerical Linear Algebra:a) L2 regression using RP" | Download |

24 | Lecture 24: "Randomized Numerical Linear Algebra:b) Leverage scores" | Download |

25 | Lecture 25: "Randomized Numerical Linear Algebra:c) Hash Kernels + Kitchen Sink" | Download |

26 | Lecture 26 : Map-reduce and Hadoop | Download |

27 | Lecture 27 : Hadoop System | Download |

28 | Lecture 28 : Hadoop System(Contd.) | Download |

29 | Lecture 29 : Hadoop System(Contd.) | Download |

30 | Lecture 30 : Spark | Download |

31 | Lecture 31 : Spark(Contd.) | Download |

32 | Lecture 32 : Spark(Contd.) | Download |

33 | Lecture 33 : Distributed Machine Learning and Optimization: Introduction | Download |

34 | Lecture 34 : SGD+Proof | Download |

35 | Lecture 35 : SGD+Proof(Contd.) | Download |

36 | Lecture 36 : Distributed Machine Learning and Optimization:ADMM + applications | Download |

37 | Lecture 37 : Distributed Machine Learning and Optimization:ADMM + applications(Contd.) | Download |

38 | Lecture 38 : Clustering | Download |

39 | Lecture 39 : Clustering(Contd.) | Download |

40 | Lecture 40 : Conclusion | Download |

Sl.No | Chapter Name | English |
---|---|---|

1 | Lecture 1 Background: Introduction | Download Verified |

2 | Lecture 2 : Probability: Concentration inequalities | Download Verified |

3 | Lecture 3 : Linear algebra: PCA, SVD | Download Verified |

4 | Lecture 4 : Optimization: Basics, Convex, GD | Download Verified |

5 | Lecture 5 : Machine Learning: Supervised, generalization, feature learning, clustering. | Download Verified |

6 | Lecture 6 : Memory-efficient data structures: Hash functions, universal / perfect hash families | Download Verified |

7 | Lecture 7 : Bloom filters | Download Verified |

8 | Lecture 8 : Sketches for distinct count | Download Verified |

9 | Lecture 9 : Sketches for distinct count (Contd.) | Download Verified |

10 | Lecture 10 : Misra-Gries sketch | Download Verified |

11 | Lecture 11 : Frequent Element: Space Saving and Count Min. | Download Verified |

12 | Lecture 12 : Frequent Element: Count Sketch | Download Verified |

13 | Lecture 13 : Near Neighbors | Download Verified |

14 | Lecture 14 : Locality Sensitive Hashing. | Download Verified |

15 | Lecture 15 : Building LSH Tables. | Download Verified |

16 | Lecture 16 : Approximate near neighbors search: Extensions e.g. multi-probe, b-bit hashing, Data dependent variants | Download Verified |

17 | Lecture 17 : Approximate near neighbors search: Extensions e.g. multi-probe, b-bit hashing, Data dependent variants (Contd.) | Download Verified |

18 | Lecture 18 : Approximate near neighbors search: Extensions e.g. multi-probe, b-bit hashing, Data dependent variants (Contd.) | Download Verified |

19 | Lecture 19 : Randomized Numerical Linear Algebra: Random projection | Download Verified |

20 | Lecture 20 : Randomized Numerical Linear Algebra: Random projection (Contd.) | Download Verified |

21 | Lecture 21: "Randomized Numerical Linear Algebra:a) Matrix multiplication + QB decomposition" | Download Verified |

22 | Lecture 22: "Randomized Numerical Linear Algebra:b) CUR+CX" | Download Verified |

23 | Lecture 23: "Randomized Numerical Linear Algebra:a) L2 regression using RP" | Download Verified |

24 | Lecture 24: "Randomized Numerical Linear Algebra:b) Leverage scores" | Download Verified |

25 | Lecture 25: "Randomized Numerical Linear Algebra:c) Hash Kernels + Kitchen Sink" | Download Verified |

26 | Lecture 26 : Map-reduce and Hadoop | Download Verified |

27 | Lecture 27 : Hadoop System | Download Verified |

28 | Lecture 28 : Hadoop System(Contd.) | Download Verified |

29 | Lecture 29 : Hadoop System(Contd.) | Download Verified |

30 | Lecture 30 : Spark | Download Verified |

31 | Lecture 31 : Spark(Contd.) | Download Verified |

32 | Lecture 32 : Spark(Contd.) | Download Verified |

33 | Lecture 33 : Distributed Machine Learning and Optimization: Introduction | Download Verified |

34 | Lecture 34 : SGD+Proof | Download Verified |

35 | Lecture 35 : SGD+Proof(Contd.) | Download Verified |

36 | Lecture 36 : Distributed Machine Learning and Optimization:ADMM + applications | Download Verified |

37 | Lecture 37 : Distributed Machine Learning and Optimization:ADMM + applications(Contd.) | Download Verified |

38 | Lecture 38 : Clustering | Download Verified |

39 | Lecture 39 : Clustering(Contd.) | Download Verified |

40 | Lecture 40 : Conclusion | Download Verified |

Sl.No | Language | Book link |
---|---|---|

1 | English | Download |

2 | Bengali | Not Available |

3 | Gujarati | Not Available |

4 | Hindi | Not Available |

5 | Kannada | Not Available |

6 | Malayalam | Not Available |

7 | Marathi | Not Available |

8 | Tamil | Not Available |

9 | Telugu | Not Available |